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School of Engineering and Technology, CQUniversity Brisbane, 160 Ann St., Brisbane City, QLD 4000, Australia Interests: artificial intelligence; pattern recognition; computer vision; machine learning; computational science; data science; digital agriculture; agroinformatics Special Issues, Collections and Topics in MDPI journals
Department of Crop and Soil Science, Oregon State University, Corvallis, OR 97331, USA Interests: precision agriculture; high-throughput phenotyping; unmanned aerial vehicle; remote sensing; machine learning; image processing Special Issues, Collections and Topics in MDPI journals
Department of Population Medicine and Diagnostic Sciences, Cornell University, Ithaca, NY 14850, USA Interests: machine learning; deep learning; precision farming; digital agriculture Special Issues, Collections and Topics in MDPI journals
Responding to an era marked by the relentless pursuit of innovation and sustainability in farming practices, this collection of articles delves into the transformative potential of artificial intelligence, specifically machine learning and deep learning techniques, in revolutionizing the agricultural landscape.
In today's world, where the global population continues to rise and climate change presents increasingly complex challenges, agriculture stands at a crossroads. It must meet the growing demand for food while mitigating its environmental footprint. The emergence of smart farming and smart agriculture, driven by machine learning, holds immense promise in achieving this delicate balance.
Machine learning, with its ability to process vast datasets and uncover hidden patterns, enables us to make sense of the intricate web of factors that affect agricultural production. Whether predicting crop yields with unprecedented accuracy, identifying and managing pest infestations, optimizing resource allocation, or enhancing the breeding of resilient crops, machine learning empowers us to make informed decisions that drive efficiency, sustainability, and profitability in agriculture.
We are particularly excited about the diverse array of topics covered in this Special Issue. We welcome contributions that encompass smart farming, precision agriculture, and data-driven solutions across the agricultural spectrum. Our contributors include esteemed researchers and practitioners from around the globe, each offering valuable insights into the dynamic field of advanced machine learning in agriculture.
Dr. Paul Kwan Dr. Jing Zhou Dr. Beibei Xu Guest Editors
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Soil fertility is a critical factor in agricultural production, directly impacting crop growth, yield, and quality. To achieve precise agricultural management, accurate spatial interpolation of soil parameters is essential. This study developed a new interpolation prediction framework that combines Radial Basis Function (RBF)
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Soil fertility is a critical factor in agricultural production, directly impacting crop growth, yield, and quality. To achieve precise agricultural management, accurate spatial interpolation of soil parameters is essential. This study developed a new interpolation prediction framework that combines Radial Basis Function (RBF) neural networks with Inverse Distance Weighting (IDW), termed the IDW-RBFNN. This framework initially uses the IDW method to apply preliminary weights based on distance to the data points, which are then used as input for the RBF neural network to form a training dataset. Subsequently, the RBF neural network further trains on these data to refine the interpolation results, achieving more precise spatial data interpolation. We compared the interpolation prediction accuracy of the IDW-RBFNN framework with ordinary Kriging (OK) and RBF methods under three different parameter settings. Ultimately, the IDW-RBFNN demonstrated lower error rates in terms of RMSE and MRE compared to direct RBF interpolation methods when adjusting settings based on different power values, even with a fixed number of data samples. As the sample size decreases, the interpolation accuracy of OK and RBF methods is significantly affected, while the error of IDW-RBFNN remains relatively low. Considering both interpolation accuracy and resource limitations, we recommend using the IDW-RBFNN method (p = 2) with at least 60 samples as the minimum sampling density to ensure high interpolation accuracy under resource constraints. Our method overcomes limitations of existing approaches that use fixed steady-state distance decay parameters, providing an effective tool for soil fertility monitoring in delta regions.
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Rice spikelet size, spikelet length and spikelet width, are very important traits directly related to a rice crop’s yield. The accurate measurement of these parameters is quite significant in research such as breeding, yield evaluation and variety improvement for rice crops. Traditional measurement
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Rice spikelet size, spikelet length and spikelet width, are very important traits directly related to a rice crop’s yield. The accurate measurement of these parameters is quite significant in research such as breeding, yield evaluation and variety improvement for rice crops. Traditional measurement methods still mainly rely on manual labor, which is time-consuming, labor-intensive and error-prone. In this study, a novel method, dubbed the “SSM-Method”, based on convolutional neural network and traditional image processing technology has been developed for the efficient and precise measurement of rice spikelet size parameters on rice panicle structures. Firstly, primary branch images of rice panicles were collected at the same height to build an image database. The spikelet detection model using convolutional neural network was then established for spikelet recognition and localization. Subsequently, the calibration value was obtained through traditional image processing technology. Finally, the “SSM-Method” integrated with a spikelet detection model and calibration value was developed for the automatic measurement of spikelet sizes. The performance of the developed SSM-Method was evaluated through testing 60 primary branch images. The test results showed that the root mean square error (RMSE) of spikelet length for two rice varieties (Huahang15 and Qingyang) were 0.26 mm and 0.30 mm, respectively, while the corresponding RMSE of spikelet width was 0.27 mm and 0.31 mm, respectively. The proposed algorithm can provide an effective, convenient and low-cost tool for yield evaluation and breeding research.
Full article
Demand and market value for pennywort largely depend on the quality of the leaves, which can be affected by various ambient environment or fertigation variables during cultivation. Although early detection of defects in pennywort leaves would enable growers to take quick action, conventional
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Demand and market value for pennywort largely depend on the quality of the leaves, which can be affected by various ambient environment or fertigation variables during cultivation. Although early detection of defects in pennywort leaves would enable growers to take quick action, conventional manual detection is laborious and time consuming as well as subjective. Therefore, the objective of this study was to develop an automatic leaf defect detection algorithm for pennywort plants grown under controlled environment conditions, using machine vision and deep learning techniques. Leaf images were captured from pennywort plants grown in an ebb-and-flow hydroponic system under fluorescent light conditions in a controlled plant factory environment. Physically or biologically damaged leaves (e.g., curled, creased, discolored, misshapen, or brown spotted) were classified as defective leaves. Images were annotated using an online tool, and Mask R-CNN models were implemented with the integrated attention mechanisms, convolutional block attention module (CBAM) and coordinate attention (CA) and compared for improved image feature extraction. Transfer learning was employed to train the model with a smaller dataset, effectively reducing processing time. The improved models demonstrated significant advancements in accuracy and precision, with the CA-augmented model achieving the highest metrics, including a mean average precision (mAP) of 0.931 and an accuracy of 0.937. These enhancements enabled more precise localization and classification of leaf defects, outperforming the baseline Mask R-CNN model in complex visual recognition tasks. The final model was robust, effectively distinguishing defective leaves in challenging scenarios, making it highly suitable for applications in precision agriculture. Future research can build on this modeling framework, exploring additional variables to identify specific leaf abnormalities at earlier growth stages, which is crucial for production quality assurance.
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Onion (Allium cepa) is a major field vegetable in South Korea and has been produced for a long time along with cabbage, radish, garlic, and dried peppers. However, as field vegetables, including onions, have recently been imported at low prices, the
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Onion (Allium cepa) is a major field vegetable in South Korea and has been produced for a long time along with cabbage, radish, garlic, and dried peppers. However, as field vegetables, including onions, have recently been imported at low prices, the profitability of onion production in South Korea is beginning to be at risk. In order to maximize farmers’ profits through onion production, this study develops onion yield prediction models via an ensemble learning-based framework involving linear regression, polynomial regression, support vector regression, decision tree, ridge regression, and lasso regression. The use of nitrogen fertilizers is considered an independent variable in the development of the yield prediction model. This is because the use of nitrogen fertilizers accounts for the highest production cost (13.47%) after labor cost (41.21%) and seed cost (17.42%), and it also directly affects onions yields. For the model development, five research datasets on changes in onion yield according to changes in the use of existing nitrogen fertilizers were used. In addition, a non-linear optimization model was devised using onion yield prediction models for the profit maximization of onion production. As a result, the developed non-linear optimization model using polynomial regression enables an increase in profits from onion production by 67.28%.
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Environmental factors such as temperature, humidity, light, and CO2 influence plant growth, and unfavorable environmental conditions cause stress in plants, producing symptoms in their early growth stages. The increasing importance of optimizing crop management strategies has led to a rising demand for
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Environmental factors such as temperature, humidity, light, and CO2 influence plant growth, and unfavorable environmental conditions cause stress in plants, producing symptoms in their early growth stages. The increasing importance of optimizing crop management strategies has led to a rising demand for the precise evaluation of stress symptoms during early plant growth. Advanced technologies are transforming plant health monitoring through enabling image-based stress analysis. Machine learning (ML) models can effectively identify the important features and morphological changes connected with various stress conditions through the use of large datasets acquired from high-resolution plant images. Therefore, the objective of this study was to develop a method for classifying the early-stage stress symptoms of pepper seedlings and enabling their identification and quantification using image processing and a support vector machine (SVM). Two-week-old pepper seedlings were grown under different temperatures (20, 25, and 30 °C), light intensity levels (50, 250, and 450 µmol m−2s−1), and day–night hours (8/16, 10/14, and 16/8) in five controlled plant growth chambers. Images of the seedling canopies were captured daily using a low-cost red, green, and blue (RGB) camera over a two-week period. Eighteen color features, nine texture features using the gray-level co-occurrence matrix (GLCM), and one morphological feature were extracted from each image. A two-way ANOVA and multiple mean comparison (Duncan) analysis were used to determine the statistical significance of the treatment effects. To reduce feature overlap, sequential feature selection (SFS) was applied, and a support vector machine (SVM) was used for stress classification. The SFS method was used to identify the optimal features for the classification model, leading to substantial increases in stress classification accuracy. The SVM model, using these selected features, achieved a classification accuracy of 82% without the SFS and 86% with the SFS. To address overfitting, 5- and 10-fold cross-validation were used, resulting in MAEs of 0.138 and 0.163 for the polynomial kernel, respectively. The SVM model, evaluated with the ROC curve and confusion matrix, achieved a classification accuracy of 85%. This classification approach enables real-time stress monitoring, allowing growers to optimize environmental conditions and enhance seedling growth. Future directions include integrating this system into automated cultivation environments to enable continuous, efficient stress monitoring and response, further improving crop management and productivity.
Full article
The rapid and accurate detection of Chinese flowering cabbage ripeness and the counting of Chinese flowering cabbage are fundamental for timely harvesting, yield prediction, and field management. The complexity of the existing model structures somewhat hinders the application of recognition models in harvesting
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The rapid and accurate detection of Chinese flowering cabbage ripeness and the counting of Chinese flowering cabbage are fundamental for timely harvesting, yield prediction, and field management. The complexity of the existing model structures somewhat hinders the application of recognition models in harvesting machines. Therefore, this paper proposes the lightweight Cabbage-YOLO model. First, the YOLOv8-n feature pyramid structure is adjusted to effectively utilize the target’s spatial structure information as well as compress the model in size. Second, the RVB-EMA module is introduced as a necking optimization mechanism to mitigate the interference of shallow noise in the high-resolution sounding layer and at the same time to reduce the number of parameters in this model. In addition, the head uses an independently designed lightweight PCDetect detection head, which enhances the computational efficiency of the model. Subsequently, the neck utilizes a lightweight DySample upsampling operator to capture and preserve underlying semantic information. Finally, the attention mechanism SimAm is inserted before SPPF for an enhanced ability to capture foreground features. The improved Cabbage-YOLO is integrated with the Byte Tracker to track and count Chinese flowering cabbage in video sequences. The average detection accuracy of Cabbage-YOLO can reach 86.4%. Compared with the original model YOLOv8-n, its FLOPs, the its number of parameters, and the size of its weights are decreased by about 35.9%, 47.2%, and 45.2%, respectively, and its average detection precision is improved by 1.9% with an FPS of 107.8. In addition, the integrated Cabbage-YOLO with the Byte Tracker can also effectively track and count the detected objects. The Cabbage-YOLO model boasts higher accuracy, smaller size, and a clear advantage in lightweight deployment. Overall, the improved lightweight model can provide effective technical support for promoting intelligent management and harvesting decisions of Chinese flowering cabbage.
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Pest damage is a major factor in reducing crop yield and has negative impacts on the economy. However, the complex background, diversity of pests, and individual differences pose challenges for classification algorithms. In this study, we propose a patch-based neural network (PMLPNet) for
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Pest damage is a major factor in reducing crop yield and has negative impacts on the economy. However, the complex background, diversity of pests, and individual differences pose challenges for classification algorithms. In this study, we propose a patch-based neural network (PMLPNet) for multi-class pest classification. PMLPNet leverages spatial and channel contextual semantic features through meticulously designed token- and channel-mixing MLPs, respectively. This innovative structure enhances the model’s ability to accurately classify complex multi-class pests by providing high-quality local and global pixel semantic features for the fully connected layer and activation function. We constructed a database of 4510 images spanning 40 types of plant pests across 4 crops. Experimental results demonstrate that PMLPNet outperforms existing CNN models, achieving an accuracy of 92.73%. Additionally, heat maps reveal distinctions among different pest images, while patch probability-based visualizations highlight heterogeneity within pest images. Validation on external datasets (IP102 and PlantDoc) confirms the robust generalization performance of PMLPNet. In summary, our research advances intelligent pest classification techniques, effectively identifying various pest types in diverse crop images.
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Due to the short time, high labor intensity and high workload of fruit and vegetable harvesting, robotic harvesting instead of manual operations is the future. The accuracy of object detection and location is directly related to the picking efficiency, quality and speed of
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Due to the short time, high labor intensity and high workload of fruit and vegetable harvesting, robotic harvesting instead of manual operations is the future. The accuracy of object detection and location is directly related to the picking efficiency, quality and speed of fruit-harvesting robots. Because of its low recognition accuracy, slow recognition speed and poor localization accuracy, the traditional algorithm cannot meet the requirements of automatic-harvesting robots. The increasingly evolving and powerful deep learning technology can effectively solve the above problems and has been widely used in the last few years. This work systematically summarizes and analyzes about 120 related literatures on the object detection and three-dimensional positioning algorithms of harvesting robots over the last 10 years, and reviews several significant methods. The difficulties and challenges faced by current fruit detection and localization algorithms are proposed from the aspects of the lack of large-scale high-quality datasets, the high complexity of the agricultural environment, etc. In response to the above challenges, corresponding solutions and future development trends are constructively proposed. Future research and technological development should first solve these current challenges using weakly supervised learning, efficient and lightweight model construction, multisensor fusion and so on.
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