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13 pages, 1173 KiB

Open AccessArticle

Crop Yield Prediction Using Hybrid Machine Learning Approach: A Case Study of Lentil (Lens culinaris Medik.)

by Pankaj Das, Girish Kumar Jha, Achal Lama and Rajender Parsad

Agriculture 2023, 13(3), 596; https://doi.org/10.3390/agriculture13030596 - 28 Feb 2023

Cited by 8 | Viewed by 5285

Abstract

This paper introduces a novel hybrid approach, combining machine learning algorithms with feature selection, for efficient modelling and forecasting of complex phenomenon governed by multifactorial and nonlinear behaviours, such as crop yield. We have attempted to harness the benefits of the soft computing [...] Read more.

This paper introduces a novel hybrid approach, combining machine learning algorithms with feature selection, for efficient modelling and forecasting of complex phenomenon governed by multifactorial and nonlinear behaviours, such as crop yield. We have attempted to harness the benefits of the soft computing algorithm multivariate adaptive regression spline (MARS) for feature selection coupled with support vector regression (SVR) and artificial neural network (ANN) for efficiently mapping the relationship between the predictors and predictand variables using the MARS-ANN and MARS-SVR hybrid frameworks. The performances of the algorithms are com-pared on different fit statistics such as RMSE, MAD, MAPE, etc., using numeric agronomic traits of 518 lentil genotypes to predict grain yield. The proposed MARS-based hybrid models outperformed individual models such as MARS, SVR and ANN. This is largely due to the enhanced feature ex-traction capability of the MARS model coupled with the nonlinear adaptive learning ability of ANN and SVR. The superiority of the proposed hybrid models MARS-ANN and MARS-SVM in terms of model building and generalisation ability was demonstrated. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Agriculture)

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19 pages, 2829 KiB

Open AccessArticle

Crop Yield Prediction Using Machine Learning Models: Case of Irish Potato and Maize

by Martin Kuradusenge, Eric Hitimana, Damien Hanyurwimfura, Placide Rukundo, Kambombo Mtonga, Angelique Mukasine, Claudette Uwitonze, Jackson Ngabonziza and Angelique Uwamahoro

Agriculture 2023, 13(1), 225; https://doi.org/10.3390/agriculture13010225 - 16 Jan 2023

Cited by 67 | Viewed by 17956

Abstract

Although agriculture remains the dominant economic activity in many countries around the world, in recent years this sector has continued to be negatively impacted by climate change leading to food insecurities. This is so because extreme weather conditions induced by climate change are [...] Read more.

Although agriculture remains the dominant economic activity in many countries around the world, in recent years this sector has continued to be negatively impacted by climate change leading to food insecurities. This is so because extreme weather conditions induced by climate change are detrimental to most crops and affect the expected quantity of agricultural production. Although there is no way to fully mitigate these natural phenomena, it could be much better if there is information known earlier about the future so that farmers can plan accordingly. Early information sharing about expected crop production may support food insecurity risk reduction. In this regard, this work employs data mining techniques to predict future crop (i.e., Irish potatoes and Maize) harvests using weather and yields historical data for Musanze, a district in Rwanda. The study applies machine learning techniques to predict crop harvests based on weather data and communicate the information about production trends. Weather data and crop yields for Irish potatoes and maize were gathered from various sources. The collected data were analyzed through Random Forest, Polynomial Regression, and Support Vector Regressor. Rainfall and temperature were used as predictors. The models were trained and tested. The results indicate that Random Forest is the best model with root mean square error of 510.8 and 129.9 for potato and maize, respectively, whereas R² was 0.875 and 0.817 for the same crops datasets. The optimum weather conditions for the optimal crop yield were identified for each crop. The results suggests that Random Forest is recommended model for early crop yield prediction. The findings of this study will go a long way to enhance reliance on data for agriculture and climate change related decisions, especially in low-to-middle income countries such as Rwanda. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Agriculture)

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19 pages, 2683 KiB

Open AccessArticle

An Artificial-Intelligence-Based Novel Rice Grade Model for Severity Estimation of Rice Diseases

by Rutuja Rajendra Patil, Sumit Kumar, Shwetambari Chiwhane, Ruchi Rani and Sanjeev Kumar Pippal

Agriculture 2023, 13(1), 47; https://doi.org/10.3390/agriculture13010047 - 23 Dec 2022

Cited by 20 | Viewed by 3730

Abstract

The pathogens such as fungi and bacteria can lead to rice diseases that can drastically impair crop production. Because the illness is difficult to control on a broad scale, crop field monitoring is one of the most effective methods of control. It allows [...] Read more.

The pathogens such as fungi and bacteria can lead to rice diseases that can drastically impair crop production. Because the illness is difficult to control on a broad scale, crop field monitoring is one of the most effective methods of control. It allows for early detection of the disease and the implementation of preventative measures. Disease severity estimation based on digital picture analysis, where the pictures are obtained from the rice field using mobile devices, is one of the most effective control strategies. This paper offers a method for quantifying the severity of three rice crop diseases (brown spot, blast, and bacterial blight) that can determine the stage of plant disease. A total of 1200 images of rice illnesses and healthy images make up the input dataset. With the help of agricultural experts, the diseased zone was labeled according to the disease type using the Make Sense tool. More than 75% of the images in the dataset correspond to one disease label, healthy plants represent more than 15%, and multiple diseases represent 5% of the images labeled. This paper proposes a novel artificial intelligence rice grade model that uses an optimized faster-region-based convolutional neural network (FRCNN) approach to calculate the area of leaf instances and the infected regions. EfficientNet-B0 architecture was used as a backbone as the network shows the best accuracy (96.43%). The performance was compared with the CNN architectures: VGG16, ResNet101, and MobileNet. The model evaluation parameters used to measure the accuracy are positive predictive value, sensitivity, and intersection over union. This severity estimation method can be further deployed as a tool that allows farmers to obtain perfect predictions of the disease severity level based on lesions in the field conditions and produce crops more organically. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Agriculture)

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23 pages, 10660 KiB

Open AccessArticle

Rice Growth Stage Classification via RF-Based Machine Learning and Image Processing

by Rodney Tai-Chu Sheng, Yu-Hsiang Huang, Pin-Cheng Chan, Showkat Ahmad Bhat, Yi-Chien Wu and Nen-Fu Huang

Agriculture 2022, 12(12), 2137; https://doi.org/10.3390/agriculture12122137 - 12 Dec 2022

Cited by 13 | Viewed by 29540

Abstract

Rice is one of the most significant crops cultivated in Asian countries. In Taiwan, almost half of the arable land is used for growing rice. The life cycle of paddy rice can be divided into several stages: vegetative stage, reproductive stage, and ripening [...] Read more.

