Agriculture

Research

Jump to: Review

23 pages, 12582 KiB

Open AccessArticle

Digital Analysis with the Help of an Integrated UAV System for the Surveillance of Fruit and Wine Areas

by George Ipate, Catalina Tudora and Filip Ilie

Agriculture 2024, 14(11), 1930; https://doi.org/10.3390/agriculture14111930 - 30 Oct 2024

Viewed by 609

Abstract

The main purpose of this study was to create a prototype of an unmanned aerial system equipped with intelligent hardware and software technologies necessary for surveillance and monitoring the health and growth of crops from orchards with vines and fruit trees. Using low-cost [...] Read more.

The main purpose of this study was to create a prototype of an unmanned aerial system equipped with intelligent hardware and software technologies necessary for surveillance and monitoring the health and growth of crops from orchards with vines and fruit trees. Using low-cost sensors that accurately measure ultraviolet solar radiation was an important objective. The device, which needed to be attached to the commercial DJI Mini 4 Pro drone, had to be small, portable, and have low energy consumption. For this purpose, the widely used Vishay VEML6075 digital optical sensor was selected and implemented in a prototype, alongside a Raspberry Pi Zero 2 W minicomputer. To collect data from these sensors, a program written in Python was used, containing specific blocks for data acquisition from each sensor, to facilitate the monitoring of ultraviolet (UV) radiation, or battery current. By analyzing the data obtained from the sensors, several important conclusions were drawn that may provide valuable pathways for the further development of mobile or modular equipment. Furthermore, the plantation state analysis results with proposed models in the geographic information system (GIS) environment are also presented. The visualization of maps indicating variations in vegetation conditions led to identifying problems such as hydric stress. Full article

(This article belongs to the Special Issue Applications of Data Analysis in Agriculture—2nd Edition)

► Show Figures

Figure 1

27 pages, 5483 KiB

Open AccessArticle

The Development of a Prediction Model Related to Food Loss and Waste in Consumer Segments of Agrifood Chain Using Machine Learning Methods

by Daniel Nijloveanu, Victor Tița, Nicolae Bold, Doru Anastasiu Popescu, Dragoș Smedescu, Cosmina Smedescu and Gina Fîntîneru

Agriculture 2024, 14(10), 1837; https://doi.org/10.3390/agriculture14101837 - 18 Oct 2024

Viewed by 535

Abstract

Food loss and waste (FLW) is a primary focus topic related to all human activity. This phenomenon has a great deal of importance due to its effect on the economic and social aspects of human systems. The most integrated approach to food waste [...] Read more.

Food loss and waste (FLW) is a primary focus topic related to all human activity. This phenomenon has a great deal of importance due to its effect on the economic and social aspects of human systems. The most integrated approach to food waste analysis is based on the study of FLW alongside the agrifood chain, which has also been performed in previous studies by the authors. This paper presents a modality of determination of food loss and waste effects with an emphasis on consumer segments in agrifood chains in the form of a predictive model based on statistical data collected based on specific methods in Romania. The determination is made comparatively, using two predictive machine learning-based methods and separate instruments (software), in order to establish the best model that fits the collected data structure. In this matter, a Decision Tree Approach (DTA) and a Neural Network Approach (NNA) will be developed, and common methodologies of the approaches will be applied. The results will determine predictive outcomes for a specific food waste (FW) agent (e.g., consumer) based on pattern recognition of the collected data. The results showed relatively high-accuracy predictions, especially for the NN approach, with lower performances using the DTA. The effects of the application of this predictive model will be expected to improve the food loss prevention measures within economic contexts when applied to real-life scenarios. Full article

(This article belongs to the Special Issue Applications of Data Analysis in Agriculture—2nd Edition)

► Show Figures

Figure 1

14 pages, 5103 KiB

Open AccessArticle

Calibration and Testing of Discrete Elemental Simulation Parameters for Pod Pepper Seeds

by Xingye Chen, Jing Bai, Xinzhong Wang, Weiquan Fang, Tianyu Hong, Nan Zang, Liangliang Fang and Gaoliang Wang

Agriculture 2024, 14(6), 831; https://doi.org/10.3390/agriculture14060831 - 26 May 2024

Viewed by 2716

Abstract

The discrete elemental parameters of pod pepper seeds were calibrated for future numerical optimization of the pod pepper seed cleaning device. The study concentrates on calibrating the intrinsic and contact parameters of pod pepper seeds utilizing the discrete element method. Compression tests were [...] Read more.

