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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Smart Agriculture".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 31412

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Dr. Krista Rizman Žalik

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1. Faculty of Natural Sciences and Mathematics, University of Maribor, SI-2000 Maribor, Slovenia
2. Faculty of Electrical Engineering and Computer Science, University of Maribor, SI-2000 Maribor, Slovenia
Interests: knowledge discovery; data mining; clustering; community detection; complex networks; data compression
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Dr. Štefan Kohek

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Faculty of electrical engineering and computer science, University of Maribor, SI-2000 Maribor, Slovenia
Interests: tree growth simulation; parallel computation; remote sensing; evolutionary computation; computer graphics; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

You are invited to submit a manuscript to this special issue of Sensors. This issue will consider cutting-edge research on the development and applications of machine learning on agricultural data obtained by sensors. Recently, we have seen a growing interest in the use of machine learning which offers new opportunities for classification, prediction, and analysis of agricultural and environmental spatial-temporal and other data obtained by in situ and remote sensors.

This Special Issue therefore aims to put together original research and survey articles from academia and industry on recent advances, technologies, solutions, applications, and new challenges in the field of using machine learning on agricultural and environmental data obtained by sensors.

Dr. Krista Zalik
Dr. Štefan Kohek
Guest Editors

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Keywords

machine learning
artificial neural networks
convolutional neural networks
recurrent neural networks
advanced deep learning models and techniques, such as deep neural networks, deep belief networks, and recurrent neural networks (GAN, DNN, RNN), and Long Short-Term Memory (LSTM)
deep-learning-based 3D/Multiview sensing, imaging, and video processing
sensor networks
multi sensor
data fusion
data mining
decision support systems
deep-learning-based visual object detection, tracking, and understanding

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Related Special Issue

Machine Learning and Sensors Technology in Agriculture: 2nd Edition in Sensors

Published Papers (12 papers)

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Research

Jump to: Review

11 pages, 548 KiB

Open AccessArticle

Machine Learning-Based Prediction of Cattle Activity Using Sensor-Based Data

by Guillermo Hernández, Carlos González-Sánchez, Angélica González-Arrieta, Guillermo Sánchez-Brizuela and Juan-Carlos Fraile

Sensors 2024, 24(10), 3157; https://doi.org/10.3390/s24103157 - 16 May 2024

Viewed by 1121

Abstract

Livestock monitoring is a task traditionally carried out through direct observation by experienced caretakers. By analyzing its behavior, it is possible to predict to a certain degree events that require human action, such as calving. However, this continuous monitoring is in many cases not feasible. In this work, we propose, develop and evaluate the accuracy of intelligent algorithms that operate on data obtained by low-cost sensors to determine the state of the animal in the terms used by the caregivers (grazing, ruminating, walking, etc.). The best results have been obtained using aggregations and averages of the time series with support vector classifiers and tree-based ensembles, reaching accuracies of 57% for the general behavior problem (4 classes) and 85% for the standing behavior problem (2 classes). This is a preliminary step to the realization of event-specific predictions. Full article

(This article belongs to the Special Issue Machine Learning and Sensors Technology in Agriculture)

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15 pages, 974 KiB

Open AccessArticle

Predictive Neural Network Modeling for Almond Harvest Dust Control

by Reza Serajian, Jian-Qiao Sun, Jeanette Cobian-Iñiguez and Reza Ehsani

Sensors 2024, 24(7), 2136; https://doi.org/10.3390/s24072136 - 27 Mar 2024

Cited by 1 | Viewed by 874

Abstract

This study introduces a neural network-based approach to predict dust emissions, specifically PM2.5 particles, during almond harvesting in California. Using a feedforward neural network (FNN), this research predicted PM2.5 emissions by analyzing key operational parameters of an advanced almond harvester. Preprocessing steps like outlier removal and normalization were employed to refine the dataset for training. The network’s architecture was designed with two hidden layers and optimized using tanh activation and MSE loss functions through the Adam algorithm, striking a balance between model complexity and predictive accuracy. The model was trained on extensive field data from an almond pickup system, including variables like brush speed, angular velocity, and harvester forward speed. The results demonstrate a notable predictive accuracy of the FNN model, with a mean squared error (MSE) of 0.02 and a mean absolute error (MAE) of 0.01, indicating high precision in forecasting PM2.5 levels. By integrating machine learning with agricultural practices, this research provides a significant tool for environmental management in almond production, offering a method to reduce harmful emissions while maintaining operational efficiency. This model presents a solution for the almond industry and sets a precedent for applying predictive analytics in sustainable agriculture. Full article