Rice is one of the most significant crops cultivated in Asian countries. In Taiwan, almost half of the arable land is used for growing rice. The life cycle of paddy rice can be divided into several stages: vegetative stage, reproductive stage, and ripening stage. These three main stages can be divided into more detailed stages. However, the transitions between stages are challenging to observe and determine, so experience is required. Thus, rice cultivation is challenging for inexperienced growers, even with the standard of procedure (SOP) provided. Additionally, aging and labor issues have had an impact on agriculture. Furthermore, smart farming has been growing rapidly in recent years and has improved agriculture in many ways. To lower the entry requirements and help novices better understand, we proposed a random forest (RF)-based machine learning (ML) classification model for rice growth stages. The experimental setup installed in the experiment fields consists of an HD smart camera (Speed-dome) to collect the image and video data, along with other internet of things (IoT) devices such as 7-in-1 soil sensors, a weather monitoring station, flow meter, and milometer connected with LoRa base station for numerical data. Then, different image processing techniques such as object detection, object classification, instance segmentation, excess green index (EGI), and modified excess green index (EGI) were used to calculate the paddy height and canopy cover (CC) or green coverage (GC). The proposed ML model uses these values as input. Furthermore, growth-related factors such as height, CC, accumulative temperature, and DAT are used to develop our model. An agronomist has been consulted to label the collected different stages of data. The developed optimal model has achieved an accuracy of 0.98772, and a macro F1-score of 0.98653. Thus, the developed model produces high-performance accuracy and can be employed in real-world scenarios. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Agriculture)

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11 pages, 6519 KiB

Open AccessCommunication

Explainable Neural Network for Classification of Cotton Leaf Diseases

by Javeria Amin, Muhammad Almas Anjum, Muhammad Sharif, Seifedine Kadry and Jungeun Kim

Agriculture 2022, 12(12), 2029; https://doi.org/10.3390/agriculture12122029 - 28 Nov 2022

Cited by 10 | Viewed by 3304

Abstract

Every nation’s development depends on agriculture. The term “cash crops” refers to cotton and other important crops. Most pathogens that significantly harm crops also impact cotton. Numerous diseases that influence yield via the leaf, such as powdery mildew, leaf curl, leaf spot, target [...] Read more.

Every nation’s development depends on agriculture. The term “cash crops” refers to cotton and other important crops. Most pathogens that significantly harm crops also impact cotton. Numerous diseases that influence yield via the leaf, such as powdery mildew, leaf curl, leaf spot, target spot, bacterial blight, and nutrient deficiencies, can affect cotton. Early disease detection protects crops from additional harm. Computerized methods perform a vital role in cotton leaf disease detection at an early stage. The method consists of two core steps such as feature extraction and classification. First, in the proposed method, data augmentation is applied to balance the input data. After that, features are extracted from a pre-trained VGG-16 model and passed to 11 fully convolutional layers, which freeze the majority and randomly initialize convolutional features to subsequently generate a score of the anomaly map, which defines the probability of the lesion region. The proposed model is trained on the selected hyperparameters that produce great classification results. The proposed model performance is evaluated on two publicly available Kaggle datasets, Cotton Leaf and Disease. The proposed method provides 99.99% accuracy, which is competent compared to existing methods. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Agriculture)

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19 pages, 11833 KiB

Open AccessArticle

Insect Detection in Sticky Trap Images of Tomato Crops Using Machine Learning

by Tiago Domingues, Tomás Brandão, Ricardo Ribeiro and João C. Ferreira

Agriculture 2022, 12(11), 1967; https://doi.org/10.3390/agriculture12111967 - 21 Nov 2022

Cited by 9 | Viewed by 3676

Abstract

As climate change, biodiversity loss, and biological invaders are all on the rise, the significance of conservation and pest management initiatives cannot be stressed. Insect traps are frequently used in projects to discover and monitor insect populations, assign management and conservation strategies, and [...] Read more.

As climate change, biodiversity loss, and biological invaders are all on the rise, the significance of conservation and pest management initiatives cannot be stressed. Insect traps are frequently used in projects to discover and monitor insect populations, assign management and conservation strategies, and assess the effectiveness of treatment. This paper assesses the application of YOLOv5 for detecting insects in yellow sticky traps using images collected from insect traps in Portuguese tomato plantations, acquired under open field conditions. Furthermore, a sliding window approach was used to minimize insect detection duplicates in a non-complex way. This article also contributes to event forecasting in agriculture fields, such as diseases and pests outbreak, by obtaining insect-related metrics that can be further analyzed and combined with other data extracted from the crop fields, contributing to smart farming and precision agriculture. The proposed method achieved good results when compared to related works, reaching 94.4% for mAP_0.5, with a precision and recall of 88% and 91%, respectively, using YOLOv5x. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Agriculture)

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16 pages, 6638 KiB

Open AccessFeature PaperArticle

Deep Learning Ensemble-Based Automated and High-Performing Recognition of Coffee Leaf Disease

by Damar Novtahaning, Hasnain Ali Shah and Jae-Mo Kang

Agriculture 2022, 12(11), 1909; https://doi.org/10.3390/agriculture12111909 - 13 Nov 2022

Cited by 23 | Viewed by 4247

Abstract

Coffee is the world’s most traded tropical crop, accounting for most export profits, and is a significant source of income for the countries in which it is produced. To meet the needs of the coffee market worldwide, farmers need to increase and monitor [...] Read more.

Coffee is the world’s most traded tropical crop, accounting for most export profits, and is a significant source of income for the countries in which it is produced. To meet the needs of the coffee market worldwide, farmers need to increase and monitor coffee production and quality. Coffee leaf disease is a significant factor that decreases coffee quality and production. In this research study, we aim to accurately classify and detect the diseases in four major types of coffee leaf disease (phoma, miner, rust, and Cercospora) in images using deep learning (DL)-based architectures, which are the most powerful artificial intelligence (AI) techniques. Specifically, we present an ensemble approach for DL models using our proposed layer. In our proposed approach, we employ transfer learning and numerous pre-trained CNN networks to extract deep characteristics from images of the coffee plant leaf. Several DL architectures then accumulate the extracted deep features. The best three models that perform well in classification are chosen and concatenated to build an ensemble architecture that is then given into classifiers to determine the outcome. Additionally, a data pre-processing and augmentation method is applied to enhance the quality and increase the data sample’s quantity to improve the training of the proposed method. According to the evaluation in this study, among all DL models, the proposed ensemble architecture outperformed other state-of-the-art neural networks by achieving 97.31% validation. An ablation study is also conducted to perform a comparative analysis of DL models in different scenarios. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Agriculture)

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13 pages, 4441 KiB

Open AccessArticle

Early Estimation of Tomato Yield by Decision Tree Ensembles

by Mario Lillo-Saavedra, Alberto Espinoza-Salgado, Angel García-Pedrero, Camilo Souto, Eduardo Holzapfel, Consuelo Gonzalo-Martín, Marcelo Somos-Valenzuela and Diego Rivera

Agriculture 2022, 12(10), 1655; https://doi.org/10.3390/agriculture12101655 - 10 Oct 2022

Cited by 5 | Viewed by 3325

Abstract

Crop yield forecasting allows farmers to make decisions in advance to improve farm management and logistics during and after harvest. In this sense, crop yield potential maps are an asset for farmers making decisions about farm management and planning. Although scientific efforts have [...] Read more.