The discrete elemental parameters of pod pepper seeds were calibrated for future numerical optimization of the pod pepper seed cleaning device. The study concentrates on calibrating the intrinsic and contact parameters of pod pepper seeds utilizing the discrete element method. Compression tests were performed to ascertain intrinsic parameters such as Poisson’s ratio and the seeds’ elastic modulus. The static friction coefficient and collision restitution coefficient between the seeds and steel plates were identified through incline and free-fall tests. Plackett–Burman, steepest ascent, and Box–Behnken experiments were performed to establish a second-order regression model correlating significant parameters with the angle of repose. The optimal parameter combination, based on the measured angle of repose (32.45°), yielded static friction coefficients between seeds, rolling friction coefficients between seeds, and static friction coefficients between seeds and steel plates of 0.608, 0.018, and 0.787, respectively. The two-sample t-test of the physical and simulated repose angles yielded p > 0.05, and the relative error of the physical and simulated repose angles was 0.68%, which confirmed the reliability of the calibration parameters. The findings indicate that the calibration method for pod pepper seeds effectively informs the calibration of parameters for other irregular crops. Full article

(This article belongs to the Special Issue Applications of Data Analysis in Agriculture—2nd Edition)

► Show Figures

Figure 1

20 pages, 3991 KiB

Open AccessArticle

Prediction of Live Bulb Weight for Field Vegetables Using Functional Regression Models and Machine Learning Methods

by Dahyun Kim, Wanhyun Cho, Inseop Na and Myung Hwan Na

Agriculture 2024, 14(5), 754; https://doi.org/10.3390/agriculture14050754 - 12 May 2024

Cited by 1 | Viewed by 1524

Abstract

(1) Background: This challenge is exacerbated by the aging of the rural population, leading to a scarcity of available manpower. To address this issue, the automation and mechanization of outdoor vegetable cultivation are imperative. Therefore, developing an automated cultivation platform that reduces labor [...] Read more.

(1) Background: This challenge is exacerbated by the aging of the rural population, leading to a scarcity of available manpower. To address this issue, the automation and mechanization of outdoor vegetable cultivation are imperative. Therefore, developing an automated cultivation platform that reduces labor requirements and improves yield by efficiently performing all the cultivation activities related to field vegetables, particularly onions and garlic, is essential. In this study, we propose methods to identify onion and garlic plants with the best growth status and accurately predict their live bulb weight by regularly photographing their growth status using a multispectral camera mounted on a drone. (2) Methods: This study was conducted in four stages. First, two pilot blocks with a total of 16 experimental units, four horizontals, and four verticals were installed for both onions and garlic. Overall, a total of 32 experimental units were prepared for both onion and garlic. Second, multispectral image data were collected using a multispectral camera repeating a total of seven times for each area in 32 experimental units prepared for both onions and garlic. Simultaneously, growth data and live bulb weight at the corresponding points were recorded manually. Third, correlation analysis was conducted to determine the relationship between various vegetation indexes extracted from multispectral images and the manually measured growth data and live bulb weights. Fourth, based on the vegetation indexes extracted from multispectral images and previously collected growth data, a method to predict the live bulb weight of onions and garlic in real time during the cultivation period, using functional regression models and machine learning methods, was examined. (3) Results: The experimental results revealed that the Functional Concurrence Regression (FCR) model exhibited the most robust prediction performance both when using growth factors and when using vegetation indexes. Following closely, with a slight distinction, Gaussian Process Functional Data Analysis (GPFDA), Random Forest Regression (RFR), and AdaBoost demonstrated the next-best predictive power. However, a Support Vector Machine (SVM) and Deep Neural Network (DNN) displayed comparatively poorer predictive power. Notably, when employing growth factors as explanatory variables, all prediction models exhibited a slightly improved performance compared to that when using vegetation indexes. (4) Discussion: This study explores predicting onion and garlic bulb weights in real-time using multispectral imaging and machine learning, filling a gap in research where previous studies primarily focused on utilizing artificial intelligence and machine learning for productivity enhancement, disease management, and crop monitoring. (5) Conclusions: In this study, we developed an automated method to predict the growth trajectory of onion and garlic bulb weights throughout the growing season by utilizing multispectral images, growth factors, and live bulb weight data, revealing that the FCR model demonstrated the most robust predictive performance among six artificial intelligence models tested. Full article