(This article belongs to the Special Issue Machine Learning and Sensors Technology in Agriculture)

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27 pages, 47076 KiB

Open AccessArticle

Customized Tracking Algorithm for Robust Cattle Detection and Tracking in Occlusion Environments

by Wai Hnin Eaindrar Mg, Pyke Tin, Masaru Aikawa, Ikuo Kobayashi, Yoichiro Horii, Kazuyuki Honkawa and Thi Thi Zin

Sensors 2024, 24(4), 1181; https://doi.org/10.3390/s24041181 - 11 Feb 2024

Cited by 1 | Viewed by 1242

Abstract

Ensuring precise calving time prediction necessitates the adoption of an automatic and precisely accurate cattle tracking system. Nowadays, cattle tracking can be challenging due to the complexity of their environment and the potential for missed or false detections. Most existing deep-learning tracking algorithms face challenges when dealing with track-ID switch cases caused by cattle occlusion. To address these concerns, the proposed research endeavors to create an automatic cattle detection and tracking system by leveraging the remarkable capabilities of Detectron2 while embedding tailored modifications to make it even more effective and efficient for a variety of applications. Additionally, the study conducts a comprehensive comparison of eight distinct deep-learning tracking algorithms, with the objective of identifying the most optimal algorithm for achieving precise and efficient individual cattle tracking. This research focuses on tackling occlusion conditions and track-ID increment cases for miss detection. Through a comparison of various tracking algorithms, we discovered that Detectron2, coupled with our customized tracking algorithm (CTA), achieves 99% in detecting and tracking individual cows for handling occlusion challenges. Our algorithm stands out by successfully overcoming the challenges of miss detection and occlusion problems, making it highly reliable even during extended periods in a crowded calving pen. Full article

(This article belongs to the Special Issue Machine Learning and Sensors Technology in Agriculture)

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28 pages, 3476 KiB

Open AccessArticle

EfficientRMT-Net—An Efficient ResNet-50 and Vision Transformers Approach for Classifying Potato Plant Leaf Diseases

by Kashif Shaheed, Imran Qureshi, Fakhar Abbas, Sohail Jabbar, Qaisar Abbas, Hafsa Ahmad and Muhammad Zaheer Sajid

Sensors 2023, 23(23), 9516; https://doi.org/10.3390/s23239516 - 30 Nov 2023

Cited by 15 | Viewed by 4686

Abstract

The primary objective of this study is to develop an advanced, automated system for the early detection and classification of leaf diseases in potato plants, which are among the most cultivated vegetable crops worldwide. These diseases, notably early and late blight caused by Alternaria solani and Phytophthora infestans, significantly impact the quantity and quality of global potato production. We hypothesize that the integration of Vision Transformer (ViT) and ResNet-50 architectures in a new model, named EfficientRMT-Net, can effectively and accurately identify various potato leaf diseases. This approach aims to overcome the limitations of traditional methods, which are often labor-intensive, time-consuming, and prone to inaccuracies due to the unpredictability of disease presentation. EfficientRMT-Net leverages the CNN model for distinct feature extraction and employs depth-wise convolution (DWC) to reduce computational demands. A stage block structure is also incorporated to improve scalability and sensitive area detection, enhancing transferability across different datasets. The classification tasks are performed using a global average pooling layer and a fully connected layer. The model was trained, validated, and tested on custom datasets specifically curated for potato leaf disease detection. EfficientRMT-Net’s performance was compared with other deep learning and transfer learning techniques to establish its efficacy. Preliminary results show that EfficientRMT-Net achieves an accuracy of 97.65% on a general image dataset and 99.12% on a specialized Potato leaf image dataset, outperforming existing methods. The model demonstrates a high level of proficiency in correctly classifying and identifying potato leaf diseases, even in cases of distorted samples. The EfficientRMT-Net model provides an efficient and accurate solution for classifying potato plant leaf diseases, potentially enabling farmers to enhance crop yield while optimizing resource utilization. This study confirms our hypothesis, showcasing the effectiveness of combining ViT and ResNet-50 architectures in addressing complex agricultural challenges. Full article

(This article belongs to the Special Issue Machine Learning and Sensors Technology in Agriculture)

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24 pages, 17328 KiB

Open AccessArticle

Automated Cow Body Condition Scoring Using Multiple 3D Cameras and Convolutional Neural Networks

by Gary I. Summerfield, Allan De Freitas, Este van Marle-Koster and Herman C. Myburgh

Sensors 2023, 23(22), 9051; https://doi.org/10.3390/s23229051 - 8 Nov 2023

Cited by 1 | Viewed by 2649

Abstract

Body condition scoring is an objective scoring method used to evaluate the health of a cow by determining the amount of subcutaneous fat in a cow. Automated body condition scoring is becoming vital to large commercial dairy farms as it helps farmers score their cows more often and more consistently compared to manual scoring. A common approach to automated body condition scoring is to utilise a CNN-based model trained with data from a depth camera. The approaches presented in this paper make use of three depth cameras placed at different positions near the rear of a cow to train three independent CNNs. Ensemble modelling is used to combine the estimations of the three individual CNN models. The paper aims to test the performance impact of using ensemble modelling with the data from three separate depth cameras. The paper also looks at which of these three cameras and combinations thereof provide a good balance between computational cost and performance. The results of this study show that utilising the data from three depth cameras to train three separate models merged through ensemble modelling yields significantly improved automated body condition scoring accuracy compared to a single-depth camera and CNN model approach. This paper also explored the real-world performance of these models on embedded platforms by comparing the computational cost to the performance of the various models. Full article

(This article belongs to the Special Issue Machine Learning and Sensors Technology in Agriculture)

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14 pages, 40696 KiB

Open AccessArticle

Object Detection for Agricultural Vehicles: Ensemble Method Based on Hierarchy of Classes

by Esma Mujkic, Martin P. Christiansen and Ole Ravn

Sensors 2023, 23(16), 7285; https://doi.org/10.3390/s23167285 - 20 Aug 2023

Cited by 2 | Viewed by 1657

Abstract

Vision-based object detection is essential for safe and efficient field operation for autonomous agricultural vehicles. However, one of the challenges in transferring state-of-the-art object detectors to the agricultural domain is the limited availability of labeled datasets. This paper seeks to address this challenge by utilizing two object detection models based on YOLOv5, one pre-trained on a large-scale dataset for detecting general classes of objects and one trained to detect a smaller number of agriculture-specific classes. To combine the detections of the models at inference, we propose an ensemble module based on a hierarchical structure of classes. Results show that applying the proposed ensemble module increases [email protected] from

0.575

0.65

on the test dataset and reduces the misclassification of similar classes detected by different models. Furthermore, by translating detections from base classes to a higher level in the class hierarchy, we can increase the overall [email protected] to

0.701

at the cost of reducing class granularity. Full article

(This article belongs to the Special Issue Machine Learning and Sensors Technology in Agriculture)

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22 pages, 3411 KiB

Open AccessArticle

Proposal of a New System for Essential Oil Classification Based on Low-Cost Gas Sensor and Machine Learning Techniques

by Sandra Viciano-Tudela, Lorena Parra, Paula Navarro-Garcia, Sandra Sendra and Jaime Lloret

Sensors 2023, 23(13), 5812; https://doi.org/10.3390/s23135812 - 22 Jun 2023

Cited by 5 | Viewed by 2191

Abstract

Essential oils are valuable in various industries, but their easy adulteration can cause adverse health effects. Electronic nasal sensors offer a solution for adulteration detection. This article proposes a new system for characterising essential oils based on low-cost sensor networks and machine learning techniques. The sensors used belong to the MQ family (MQ-2, MQ-3, MQ-4, MQ-5, MQ-6, MQ-7, and MQ-8). Six essential oils were used, including Cistus ladanifer, Pinus pinaster, and Cistus ladanifer oil adulterated with Pinus pinaster, Melaleuca alternifolia, tea tree, and red fruits. A total of up to 7100 measurements were included, with more than 118 h of measurements of 33 different parameters. These data were used to train and compare five machine learning algorithms: discriminant analysis, support vector machine, k-nearest neighbours, neural network, and naive Bayesian when the data were used individually or when hourly mean values were included. To evaluate the performance of the included machine learning algorithms, accuracy, precision, recall, and F1-score were considered. The study found that using k-nearest neighbours, accuracy, recall, F1-score, and precision values were 1, 0.99, 0.99, and 1, respectively. The accuracy reached 100% with k-nearest neighbours using only 2 parameters for averaged data or 15 parameters for individual data. Full article

(This article belongs to the Special Issue Machine Learning and Sensors Technology in Agriculture)

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20 pages, 3098 KiB

Open AccessArticle

FA-Net: A Fused Feature for Multi-Head Attention Recoding Network for Pear Leaf Nutritional Deficiency Diagnosis with Visual RGB-Image Depth and Shallow Features

by Yi Song, Li Liu, Yuan Rao, Xiaodan Zhang and Xiu Jin

Sensors 2023, 23(9), 4507; https://doi.org/10.3390/s23094507 - 5 May 2023

Cited by 6 | Viewed by 2025

Abstract

Accurate diagnosis of pear tree nutrient deficiency symptoms is vital for the timely adoption of fertilization and treatment. This study proposes a novel method on the fused feature multi-head attention recording network with image depth and shallow feature fusion for diagnosing nutrient deficiency symptoms in pear leaves. First, the shallow features of nutrient-deficient pear leaf images are extracted using manual feature extraction methods, and the depth features are extracted by the deep network model. Second, the shallow features are fused with the depth features using serial fusion. In addition, the fused features are trained using three classification algorithms, F-Net, FC-Net, and FA-Net, proposed in this paper. Finally, we compare the performance of single feature-based and fusion feature-based identification algorithms in the nutrient-deficient pear leaf diagnostic task. The best classification performance is achieved by fusing the depth features output from the ConvNeXt-Base deep network model with shallow features using the proposed FA-Net network, which improved the average accuracy by 15.34 and 10.19 percentage points, respectively, compared with the original ConvNeXt-Base model and the shallow feature-based recognition model. The result can accurately recognize pear leaf deficiency images by providing a theoretical foundation for identifying plant nutrient-deficient leaves. Full article

(This article belongs to the Special Issue Machine Learning and Sensors Technology in Agriculture)

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20 pages, 4404 KiB

Open AccessArticle

Multi-Camera-Based Sorting System for Surface Defects of Apples

by Ju-Hwan Lee, Hoang-Trong Vo, Gyeong-Ju Kwon, Hyoung-Gook Kim and Jin-Young Kim

Sensors 2023, 23(8), 3968; https://doi.org/10.3390/s23083968 - 13 Apr 2023

Cited by 5 | Viewed by 2935

Abstract

In this paper, we addressed the challenges in sorting high-yield apple cultivars that traditionally relied on manual labor or system-based defect detection. Existing single-camera methods failed to uniformly capture the entire surface of apples, potentially leading to misclassification due to defects in unscanned areas. Various methods were proposed where apples were rotated using rollers on a conveyor. However, since the rotation was highly random, it was difficult to scan the apples uniformly for accurate classification. To overcome these limitations, we proposed a multi-camera-based apple sorting system with a rotation mechanism that ensured uniform and accurate surface imaging. The proposed system applied a rotation mechanism to individual apples while simultaneously utilizing three cameras to capture the entire surface of the apples. This method offered the advantage of quickly and uniformly acquiring the entire surface compared to single-camera and random rotation conveyor setups. The images captured by the system were analyzed using a CNN classifier deployed on embedded hardware. To maintain excellent CNN classifier performance while reducing its size and inference time, we employed knowledge distillation techniques. The CNN classifier demonstrated an inference speed of 0.069 s and an accuracy of 93.83% based on 300 apple samples. The integrated system, which included the proposed rotation mechanism and multi-camera setup, took a total of 2.84 s to sort one apple. Our proposed system provided an efficient and precise solution for detecting defects on the entire surface of apples, improving the sorting process with high reliability. Full article

(This article belongs to the Special Issue Machine Learning and Sensors Technology in Agriculture)

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14 pages, 2583 KiB

Open AccessArticle

Development and Analysis of a CNN- and Transfer-Learning-Based Classification Model for Automated Dairy Cow Feeding Behavior Recognition from Accelerometer Data

by Victor Bloch, Lilli Frondelius, Claudia Arcidiacono, Massimo Mancino and Matti Pastell

Sensors 2023, 23(5), 2611; https://doi.org/10.3390/s23052611 - 27 Feb 2023

Cited by 15 | Viewed by 2851

Abstract

Due to technological developments, wearable sensors for monitoring the behavior of farm animals have become cheaper, have a longer lifespan and are more accessible for small farms and researchers. In addition, advancements in deep machine learning methods provide new opportunities for behavior recognition. However, the combination of the new electronics and algorithms are rarely used in PLF, and their possibilities and limitations are not well-studied. In this study, a CNN-based model for the feeding behavior classification of dairy cows was trained, and the training process was analyzed considering a training dataset and the use of transfer learning. Commercial acceleration measuring tags, which were connected by BLE, were fitted to cow collars in a research barn. Based on a dataset including 33.7 cow × days (21 cows recorded during 1–3 days) of labeled data and an additional free-access dataset with similar acceleration data, a classifier with F1 = 93.9% was developed. The optimal classification window size was 90 s. In addition, the influence of the training dataset size on the classifier accuracy was analyzed for different neural networks using the transfer learning technique. While the size of the training dataset was being increased, the rate of the accuracy improvement decreased. Beginning from a specific point, the use of additional training data can be impractical. A relatively high accuracy was achieved with few training data when the classifier was trained using randomly initialized model weights, and a higher accuracy was achieved when transfer learning was used. These findings can be used for the estimation of the necessary dataset size for training neural network classifiers intended for other environments and conditions. Full article

(This article belongs to the Special Issue Machine Learning and Sensors Technology in Agriculture)

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25 pages, 12117 KiB

Open AccessArticle

An Improved CatBoost-Based Classification Model for Ecological Suitability of Blueberries

by Wenfeng Chang, Xiao Wang, Jing Yang and Tao Qin

Sensors 2023, 23(4), 1811; https://doi.org/10.3390/s23041811 - 6 Feb 2023

Cited by 10 | Viewed by 4117

Abstract

Selecting the best planting area for blueberries is an essential issue in agriculture. To better improve the effectiveness of blueberry cultivation, a machine learning-based classification model for blueberry ecological suitability was proposed for the first time and its validation was conducted by using multi-source environmental features data in this paper. The sparrow search algorithm (SSA) was adopted to optimize the CatBoost model and classify the ecological suitability of blueberries based on the selection of data features. Firstly, the Borderline-SMOTE algorithm was used to balance the number of positive and negative samples. The Variance Inflation Factor and information gain methods were applied to filter out the factors affecting the growth of blueberries. Subsequently, the processed data were fed into the CatBoost for training, and the parameters of the CatBoost were optimized to obtain the optimal model using SSA. Finally, the SSA-CatBoost model was adopted to classify the ecological suitability of blueberries and output the suitability types. Taking a study on a blueberry plantation in Majiang County, Guizhou Province, China as an example, the findings demonstrate that the AUC value of the SSA-CatBoost-based blueberry ecological suitability model is 0.921, which is 2.68% higher than that of the CatBoost (AUC = 0.897) and is significantly higher than Logistic Regression (AUC = 0.855), Support Vector Machine (AUC = 0.864), and Random Forest (AUC = 0.875). Furthermore, the ecological suitability of blueberries in Majiang County is mapped according to the classification results of different models. When comparing the actual blueberry cultivation situation in Majiang County, the classification results of the SSA-CatBoost model proposed in this paper matches best with the real blueberry cultivation situation in Majiang County, which is of a high reference value for the selection of blueberry cultivation sites. Full article

(This article belongs to the Special Issue Machine Learning and Sensors Technology in Agriculture)

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Review

Jump to: Research

20 pages, 832 KiB

Open AccessReview

A Review of Federated Learning in Agriculture

by Krista Rizman Žalik and Mitja Žalik

Sensors 2023, 23(23), 9566; https://doi.org/10.3390/s23239566 - 2 Dec 2023

Cited by 9 | Viewed by 3185

Abstract

Federated learning (FL), with the aim of training machine learning models using data and computational resources on edge devices without sharing raw local data, is essential for improving agricultural management and smart agriculture. This study is a review of FL applications that address various agricultural problems. We compare the types of data partitioning and types of FL (horizontal partitioning and horizontal FL, vertical partitioning and vertical FL, and hybrid partitioning and transfer FL), architectures (centralized and decentralized), levels of federation (cross-device and cross-silo), and the use of aggregation algorithms in different reviewed approaches and applications of FL in agriculture. We also briefly review how the communication challenge is solved by different approaches. This work is useful for gaining an overview of the FL techniques used in agriculture and the progress made in this field. Full article

(This article belongs to the Special Issue Machine Learning and Sensors Technology in Agriculture)

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