Crop yield forecasting allows farmers to make decisions in advance to improve farm management and logistics during and after harvest. In this sense, crop yield potential maps are an asset for farmers making decisions about farm management and planning. Although scientific efforts have been made to determine crop yields from in situ information and through remote sensing, most studies are limited to evaluating data from a single date just before harvest. This has a direct negative impact on the quality and predictability of these estimates, especially for logistics. This study proposes a methodology for the early prediction of tomato yield using decision tree ensembles, vegetation spectral indices, and shape factors from images captured by multispectral sensors on board an unmanned aerial vehicle (UAV) during different phenological stages of crop development. With the predictive model developed and based on the collection of training characteristics for 6 weeks before harvest, the tomato yield was estimated for a 0.4 ha plot, obtaining an error rate of

9.28

%. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Agriculture)

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12 pages, 4591 KiB

Open AccessArticle

A Lightweight Real-Time Rice Blast Disease Segmentation Method Based on DFFANet

by Changguang Feng, Minlan Jiang, Qi Huang, Lingguo Zeng, Changjiang Zhang and Yulong Fan

Agriculture 2022, 12(10), 1543; https://doi.org/10.3390/agriculture12101543 - 24 Sep 2022

Cited by 8 | Viewed by 2179

Abstract

The evaluation of rice disease severity is a quantitative indicator for precise disease control, which is of great significance for ensuring rice yield. In the past, it was usually done manually, and the judgment of rice blast severity can be subjective and time-consuming. [...] Read more.

The evaluation of rice disease severity is a quantitative indicator for precise disease control, which is of great significance for ensuring rice yield. In the past, it was usually done manually, and the judgment of rice blast severity can be subjective and time-consuming. To address the above problems, this paper proposes a real-time rice blast disease segmentation method based on a feature fusion and attention mechanism: Deep Feature Fusion and Attention Network (abbreviated to DFFANet). To realize the extraction of the shallow and deep features of rice blast disease as complete as possible, a feature extraction (DCABlock) module and a feature fusion (FFM) module are designed; then, a lightweight attention module is further designed to guide the features learning, effectively fusing the extracted features at different scales, and use the above modules to build a DFFANet lightweight network model. This model is applied to rice blast spot segmentation and compared with other existing methods in this field. The experimental results show that the method proposed in this study has better anti-interference ability, achieving 96.15% MioU, a speed of 188 FPS, and the number of parameters is only 1.4 M, which can achieve a high detection speed with a small number of model parameters, and achieves an effective balance between segmentation accuracy and speed, thereby reducing the requirements for hardware equipment and realizing low-cost embedded development. It provides technical support for real-time rapid detection of rice diseases. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Agriculture)

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16 pages, 2692 KiB

Open AccessArticle

TPE-RBF-SVM Model for Soybean Categories Recognition in Selected Hyperspectral Bands Based on Extreme Gradient Boosting Feature Importance Values

by Qinghe Zhao, Zifang Zhang, Yuchen Huang and Junlong Fang

Agriculture 2022, 12(9), 1452; https://doi.org/10.3390/agriculture12091452 - 13 Sep 2022

Cited by 9 | Viewed by 2204

Abstract

Soybeans with insignificant differences in appearance have large differences in their internal physical and chemical components; therefore, follow-up storage, transportation and processing require targeted differential treatment. A fast and effective machine learning method based on hyperspectral data of soybeans for pattern recognition of [...] Read more.

Soybeans with insignificant differences in appearance have large differences in their internal physical and chemical components; therefore, follow-up storage, transportation and processing require targeted differential treatment. A fast and effective machine learning method based on hyperspectral data of soybeans for pattern recognition of categories is designed as a non-destructive testing method in this paper. A hyperspectral-image dataset with 2299 soybean seeds in four categories is collected. Ten features are selected using an extreme gradient boosting algorithm from 203 hyperspectral bands in a range of 400 to 1000 nm; a Gaussian radial basis kernel function support vector machine with optimization by the tree-structured Parzen estimator algorithm is built as the TPE-RBF-SVM model for pattern recognition of soybean categories. The metrics of TPE-RBF-SVM are significantly improved compared with other machine learning algorithms. The accuracy is 0.9165 in the independent test dataset, which is 9.786% higher for the vanilla RBF-SVM model and 10.02% higher than the extreme gradient boosting model. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Agriculture)

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12 pages, 3618 KiB

Open AccessEditor’s ChoiceArticle

Grape Cluster Real-Time Detection in Complex Natural Scenes Based on YOLOv5s Deep Learning Network

by Chuandong Zhang, Huali Ding, Qinfeng Shi and Yunfei Wang

Agriculture 2022, 12(8), 1242; https://doi.org/10.3390/agriculture12081242 - 17 Aug 2022

Cited by 26 | Viewed by 3966

Abstract

Due to differences in planting environment, color, shape, size, and compactness, accurate detection of grape clusters is very difficult. Herein, a real-time detection method for grape clusters based on the YOLOv5s deep learning algorithm was proposed. More specifically, a novel dataset called Grape-internet [...] Read more.

Due to differences in planting environment, color, shape, size, and compactness, accurate detection of grape clusters is very difficult. Herein, a real-time detection method for grape clusters based on the YOLOv5s deep learning algorithm was proposed. More specifically, a novel dataset called Grape-internet was constructed, which consisted of 8657 grape images and corresponding annotation files in complex scenes. By training and adjusting the parameters of the YOLOv5s model on the data set, and by reducing the depth and width of the network, the lightweight processing of the network was completed, losing only a small amount of accuracy. As a result, the fast and accurate detection of grape clusters was finally realized. The test results showed that the precision, recall, mAP and F1 of the grape cluster detection network were 99.40%, 99.40%, 99.40% and 99.40%, respectively, and the average detection speed per image was 344.83 fps, with a model size of 13.67 MB. Compared with the YOLOv5x, ScaledYOLOv4-CSP and YOLOv3 models, the precision of YOLOv5s was 1.84% higher than that of ScaledYOLOv4-CSP, and the recall rate and mAP were slightly lower than three networks by 0.1–0.3%. The speed was the fastest (4.6 times, 2.83 times and 6.7 times of YOLOv3, ScaledYOLOv4-CSP and YOLOv5x network, respectively) and the network scale was the smallest (1.61%, 6.81% and 8.28% of YOLOv3, ScaledYOLOv4-CSP YOLOv5x, respectively) for YOLOv5s. Moreover, the detection precision and recall rate of YOLOv5s was 26.14% and 30.96% higher, respectively, than those of Mask R-CNN. Further, it exhibited more lightweight and better real-time performance. In short, the detection network can not only meet the requirements of being a high precision, high speed and lightweight solution for grape cluster detection, but also it can adapt to differences between products and complex environmental interference, possessing strong robustness, generalization, and real-time adaptability. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Agriculture)

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13 pages, 2032 KiB

Open AccessArticle

Semantic Segmentation Algorithm of Rice Small Target Based on Deep Learning

by Shuofeng Li, Bing Li, Jin Li, Bin Liu and Xin Li

Agriculture 2022, 12(8), 1232; https://doi.org/10.3390/agriculture12081232 - 16 Aug 2022

Cited by 4 | Viewed by 2293

Abstract

At present, rice is generally in a state of dense adhesion and small granular volume during processing, resulting in no effective semantic segmentation method for rice to extract complete rice. Aiming at the above problems, this paper designs a small object semantic segmentation [...] Read more.

At present, rice is generally in a state of dense adhesion and small granular volume during processing, resulting in no effective semantic segmentation method for rice to extract complete rice. Aiming at the above problems, this paper designs a small object semantic segmentation network model based on multi-view feature fusion. The overall structure of the network is divided into a multi-view feature extraction module, a super-resolution feature building module and a semantic segmentation module. The extraction ability of small target features is improved by super-resolution construction of small target detail features, and the learning ability of the network for small target features is enhanced and expanded through multi-view. At the same time, a dataset of quality inspection during rice processing was constructed. We train and test the model on this dataset. The results show that the average segmentation accuracy of the semantic segmentation model in this paper reaches 87.89%. Compared with the semantic segmentation models such as SegNet, CBAM, RefineNet, DeepLabv3+ and G-FRNet, it has obvious advantages in various indicators, which can provide rice quality detection and an efficient method of rice grain extraction. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Agriculture)

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16 pages, 5355 KiB

Open AccessArticle

A Dead Broiler Inspection System for Large-Scale Breeding Farms Based on Deep Learning

by Hongyun Hao, Peng Fang, Enze Duan, Zhichen Yang, Liangju Wang and Hongying Wang

Agriculture 2022, 12(8), 1176; https://doi.org/10.3390/agriculture12081176 - 7 Aug 2022

Cited by 14 | Viewed by 3907

Abstract

Stacked cage is the main breeding method of the large-scale farm in China. In broiler farms, dead broiler inspection is a routine task in the breeding process. It refers to the manual inspection of all cages and removal of dead broilers in the [...] Read more.

Stacked cage is the main breeding method of the large-scale farm in China. In broiler farms, dead broiler inspection is a routine task in the breeding process. It refers to the manual inspection of all cages and removal of dead broilers in the broiler house by the breeders every day. However, as the total amount of broilers is huge, the inspection work is not only time-consuming but also laborious. Therefore, a dead broiler inspection system is constructed in this study to replace the manual inspection work. It mainly consists of an autonomous inspection platform and a dead broiler detection model. The automatic inspection platform performs inspections at the speed of 0.2 m/s in the broiler house aisle, and simultaneously collects images of the four-layer broilers. The images are sent to a server and processed by a dead broiler detection model, which was developed based on the YOLOv3 network. A mosaic augment, the Swish function, an spatial pyramid pooling (SPP) module, and complete intersection over union (CIoU) loss are used to improve the YOLOv3 performance. It achieves a 98.6% mean average precision (intersection of union (IoU) = 0.5) and can process images at 0.007 s per frame. The dead broiler detection model is robust to broilers of different ages and can adapt to different lighting conditions. It is deployed on the server with a human–machine interface. By observing the processing results using the human–machine interface, the breeders could directly find the cage position of dead broilers and remove them, which could reduce the workload of breeders and promote the intelligent development of poultry breeding. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Agriculture)

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16 pages, 3766 KiB

Open AccessArticle

Deep Network with Score Level Fusion and Inference-Based Transfer Learning to Recognize Leaf Blight and Fruit Rot Diseases of Eggplant

by Md. Reduanul Haque and Ferdous Sohel

Agriculture 2022, 12(8), 1160; https://doi.org/10.3390/agriculture12081160 - 4 Aug 2022

Cited by 9 | Viewed by 4908

Abstract

Eggplant is a popular vegetable crop. Eggplant yields can be affected by various diseases. Automatic detection and recognition of diseases is an important step toward improving crop yields. In this paper, we used a two-stream deep fusion architecture, employing CNN-SVM and CNN-Softmax pipelines, [...] Read more.

Eggplant is a popular vegetable crop. Eggplant yields can be affected by various diseases. Automatic detection and recognition of diseases is an important step toward improving crop yields. In this paper, we used a two-stream deep fusion architecture, employing CNN-SVM and CNN-Softmax pipelines, along with an inference model to infer the disease classes. A dataset of 2284 images was sourced from primary (using a consumer RGB camera) and secondary sources (the internet). The dataset contained images of nine eggplant diseases. Experimental results show that the proposed method achieved better accuracy and lower false-positive results compared to other deep learning methods (such as VGG16, Inception V3, VGG 19, MobileNet, NasNetMobile, and ResNet50). Full article

(This article belongs to the Special Issue The Application of Machine Learning in Agriculture)

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17 pages, 4079 KiB

Open AccessEditor’s ChoiceArticle

Recommending Advanced Deep Learning Models for Efficient Insect Pest Detection

by Wei Li, Tengfei Zhu, Xiaoyu Li, Jianzhang Dong and Jun Liu

Agriculture 2022, 12(7), 1065; https://doi.org/10.3390/agriculture12071065 - 21 Jul 2022

Cited by 37 | Viewed by 6135

Abstract

Insect pest management is one of the main ways to improve the crop yield and quality in agriculture and it can accurately and timely detect insect pests, which is of great significance to agricultural production. In the past, most insect pest detection tasks [...] Read more.

Insect pest management is one of the main ways to improve the crop yield and quality in agriculture and it can accurately and timely detect insect pests, which is of great significance to agricultural production. In the past, most insect pest detection tasks relied on the experience of agricutural experts, which is time-consuming, laborious and subjective. In rencent years, various intelligent methods have emerged for detection. This paper employs three frontier Deep Convolutional Neural Network (DCNN) models—Faster-RCNN, Mask-RCNN and Yolov5, for efficient insect pest detection. In addition, we made two coco datasets by ourselves on the basis of Baidu AI insect detection dataset and IP102 dataset, and compared these three frontier deep learning models on the two coco datasets. In terms of Baidu AI insect detection dataset whose background is simple, the experimental results strongly recommend Yolov5 for the insect pest detection, because its accuracy reaches above 99% while Faster-RCNN’s and Mask-RCNN’s reach above 98%. Meanwhile, Yolov5 has the faster computational speed than Faster-RCNN and Mask-RCNN. Comparatively speaking, with regard to the IP102 dataset whose background is complex and categories are abundant, Faster-RCNN and Mask-RCNN have the higher accuracy, reaching 99%, than Yolov5 whose accuracy is about 97%. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Agriculture)

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18 pages, 2475 KiB

Open AccessArticle

Identifying the Determinants of Regional Raw Milk Prices in Russia Using Machine Learning

by Svetlana Kresova and Sebastian Hess

Agriculture 2022, 12(7), 1006; https://doi.org/10.3390/agriculture12071006 - 11 Jul 2022

Cited by 3 | Viewed by 2756

Abstract

In this study, official data from Russia’s regions for the period from 2015 to 2019 were analysed on the basis of 12 predictor variables in order to explain the regional raw milk price. Model training and hyperparameter optimisation were performed with a spatiotemporal [...] Read more.

In this study, official data from Russia’s regions for the period from 2015 to 2019 were analysed on the basis of 12 predictor variables in order to explain the regional raw milk price. Model training and hyperparameter optimisation were performed with a spatiotemporal cross-validation technique using the machine learning (ML) algorithm. The findings of the study showed that the RF algorithm had a good predictive performance Variable importance revealed that drinking milk production, income, livestock numbers and population density are the four most important determinants to explain the variation in regional raw milk prices in Russia. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Agriculture)

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10 pages, 883 KiB

Open AccessArticle

Detection of Unripe Kernels and Foreign Materials in Chickpea Mixtures Using Image Processing

by Somayeh Salam, Kamran Kheiralipour and Fuji Jian

Agriculture 2022, 12(7), 995; https://doi.org/10.3390/agriculture12070995 - 10 Jul 2022

Cited by 14 | Viewed by 2213

Abstract

The existence of dockage, unripe kernels, and foreign materials in chickpea mixtures is one of the main concerns during chickpea storage and marketing. Novel algorithms based on image processing were developed to detect undesirable, foreign materials, and matured chickpea kernels in the chickpea [...] Read more.

The existence of dockage, unripe kernels, and foreign materials in chickpea mixtures is one of the main concerns during chickpea storage and marketing. Novel algorithms based on image processing were developed to detect undesirable, foreign materials, and matured chickpea kernels in the chickpea mixture. Images of 270 objects including 54 sound samples and 36 samples of each undesired object were prepared and features of these acquired images were extracted. Different models based on linear discriminant analysis (LDA), support vector machine (SVM), and artificial neural networks (ANN) methods were developed by using MATLAB. Three classification algorithms based on LDA, SVM, and ANN methods were developed. The classification accuracy in training, testing, and overall detection showed the superiority of ANN (99.4, 92.6, and 94.4%, respectively) and LDA (91.1, 94.0, and 91.9%, respectively) over the SVM (100, 53.7, and 88.5%, respectively). The developed image processing technique can be incorporated with a vision-based real-time system. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Agriculture)

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18 pages, 4872 KiB

Open AccessArticle

Cannabis sativa L. Spectral Discrimination and Classification Using Satellite Imagery and Machine Learning

by Fatih Bicakli, Gordana Kaplan and Abduldaem S. Alqasemi

Agriculture 2022, 12(6), 842; https://doi.org/10.3390/agriculture12060842 - 10 Jun 2022

Cited by 4 | Viewed by 5827

Abstract

Crops such as cannabis, poppy, and coca tree are used to make illicit and addictive drugs. Detection and mapping of such crops can be significant for the controlled growth of the plants, thus supporting the prevention of illegal production. Remote sensing has the [...] Read more.

Crops such as cannabis, poppy, and coca tree are used to make illicit and addictive drugs. Detection and mapping of such crops can be significant for the controlled growth of the plants, thus supporting the prevention of illegal production. Remote sensing has the ability to monitor areas for cannabis growing. However, in the scientific literature, there is relatively little information on the spectral features of cannabis. Here in this study, we aim to: (1) offer a literature review on the studies investigating Cannabis sativa L. using remote sensing data; (2) define the spectral features of cannabis fields and other plants found in areas where cannabis is produced in northern Turkey; (3) apply machine learning algorithms for distinguishing cannabis from non-cannabis fields. For the purposes of this study, high-resolution imagery from PlanetScope satellites was used. The investigation showed that the most significant difference between cannabis and the other investigated plants was noticed in May–June. The classification results showed that, with Random Forest (RF) cannabis, fields can be accurately classified with accuracy higher than 93%. Following these results, the investigations with machine learning techniques showed promising results for classifying cannabis fields. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Agriculture)

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17 pages, 3696 KiB

Open AccessArticle

Detection of Rice Spikelet Flowering for Hybrid Rice Seed Production Using Hyperspectral Technique and Machine Learning

by Yali Zhang, Luchao Bai, Yuan Qi, Huasheng Huang, Xiaoyang Lu, Junqi Xiao, Yubin Lan, Muhua Lin and Jizhong Deng

Agriculture 2022, 12(6), 755; https://doi.org/10.3390/agriculture12060755 - 25 May 2022

Cited by 3 | Viewed by 2570

Abstract

Effective detection of rice spikelet flowering is crucial to the determination of optimal pollination timing for hybrid rice seed production. Currently, the detection of rice spikelet flowering status relies on manual observation of farmers, which has low efficiency and large errors. This study [...] Read more.

Effective detection of rice spikelet flowering is crucial to the determination of optimal pollination timing for hybrid rice seed production. Currently, the detection of rice spikelet flowering status relies on manual observation of farmers, which has low efficiency and large errors. This study attempts to acquire rice spikelet flowering information using a hyperspectral technique and machine learning in order to meet the needs of hybrid rice seed pollination rapidly and automatically. Hyperspectral data of rice male parents with flowering and non-flowering in two experimental sites were collected with an ASD FieldSpec^® HandHeld™2 spectrometer. Three traditional classifiers, Random Forest (RF), Support Vector Machine (SVM) and Back Propagation (BP) neural network, and Convolutional Neural Network (CNN), were used to build classification models for rice spikelets flowering detection. Three data processing methods, PCA feature extraction, GA feature selection, and the PCA and GA combination algorithm, were used for data dimensionality reduction. By comparing the precision and recall rate of different algorithms and data processing methods, the algorithms applicable to identify rice spikelet flowering were investigated. Results show that by evaluating different feature reduction methods and classifiers, the optimal model for rice spikelets flowering detection is the BP model with PCA feature extraction. The accuracy of the model reaches up to 96–100%. Hyperspectral technology and machine learning algorithm are capable of effective detection of rice spikelet flowering. This study provides technical reference for accurate judgment of rice flowering and helps to determine the optimal operation time for supplementary pollination of hybrid rice. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Agriculture)

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17 pages, 10875 KiB

Open AccessArticle

Deep Learning Based Disease, Pest Pattern and Nutritional Deficiency Detection System for “Zingiberaceae” Crop

by Hamna Waheed, Noureen Zafar, Waseem Akram, Awais Manzoor, Abdullah Gani and Saif ul Islam

Agriculture 2022, 12(6), 742; https://doi.org/10.3390/agriculture12060742 - 24 May 2022

Cited by 22 | Viewed by 4894

Abstract

Plants’ diseases cannot be avoided because of unpredictable climate patterns and environmental changes. The plants like ginger get affected by various pests, conditions, and nutritional deficiencies. Therefore, it is essential to identify such causes early and perform the cure to get the desired [...] Read more.

Plants’ diseases cannot be avoided because of unpredictable climate patterns and environmental changes. The plants like ginger get affected by various pests, conditions, and nutritional deficiencies. Therefore, it is essential to identify such causes early and perform the cure to get the desired production rate. Deep learning-based methods are helpful for the identification and classification of problems in this domain. This paper presents deep artificial neural network and deep learning-based methods for the early detection of diseases, pest patterns, and nutritional deficiencies. We have used a real-field dataset consisting of healthy and affected ginger plant leaves. The results show that the convolutional neural network (CNN) has achieved the highest accuracy of

99 %

for disease rhizomes detection. For pest pattern leaves, VGG-16 models showed the highest accuracy of

96 %

. For nutritional deficiency-affected leaves, ANN has achieved the highest accuracy (

96 %

). The experimental results achieved are comparable with other existing techniques in the literature. In addition, the results demonstrated the potential in improving the yield of ginger using the proposed disease detection methods and an essential consideration for the design of real-time disease detection applications. However, the results are specific to the dataset used in this work and may yield different results for the other datasets. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Agriculture)

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15 pages, 2710 KiB

Open AccessArticle

Weedy Rice Classification Using Image Processing and a Machine Learning Approach

by Rashidah Ruslan, Siti Khairunniza-Bejo, Mahirah Jahari and Mohd Firdaus Ibrahim

Agriculture 2022, 12(5), 645; https://doi.org/10.3390/agriculture12050645 - 29 Apr 2022

Cited by 13 | Viewed by 3985

Abstract

Weedy rice infestation has become a major problem in all rice-growing countries, especially in Malaysia. Challenges remain in finding a rapid technique to identify the weedy rice seeds that tend to pose similar taxonomic and physiological features as the cultivated rice seeds. This [...] Read more.

Weedy rice infestation has become a major problem in all rice-growing countries, especially in Malaysia. Challenges remain in finding a rapid technique to identify the weedy rice seeds that tend to pose similar taxonomic and physiological features as the cultivated rice seeds. This study presents image processing and machine learning techniques to classify weedy rice seed variants and cultivated rice seeds. A machine vision unit was set up for image acquisition using an area scan camera for the Red, Green and Blue (RGB) and monochrome images of five cultivated rice varieties and a weedy rice seed variant. Sixty-seven features from the RGB and monochrome images of the seed kernels were extracted from three primary parameters, namely morphology, colour and texture, and were used as the input for machine learning. Seven machine learning classifiers were used, and the classification performance was evaluated. Analyses of the best model were based on the overall performance measures, such as the sensitivity, specificity, accuracy and the average correct classification of the classifiers that best described the unbalanced dataset. Results showed that the best optimum model was developed by the RGB image using the logistic regression (LR) model that achieved 85.3% sensitivity, 99.5% specificity, 97.9% accuracy and 92.4% average correct classification utilising all the 67 features. In conclusion, this study has proved that the features extracted from the RGB images have higher sensitivity and accuracy in identifying the weedy rice seeds than the monochrome images by using image processing and a machine learning technique with the selected colour, morphological and textural features. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Agriculture)

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25 pages, 9463 KiB

Open AccessArticle

An Automated Crop Growth Detection Method Using Satellite Imagery Data

by Dong-Chong Hsiou, Fay Huang, Fu Jie Tey, Tin-Yu Wu and Yi-Chuan Lee

Agriculture 2022, 12(4), 504; https://doi.org/10.3390/agriculture12040504 - 2 Apr 2022

Cited by 1 | Viewed by 4006

Abstract

This study develops an automated crop growth detection APP, with the functionality to access the cadastral data for the target field, that was to be used for a satellite-imagery-based field survey. A total of 735 ground-truth records of the cabbage cultivation areas in [...] Read more.

This study develops an automated crop growth detection APP, with the functionality to access the cadastral data for the target field, that was to be used for a satellite-imagery-based field survey. A total of 735 ground-truth records of the cabbage cultivation areas in Yunlin were collected via the implemented APP in order to train a deep learning model to make accurate predictions of the growth stages of the cabbage from 0 to 70 days. A regression analysis was performed by the gradient boosting decision tree (GBDT) technique. The model was trained on multitemporal multispectral satellite images, which were retrieved from the ground-truth data. The experimental results show that the mean average error of the predictions is 8.17 days, and that 75% of the predictions have errors less than 11 days. Moreover, the GBDT algorithm was also adopted for the classification analysis. After planting, the cabbage growth stages can be divided into the cupping, early heading, and mature stages. For each stage, the prediction capture rate is 0.73, 0.51, and 0.74, respectively. If the days of growth of the cabbages are partitioned into two groups, the prediction capture rate for 0–40 days is 0.83, and that for 40–70 days is 0.76. Therefore, by applying appropriate data mining techniques, together with multitemporal multispectral satellite images, the proposed method can predict the growth stages of the cabbage automatically, which can assist the governmental agriculture department to make cabbage yield predictions when creating precautionary measures to deal with the imbalance between production and sales when needed. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Agriculture)

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21 pages, 3522 KiB

Open AccessArticle

Assessing the Efficiency of Remote Sensing and Machine Learning Algorithms to Quantify Wheat Characteristics in the Nile Delta Region of Egypt

by Adel H. Elmetwalli, Yasser S. A. Mazrou, Andrew N. Tyler, Peter D. Hunter, Osama Elsherbiny, Zaher Mundher Yaseen and Salah Elsayed

Agriculture 2022, 12(3), 332; https://doi.org/10.3390/agriculture12030332 - 25 Feb 2022

Cited by 14 | Viewed by 3230

Abstract

Monitoring strategic agricultural crops in terms of crop growth performance, by accurate cost-effective and quick tools is crucially important in site-specific management to avoid crop reductions. The availability of commercial high resolution satellite images with high resolution (spatial and spectral) as well as [...] Read more.

Monitoring strategic agricultural crops in terms of crop growth performance, by accurate cost-effective and quick tools is crucially important in site-specific management to avoid crop reductions. The availability of commercial high resolution satellite images with high resolution (spatial and spectral) as well as in situ spectra measurements can help decision takers to have deep insight on crop stress in a certain region. The research attempts to examine remote sensing dataset for forecasting wheat crop (Sakha 61) characteristics including the leaf area index (LAI), plant height (plant-h), above ground biomass (AGB) and Soil Plant Analysis Development (SPAD) value of wheat across non-stress, drought and salinity-induced stress in the Nile Delta region. In this context, the ability of in situ spectroradiometry measurements and QuickBird high resolution images was evaluated in our research. The efficiency of Random Forest (RF) and Artificial Neural Network (ANN), mathematical models was assessed to estimate the four measured wheat characteristics based on vegetation spectral reflectance indices (V-SRIs) extracted from both approaches and their interactions. Field surveys were carried out to collect in situ spectroradiometry measurements concomitant with the acquisition of QuickBird imagery. The results demonstrated that several V-SRIs extracted from in situ spectroradiometry data and the QuickBird image correlated with the LAI, plant-h, AGB, and SPAD value of wheat crop across the study site. The determination coefficient (R²) values of the association between V-SRIs of in situ spectroradiometry data and various determined wheat characteristics varied from 0.26 to 0.85. The ANN-GSIs-3 was found to be the optimum predictive model, demonstrating a greater relationship between the advanced features and LAI. The three features of V-SRIs comprised in this model were strongly significant for the prediction of LAI. The attained results indicated high R² values of 0.94 and 0.86 for the training and validation phases. The ANN-GSIs-3 model constructed for the determination of chlorophyll in the plant which had higher performance expectations (R² = 0.96 and 0.92 for training and validation datasets, respectively). In conclusion, the results of our study revealed that high resolution remote sensing images such as QuickBird or similar imagery, and in situ spectroradiometry measurements have the feasibility of providing necessary crop monitoring data across non-stressed and stressed (drought and salinity) conditions when integrating V-SRIs with ANN and RF algorithms. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Agriculture)

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19 pages, 3022 KiB

Open AccessArticle

Machine Learning Approach to Predict Air Temperature and Relative Humidity inside Mechanically and Naturally Ventilated Duck Houses: Application of Recurrent Neural Network

by Sang-yeon Lee, In-bok Lee, Uk-hyeon Yeo, Jun-gyu Kim and Rack-woo Kim

Agriculture 2022, 12(3), 318; https://doi.org/10.3390/agriculture12030318 - 22 Feb 2022

Cited by 10 | Viewed by 2977

Abstract

The duck industry ranks sixth as one of the fastest-growing major industries for livestock production in South Korea. However, there are few studies quantitatively predicting the internal thermal and moisture environment of duck houses. In this study, high-accuracy recurrent neural network (RNN) models [...] Read more.

The duck industry ranks sixth as one of the fastest-growing major industries for livestock production in South Korea. However, there are few studies quantitatively predicting the internal thermal and moisture environment of duck houses. In this study, high-accuracy recurrent neural network (RNN) models were used to predict the internal air temperature and relative humidity of mechanically and naturally ventilated duck houses. The models were developed according to the type of duck houses, seasons, and environmental variables by learning the monitoring data of the internal and external environments. The optimal sequence length of learning data for the development of the RNN model was selected as 120 min. As a result of the validation, both air temperature and relative humidity could be accurately predicted within 1% error. In addition, simplified RNN models were additionally developed by learning only from the data of external air temperature, relative humidity, and duck weight, which are relatively easy to acquire at the farms. The accuracy of the simplified RNN models was similar to the basic model for predicting the internal air temperature and relative humidity of duck houses in real time. In the future, for the convergence of information and communications technologies (ICTs) and application of smart farms in duck houses, the RNN models of duck houses developed in this study can be applied to predict and control the internal environments of duck houses using the model predictive control (MPC) technique. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Agriculture)

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17 pages, 97282 KiB

Open AccessEditor’s ChoiceArticle

Artificial Intelligence-Based Real-Time Pineapple Quality Classification Using Acoustic Spectroscopy

by Ting-Wei Huang, Showkat Ahmad Bhat, Nen-Fu Huang, Chung-Ying Chang, Pin-Cheng Chan and Arnold R. Elepano

Agriculture 2022, 12(2), 129; https://doi.org/10.3390/agriculture12020129 - 18 Jan 2022

Cited by 17 | Viewed by 5106

Abstract

The pineapple is an essential fruit in Taiwan. Farmers separate pineapples into two types, according to the percentages of water in the pineapples. One is the “drum sound pineapple” and the other is the “meat sound pineapple”. As there is more water in [...] Read more.

The pineapple is an essential fruit in Taiwan. Farmers separate pineapples into two types, according to the percentages of water in the pineapples. One is the “drum sound pineapple” and the other is the “meat sound pineapple”. As there is more water in the meat sound pineapple, the meat sound pineapple more easily rots and is more challenging to store than the drum sound pineapple. Thus, farmers need to filter out the meat sound pineapple, so that they can sell pineapples overseas. The classification, based on striking the pineapple fruit with rigid objects (e.g., plastic rulers) is most commonly used by farmers due to the negligibly low costs and availability. However, it is a time-consuming job, so we propose a method to automatically classify pineapples in this work. Using embedded onboard computing processors, servo, and an ultrasonic sensor, we built a hitting machine and combined it with a conveyor to automatically separate pineapples. To classify pineapples, we proposed a method related to acoustic spectrogram spectroscopy, which uses acoustic data to generate spectrograms. In the acoustic data collection step, we used the hitting machine mentioned before and collected many groups of data with different factors; some groups also included the noise in the farm. With these differences, we tested our deep learning-based convolutional neural network (CNN) performances. The best accuracy of the developed CNN model is 0.97 for data Group V. The proposed hitting machine and the CNN model can assist in the classification of pineapple fruits with high accuracy and time efficiency. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Agriculture)

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11 pages, 2005 KiB

Open AccessArticle

Rice (Oryza sativa L.) Growth Modeling Based on Growth Degree Day (GDD) and Artificial Intelligence Algorithms

by Li-Wei Liu, Chun-Tang Lu, Yu-Min Wang, Kuan-Hui Lin, Xingmao Ma and Wen-Shin Lin

Agriculture 2022, 12(1), 59; https://doi.org/10.3390/agriculture12010059 - 3 Jan 2022

Cited by 11 | Viewed by 4771

Abstract

Rice (Oryza sativa L.) growth prediction is key for precise rice production. However, the traditional linear rice growth forecasting model is ineffective under rapidly changing climate conditions. Here we show that growth rate (Gr) can be well-predicted by artificial intelligence (AI)-based artificial [...] Read more.

Rice (Oryza sativa L.) growth prediction is key for precise rice production. However, the traditional linear rice growth forecasting model is ineffective under rapidly changing climate conditions. Here we show that growth rate (Gr) can be well-predicted by artificial intelligence (AI)-based artificial neural networks (ANN) and gene-expression programming (GEP), with accumulated air temperatures based on growth degree day (GDD). In total, 10,246 Gr from 95 cultivations were obtained with three cultivars, TK9, TNG71, and KH147, in Central and Southern Taiwan. The model performance was evaluated by the Pearson correlation coefficient (r), root mean square error (RMSE), and relative RMSE (r-RMSE) in the whole growth period (lifecycle), as well as the average and specific key stages (transplanting, 50% initial tillering, panicle initiation, 50% heading, and physiological maturity). The results in lifecycle Gr modeling showed that ANN and GEP models had comparable r (0.9893), but the GEP model had the lowest RMSE (3.83 days) and r-RMSE (7.24%). In stage average and specific key stages, each model has its own best-fit growth period. Overall, GEP model is recommended for rice growth prediction considering the model performance, applicability, and routine farming work. This study may lead to smart rice production due to the enhanced capacity to predict rice growth in the field. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Agriculture)

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20 pages, 31778 KiB

Open AccessArticle

Application of Optical Spectrometer to Determine Maturity Level of Oil Palm Fresh Fruit Bunches Based on Analysis of the Front Equatorial, Front Basil, Back Equatorial, Back Basil and Apical Parts of the Oil Palm Bunches

by Jia Quan Goh, Abdul Rashid Mohamed Shariff and Nazmi Mat Nawi

Agriculture 2021, 11(12), 1179; https://doi.org/10.3390/agriculture11121179 - 23 Nov 2021

Cited by 4 | Viewed by 2943

Abstract

The quality of palm oil depends on the maturity level of the oil palm fresh fruit bunch (FFB). This research applied an optical spectrometer to collect the reflectance data of 96 FFB from unripe, ripe, and overripe classes for the maturity level classification. [...] Read more.

The quality of palm oil depends on the maturity level of the oil palm fresh fruit bunch (FFB). This research applied an optical spectrometer to collect the reflectance data of 96 FFB from unripe, ripe, and overripe classes for the maturity level classification. The spectrometer scanned the FFB from different parts, including apical, front equatorial, front basil, back equatorial, and back basil. Principal component analysis was carried out to extract principal components from the reflectance data of each of the parts. The extracted principal components were used in an ANOVA test, which found that the reflectance data of the front equatorial showed statistically significant differences between the three maturity groups. Then, the collected reflectance data was subjected to machine learning training and testing by using the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). The front equatorial achieved the highest accuracy, of 90.6%, by using SVM as classifiers; thus, it was proven to be the most optimal part of FFB that can be utilized for maturity classification. Next, the front equatorial dataset was divided into UV (180–400 nm), blue (450–490 nm), green (500–570 nm), red (630–700 nm), and NIR (800–1100 nm) regions for classification testing. The UV bands showed a 91.7% accuracy. After this, representative bands of 365, 460, 523, 590, 623, 660, 735, and 850 nm were extracted from the front equatorial dataset for further classification testing. The 660 nm band achieved an 89.6% accuracy using KNN as a classifier. Composite models were built from the representative bands. The combination of 365, 460, 735, and 850 nm had the highest accuracy in this research, which was 93.8% with the use of SVM. In conclusion, these research findings showed that the front equatorial has the better ability for maturity classification, whereas the composite model with only four bands has the best accuracy. These findings are useful to the industry for future oil palm FFB classification research. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Agriculture)

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19 pages, 6252 KiB

Open AccessEditor’s ChoiceArticle

Reliable Identification of Oolong Tea Species: Nondestructive Testing Classification Based on Fluorescence Hyperspectral Technology and Machine Learning

by Yan Hu, Lijia Xu, Peng Huang, Xiong Luo, Peng Wang and Zhiliang Kang

Agriculture 2021, 11(11), 1106; https://doi.org/10.3390/agriculture11111106 - 6 Nov 2021

Cited by 26 | Viewed by 3215

Abstract

A rapid and nondestructive tea classification method is of great significance in today’s research. This study uses fluorescence hyperspectral technology and machine learning to distinguish Oolong tea by analyzing the spectral features of tea in the wavelength ranging from 475 to 1100 nm. [...] Read more.

A rapid and nondestructive tea classification method is of great significance in today’s research. This study uses fluorescence hyperspectral technology and machine learning to distinguish Oolong tea by analyzing the spectral features of tea in the wavelength ranging from 475 to 1100 nm. The spectral data are preprocessed by multivariate scattering correction (MSC) and standard normal variable (SNV), which can effectively reduce the impact of baseline drift and tilt. Then principal component analysis (PCA) and t-distribution random neighborhood embedding (t-SNE) are adopted for feature dimensionality reduction and visual display. Random Forest-Recursive Feature Elimination (RF-RFE) is used for feature selection. Decision Tree (DT), Random Forest Classification (RFC), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are used to establish the classification model. The results show that MSC-RF-RFE-SVM is the best model for the classification of Oolong tea in which the accuracy of the training set and test set is 100% and 98.73%, respectively. It can be concluded that fluorescence hyperspectral technology and machine learning are feasible to classify Oolong tea. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Agriculture)

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Review

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19 pages, 1046 KiB

Open AccessReview

Applications of Electronic Nose Coupled with Statistical and Intelligent Pattern Recognition Techniques for Monitoring Tea Quality: A Review

by Sushant Kaushal, Pratik Nayi, Didit Rahadian and Ho-Hsien Chen

Agriculture 2022, 12(9), 1359; https://doi.org/10.3390/agriculture12091359 - 1 Sep 2022

Cited by 19 | Viewed by 4197

Abstract

Tea is the most widely consumed non-alcoholic beverage worldwide. In the tea sector, the high demand for tea has led to an increase in the adulteration of superior tea grades. The procedure of evaluating tea quality is difficult to assure the highest degree [...] Read more.

Tea is the most widely consumed non-alcoholic beverage worldwide. In the tea sector, the high demand for tea has led to an increase in the adulteration of superior tea grades. The procedure of evaluating tea quality is difficult to assure the highest degree of tea safety in the context of consumer preferences. In recent years, the advancement in sensor technology has replaced the human olfaction system with an artificial olfaction system, i.e., electronic noses (E-noses) for quality control of teas to differentiate the distinct aromas. Therefore, in this review, the potential applications of E-nose as a monitoring device for different teas have been investigated. The instrumentation, working principles, and different gas sensor types employed for E-nose applications have been introduced. The widely used statistical and intelligent pattern recognition methods, namely, PCA, LDA, PLS-DA, KNN, ANN, CNN, SVM, etc., have been discussed in detail. The challenges and the future trends for E-nose devices have also been highlighted. Overall, this review provides the insight that E-nose combined with an appropriate pattern recognition method is a powerful non-destructive tool for monitoring tea quality. In future, E-noses will undoubtedly reduce their shortcomings with improved detection accuracy and consistency by employing food quality testing. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Agriculture)

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