(This article belongs to the Special Issue Applications of Data Analysis in Agriculture—2nd Edition)

► Show Figures

Figure 1

24 pages, 7413 KiB

Open AccessArticle

An Accurate Approach for Predicting Soil Quality Based on Machine Learning in Drylands

by Radwa A. El Behairy, Hasnaa M. El Arwash, Ahmed A. El Baroudy, Mahmoud M. Ibrahim, Elsayed Said Mohamed, Nazih Y. Rebouh and Mohamed S. Shokr

Agriculture 2024, 14(4), 627; https://doi.org/10.3390/agriculture14040627 - 18 Apr 2024

Cited by 3 | Viewed by 2426

Abstract

Nowadays, machine learning (ML) is a useful technology due to its high accuracy in constructing non-linear models and algorithms that can adapt to the complexity and diversity of data. Thus, the current work aimed to predict the soil quality index (SQI) from extensive [...] Read more.

Nowadays, machine learning (ML) is a useful technology due to its high accuracy in constructing non-linear models and algorithms that can adapt to the complexity and diversity of data. Thus, the current work aimed to predict the soil quality index (SQI) from extensive soil data, achieving high accuracy with the artificial neural networks (ANN) model. However, the efficiency of ANN depends on the accuracy of the data that is prepared for training. For this purpose, MATLAB programming language was used to enable the calculation, classification, and compilation of the results into databases within a few minutes. The proposed MATLAB program was highly efficient, accurate, and quick in calculating soil big data for training the machine compared with traditional methods. The database contains 306 vector sets, 80% of them are used for training and the remaining 20% are reserved for testing. The optimal model obtained comprises one hidden layer with 250 neurons and one output layer with a sigmoid function. The ANN achieved a high coefficient of determination (R²) values for SQI estimation, with around 0.97 and 0.98 for training and testing, respectively. The results indicate that 36.93% of the total soil samples belonged to the very high quality class (C1). In contrast, the high quality (C2), moderate quality (C3), low quality (C4), and very low quality (C5) classes accounted for 10.46%, 31.37%, 20.92%, and 0.33% of the samples, respectively. The high contents of CaCO₃, pH, sodium saturation, salinity, and clay content were identified as limiting factors in certain areas. The results of this study indicated high accuracy of soil quality assessment using physical, chemical, and fertility soil features in regression analysis with ANN. This method, which is suitable for arid zones, enhances agricultural productivity and decision-making by identifying critical soil quality categories and constraints. Full article

(This article belongs to the Special Issue Applications of Data Analysis in Agriculture—2nd Edition)

► Show Figures

Figure 1

21 pages, 3200 KiB

Open AccessArticle

Hybrid Deep Neural Networks with Multi-Tasking for Rice Yield Prediction Using Remote Sensing Data

by Che-Hao Chang, Jason Lin, Jia-Wei Chang, Yu-Shun Huang, Ming-Hsin Lai and Yen-Jen Chang

Agriculture 2024, 14(4), 513; https://doi.org/10.3390/agriculture14040513 - 22 Mar 2024

Cited by 1 | Viewed by 1625

Abstract

Recently, data-driven approaches have become the dominant solution for prediction problems in agricultural industries. Several deep learning models have been applied to crop yield prediction in smart farming. In this paper, we proposed an efficient hybrid deep learning model that coordinates the outcomes [...] Read more.

Recently, data-driven approaches have become the dominant solution for prediction problems in agricultural industries. Several deep learning models have been applied to crop yield prediction in smart farming. In this paper, we proposed an efficient hybrid deep learning model that coordinates the outcomes of a classification model and a regression model in deep learning via the shared layers to predict the rice crop yield. Three statistical analyses on the features, including Pearson correlation coefficients (PCC), Shapley additive explanations (SHAP), and recursive feature elimination with cross-validation (RFECV), are proposed to select the most relevant ones for the predictive goal to reduce the model training time. The data preprocessing normalizes the features of the collected data into specific ranges of values and then reformats them into a three-dimensional matrix. As a result, the root-mean-square error (RMSE) of the proposed model in rice yield prediction has achieved 344.56 and an R-squared of 0.64. The overall performance of the proposed model is better than the other deep learning models, such as the multi-parametric deep neural networks (MDNNs) (i.e., RMSE = 370.80, R-squared = 0.59) and the artificial neural networks (ANNs) (i.e., RMSE = 550.03, R-squared = 0.09). The proposed model has demonstrated significant improvement in the predictive results of distinguishing high yield from low yield with 90% accuracy and 94% F1 score. Full article

(This article belongs to the Special Issue Applications of Data Analysis in Agriculture—2nd Edition)

► Show Figures

Figure 1

16 pages, 3171 KiB

Open AccessArticle

Research on Entity and Relationship Extraction with Small Training Samples for Cotton Pests and Diseases

by Weiwei Yuan, Wanxia Yang, Liang He, Tingwei Zhang, Yan Hao, Jing Lu and Wenbo Yan

Agriculture 2024, 14(3), 457; https://doi.org/10.3390/agriculture14030457 - 11 Mar 2024

Viewed by 1315

Abstract

The extraction of entities and relationships is a crucial task in the field of natural language processing (NLP). However, existing models for this task often rely heavily on a substantial amount of labeled data, which not only consumes time and labor but also [...] Read more.

The extraction of entities and relationships is a crucial task in the field of natural language processing (NLP). However, existing models for this task often rely heavily on a substantial amount of labeled data, which not only consumes time and labor but also hinders the development of downstream tasks. Therefore, with a focus on enhancing the model’s ability to learn from small samples, this paper proposes an entity and relationship extraction method based on the Universal Information Extraction (UIE) model. The core of the approach is the design of a specialized prompt template and schema on cotton pests and diseases as one of the main inputs to the UIE, which, under its guided fine-tuning, enables the model to subdivide the entity and relationship in the corpus. As a result, the UIE-base model achieves an accuracy of 86.5% with only 40 labeled training samples, which really solves the problem of the existing models that require a large amount of manually labeled training data for knowledge extraction. To verify the generalization ability of the model in this paper, experiments are designed to compare the model with four classical models, such as the Bert-BiLSTM-CRF. The experimental results show that the F1 value on the self-built cotton data set is 1.4% higher than that of the Bert-BiLSTM-CRF model, and the F1 value on the public data set is 2.5% higher than that of the Bert-BiLSTM-CRF model. Furthermore, experiments are designed to verify that the UIE-base model has the best small-sample learning performance when the number of samples is 40. This paper provides an effective method for small-sample knowledge extraction. Full article

(This article belongs to the Special Issue Applications of Data Analysis in Agriculture—2nd Edition)

► Show Figures

Figure 1

Review

Jump to: Research

36 pages, 3597 KiB

Open AccessReview

Artificial Intelligence in Agricultural Mapping: A Review

by Ramón Espinel, Gricelda Herrera-Franco, José Luis Rivadeneira García and Paulo Escandón-Panchana

Agriculture 2024, 14(7), 1071; https://doi.org/10.3390/agriculture14071071 - 3 Jul 2024

Cited by 1 | Viewed by 3790

Abstract

Artificial intelligence (AI) plays an essential role in agricultural mapping. It reduces costs and time and increases efficiency in agricultural management activities, which improves the food industry. Agricultural mapping is necessary for resource management and requires technologies for farming challenges. The mapping in [...] Read more.

Artificial intelligence (AI) plays an essential role in agricultural mapping. It reduces costs and time and increases efficiency in agricultural management activities, which improves the food industry. Agricultural mapping is necessary for resource management and requires technologies for farming challenges. The mapping in agricultural AI applications gives efficiency in mapping and its subsequent use in decision-making. This study analyses AI’s current state in agricultural mapping through bibliometric indicators and a literature review to identify methods, agricultural resources, geomatic tools, mapping types, and their applications in agricultural management. The methodology begins with a bibliographic search in Scopus and the Web of Science (WoS). Subsequently, a bibliographic data analysis and literature review establish the scientific contribution, collaboration, AI methods, and trends. The United States (USA), Spain, and Italy are countries that produce and collaborate more in this area of knowledge. Of the studies, 76% use machine learning (ML) and 24% use deep learning (DL) for agricultural mapping applications. Prevailing algorithms such as Random Forest (RF), Artificial Neural Networks (ANNs), and Support Vector Machines (SVMs) correlate mapping activities in agricultural management. In addition, AI contributes to agricultural mapping in activities associated with production, disease detection, crop classification, rural planning, forest dynamics, and irrigation system improvements. Full article

(This article belongs to the Special Issue Applications of Data Analysis in Agriculture—2nd Edition)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Applications of Data Analysis in Agriculture—2nd Edition

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (8 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI