Pub Date : 2024-11-15DOI: 10.1016/j.compag.2024.109617
J.P. Vásconez , I.N. Vásconez , V. Moya , M.J. Calderón-Díaz , M. Valenzuela , X. Besoain , M. Seeger , F. Auat Cheein
Classification of plant diseases based on computer vision is a multidisciplinary challenge that involves technical and data-related complexities. Artificial Intelligence (AI) has increasingly found its application in plant pathology, disease, and anomaly visual characterization. Specifically, Machine Learning (ML) and Deep Learning (DL) algorithms have proven to be highly effective for tasks such as plant disease classification, detection, diagnosis, and management. In this work, we present a comparative analysis of multiple DL models based on Convolutional Neural Networks (CNNs) for visual symptoms classification of the phytopathogen Ralstonia solanacearum in tomato plants. We demonstrate that by implementing DL classification algorithms based on CNNs, it is possible to identify Ralstonia solanacearum potentially affected plants. This was possible due to the main virulence factor of Ralstonia solanacearum, the exopolysaccharide (EPS), which obstructs the plant’s xylem limiting water absorption and consequently inducing visual wilting symptoms. For this, we implemented, trained, and evaluated fourteen different CNN-based models. We evaluated the models by using different metrics such as precision, recall, accuracy, specificity, and F1-score. The models that obtained the best accuracy results were MobileNet-v2 and Xception, with an accuracy of 97.7% for both models. The presented findings significantly contribute to the visual symptoms classification of Ralstonia solanacearum in tomato plants, which may contribute to the control of this disease and its spread to healthy crops or other susceptible hosts in the future.
{"title":"Deep learning-based classification of visual symptoms of bacterial wilt disease caused by Ralstonia solanacearum in tomato plants","authors":"J.P. Vásconez , I.N. Vásconez , V. Moya , M.J. Calderón-Díaz , M. Valenzuela , X. Besoain , M. Seeger , F. Auat Cheein","doi":"10.1016/j.compag.2024.109617","DOIUrl":"10.1016/j.compag.2024.109617","url":null,"abstract":"<div><div>Classification of plant diseases based on computer vision is a multidisciplinary challenge that involves technical and data-related complexities. Artificial Intelligence (AI) has increasingly found its application in plant pathology, disease, and anomaly visual characterization. Specifically, Machine Learning (ML) and Deep Learning (DL) algorithms have proven to be highly effective for tasks such as plant disease classification, detection, diagnosis, and management. In this work, we present a comparative analysis of multiple DL models based on Convolutional Neural Networks (CNNs) for visual symptoms classification of the phytopathogen <em>Ralstonia solanacearum</em> in tomato plants. We demonstrate that by implementing DL classification algorithms based on CNNs, it is possible to identify <em>Ralstonia solanacearum</em> potentially affected plants. This was possible due to the main virulence factor of <em>Ralstonia solanacearum</em>, the exopolysaccharide (EPS), which obstructs the plant’s xylem limiting water absorption and consequently inducing visual wilting symptoms. For this, we implemented, trained, and evaluated fourteen different CNN-based models. We evaluated the models by using different metrics such as precision, recall, accuracy, specificity, and F1-score. The models that obtained the best accuracy results were MobileNet-v2 and Xception, with an accuracy of 97.7% for both models. The presented findings significantly contribute to the visual symptoms classification of <em>Ralstonia solanacearum</em> in tomato plants, which may contribute to the control of this disease and its spread to healthy crops or other susceptible hosts in the future.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109617"},"PeriodicalIF":7.7,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fusarium head blight (FHB) is a severe disease, with implications for both crop quality and safety. The inability to accurately and rapidly determine diseases severity has resulted in increasing grain loss and the pesticide expenses. Furthermore, the complexity of many current models presents challenges in their deployment and utilization. Thus, this study introduces an improved lightweight model for efficient and rapid assessment of FHB severity. Firstly, we collected 2650 wheat images with different severities in natural environments. Second, we refined and compressed RepGhostNet, replacing the original ReLU function with LeakyReLU and using the AdamW optimizer during training to enhance model accuracy. Third, using the strategy of masked generative distillation, we further improved the accuracy of SlimRepGhostNet while ensuring model lightweight. The MGD-SlimRepGhostNet achieved an accuracy of 94.58% and a frames per second (FPS) of 152.17. This represents a 4.34% increase in accuracy and a 21.17 increase in speed compared to the original RepGhostNet. Lastly, we have designed a WeChat mini program that achieves the performance of MGD-SlimRepGhostNet in real environments, highlighting its practicality. The proposed method effectively addresses the inaccuracies and labor-intensive associated with nature of traditional visual assessment methods deployed for evaluating FHB severity in wheat, while its rapid inference capability renders it highly suitable for deployment and application on mobile devices.
{"title":"Integrating masked generative distillation and network compression to identify the severity of wheat fusarium head blight","authors":"Zheng Gong, Chunfeng Gao, Zhihui Feng, Ping Dong, Hongbo Qiao, Hui Zhang, Lei Shi, Wei Guo","doi":"10.1016/j.compag.2024.109647","DOIUrl":"10.1016/j.compag.2024.109647","url":null,"abstract":"<div><div>Fusarium head blight (FHB) is a severe disease, with implications for both crop quality and safety. The inability to accurately and rapidly determine diseases severity has resulted in increasing grain loss and the pesticide expenses. Furthermore, the complexity of many current models presents challenges in their deployment and utilization. Thus, this study introduces an improved lightweight model for efficient and rapid assessment of FHB severity. Firstly, we collected 2650 wheat images with different severities in natural environments. Second, we refined and compressed RepGhostNet, replacing the original ReLU function with LeakyReLU and using the AdamW optimizer during training to enhance model accuracy. Third, using the strategy of masked generative distillation, we further improved the accuracy of SlimRepGhostNet while ensuring model lightweight. The MGD-SlimRepGhostNet achieved an accuracy of 94.58% and a frames per second (FPS) of 152.17. This represents a 4.34% increase in accuracy and a 21.17 increase in speed compared to the original RepGhostNet. Lastly, we have designed a WeChat mini program that achieves the performance of MGD-SlimRepGhostNet in real environments, highlighting its practicality. The proposed method effectively addresses the inaccuracies and labor-intensive associated with nature of traditional visual assessment methods deployed for evaluating FHB severity in wheat, while its rapid inference capability renders it highly suitable for deployment and application on mobile devices.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109647"},"PeriodicalIF":7.7,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-14DOI: 10.1016/j.compag.2024.109586
Kyle Lammers , Kaixiang Zhang , Keyi Zhu , Pengyu Chu , Zhaojian Li , Renfu Lu
Harvesting labor is the single largest cost in apple production in the U.S. Increased cost and growing shortage of labor has forced the apple industry to seek automated harvesting solutions. Despite considerable progress in recent years, the existing robotic harvesting systems still fall short of performance expectations, lacking robustness and proving inefficient or overly complex for practical commercial deployment. In this paper, we present the development and evaluation of a new dual-arm robotic apple harvesting system. The system hardware mainly consists of a perception component, two four-degree-of-freedom manipulators, a centralized vacuum system, and a fruit handling and bin filling component designed for the collection and transportation of picked fruits. Synergistic functionalities for automated apple harvesting were achieved through the development of software algorithms. In particular, an updated perception system based on dual-laser scanning was proposed to enable sequential localization of apples for the dual-arm robotic system. A sophisticated planning scheme was devised to coordinate the movement of the two manipulators, allowing them to approach the fruit effectively and share a centralized vacuum system for efficient fruit detachment. The robotic system has been evaluated through field trials in a challenging apple orchard with complex, dense canopy, and it achieved 60% successful picking rate. The dual-arm coordination algorithm resulted in 9% to 34% harvest time improvements, compared to the 1-arm robotic system design. The new dual-arm robotic system is compact in design and dexterous in movement, and with further improvements in hardware and software, it holds great potential for providing a commercially viable harvesting automation solution for the apple industry
{"title":"Development and evaluation of a dual-arm robotic apple harvesting system","authors":"Kyle Lammers , Kaixiang Zhang , Keyi Zhu , Pengyu Chu , Zhaojian Li , Renfu Lu","doi":"10.1016/j.compag.2024.109586","DOIUrl":"10.1016/j.compag.2024.109586","url":null,"abstract":"<div><div>Harvesting labor is the single largest cost in apple production in the U.S. Increased cost and growing shortage of labor has forced the apple industry to seek automated harvesting solutions. Despite considerable progress in recent years, the existing robotic harvesting systems still fall short of performance expectations, lacking robustness and proving inefficient or overly complex for practical commercial deployment. In this paper, we present the development and evaluation of a new dual-arm robotic apple harvesting system. The system hardware mainly consists of a perception component, two four-degree-of-freedom manipulators, a centralized vacuum system, and a fruit handling and bin filling component designed for the collection and transportation of picked fruits. Synergistic functionalities for automated apple harvesting were achieved through the development of software algorithms. In particular, an updated perception system based on dual-laser scanning was proposed to enable sequential localization of apples for the dual-arm robotic system. A sophisticated planning scheme was devised to coordinate the movement of the two manipulators, allowing them to approach the fruit effectively and share a centralized vacuum system for efficient fruit detachment. The robotic system has been evaluated through field trials in a challenging apple orchard with complex, dense canopy, and it achieved 60% successful picking rate. The dual-arm coordination algorithm resulted in 9% to 34% harvest time improvements, compared to the 1-arm robotic system design. The new dual-arm robotic system is compact in design and dexterous in movement, and with further improvements in hardware and software, it holds great potential for providing a commercially viable harvesting automation solution for the apple industry</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109586"},"PeriodicalIF":7.7,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-14DOI: 10.1016/j.compag.2024.109634
Carlos J. Cortés , Nelson O. Moraga , Constanza Jana , Germán E. Merino
Experiments and computational modeling were developed to determine the effect of different types of hydraulic configurations on water quality variables to improve growth of lettuce in hydroponic beds. The variants in the hydraulic configurations consider water recirculation in hydroponic modules using Deep Water Culture technique (DWC), for continuous (CWF) and pulsatile water flow (PWF) using either one or three water flow inlets (TWF). These data were used to generate fluid mechanics and heat transfer models for the described hydraulic configurations to assess the effect of the hydraulic configuration on lettuce growth. The results obtained from the mathematical model by the finite volume method allowed to explain the influence of water flow and temperature on the rate of growing for lettuce during summer and autumn in the southern hemisphere. The main findings obtained from the hybrid numerical – experimental model to achieve high lettuce yield were that the number of water inlets has an effect on influenced nutrient transport and water quality variation, where the variant with three water inlets (TWF), and the climatic condition for autumn achieve better plant growth performance than summer. Computational modelling of fluid mechanics and heat transfer allowed to predict the variation of water quality variables in DWC bed, being a suitable technique with a high potential for achieving new accurate agriculture standards.
{"title":"Effect of hydraulic configuration on lettuce growth in hydroponic bed using Deep water culture technique (DWC)","authors":"Carlos J. Cortés , Nelson O. Moraga , Constanza Jana , Germán E. Merino","doi":"10.1016/j.compag.2024.109634","DOIUrl":"10.1016/j.compag.2024.109634","url":null,"abstract":"<div><div>Experiments and computational modeling were developed to determine the effect of different types of hydraulic configurations on water quality variables to improve growth of lettuce in hydroponic beds. The variants in the hydraulic configurations consider water recirculation in hydroponic modules using Deep Water Culture technique (DWC), for continuous (CWF) and pulsatile water flow (PWF) using either one or three water flow inlets (TWF). These data were used to generate fluid mechanics and heat transfer models for the described hydraulic configurations to assess the effect of the hydraulic configuration on lettuce growth. The results obtained from the mathematical model by the finite volume method allowed to explain the influence of water flow and temperature on the rate of growing for lettuce during summer and autumn in the southern hemisphere. The main findings obtained from the hybrid numerical – experimental model to achieve high lettuce yield were that the number of water inlets has an effect on influenced nutrient transport and water quality variation, where the variant with three water inlets (TWF), and the climatic condition for autumn achieve better plant growth performance than summer. Computational modelling of fluid mechanics and heat transfer allowed to predict the variation of water quality variables in DWC bed, being a suitable technique with a high potential for achieving new accurate agriculture standards.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109634"},"PeriodicalIF":7.7,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-14DOI: 10.1016/j.compag.2024.109636
Yi-Chen Chen , Jen-Cheng Wang , Mu-Hwa Lee , An-Chi Liu , Joe-Air Jiang
Mango trees affected by various diseases often exhibit distinctive leaf symptoms. Accurate and timely diagnosis is crucial for mango cultivation. Deep learning algorithms provide a viable solution for precisely detection of mango leaf diseases. However, two main challenges exist: environmental interference and the difficulty of collecting leaf image data from the field. To address these challenges, this study introduces a multi-scale and multi-pooling convolutional neural network (MSMP-CNN) model. The proposed model undergoes a pre-training phase, followed by transfer learning and fine-tuning, and ultimately focuses on identifying mango leaf diseases using real-world images. This model exhibits outstanding performance in identifying various mango leaf diseases. The model achieved an accuracy of 95 % on its own. After being enhanced by transfer learning and find-tuning, the model achieved an impressive accuracy of 98.5 %. To compare the classification performance with and without transfer learning and fine-tuning, t-distributed stochastic neighbor embedding (t-SNE) plots were used. Class activation mapping (CAM) heatmaps were also utilized to highlight class-specific regions of images, helping verify whether the model focused on the appropriate parts of the image for disease identification. These findings underscore the strong potential of the model combining with transfer learning and fine-tuning to advance mango leaf disease detection. In the future, the proposed model will evolve into a real-time, precise diagnostic system for mango leaf diseases, thereby transforming mango cultivation management from precision farming to smart agriculture.
受各种病害影响的芒果树通常会表现出独特的叶片症状。准确及时的诊断对芒果种植至关重要。深度学习算法为精确检测芒果叶片疾病提供了可行的解决方案。然而，目前存在两大挑战：环境干扰和从田间收集叶片图像数据的难度。为应对这些挑战，本研究引入了多尺度和多池化卷积神经网络（MSMP-CNN）模型。该模型经过预训练阶段、迁移学习阶段和微调阶段，最终专注于利用真实世界的图像识别芒果叶病。该模型在识别各种芒果叶病方面表现出色。模型本身的准确率达到 95%。在经过迁移学习和查找调整增强后，该模型的准确率达到了令人印象深刻的 98.5%。为了比较有无迁移学习和微调的分类性能，使用了 t 分布随机邻域嵌入（t-SNE）图。此外，还使用了类激活图谱（CAM）热图来突出图像的特定类区域，以帮助验证模型是否侧重于图像的适当部分进行疾病识别。这些发现凸显了该模型与迁移学习和微调相结合在推进芒果叶病害检测方面的强大潜力。未来，该模型将发展成为一个实时、精确的芒果叶病诊断系统，从而将芒果种植管理从精准农业转变为智慧农业。
{"title":"Enhanced detection of mango leaf diseases in field environments using MSMP-CNN and transfer learning","authors":"Yi-Chen Chen , Jen-Cheng Wang , Mu-Hwa Lee , An-Chi Liu , Joe-Air Jiang","doi":"10.1016/j.compag.2024.109636","DOIUrl":"10.1016/j.compag.2024.109636","url":null,"abstract":"<div><div>Mango trees affected by various diseases often exhibit distinctive leaf symptoms. Accurate and timely diagnosis is crucial for mango cultivation. Deep learning algorithms provide a viable solution for precisely detection of mango leaf diseases. However, two main challenges exist: environmental interference and the difficulty of collecting leaf image data from the field. To address these challenges, this study introduces a multi-scale and multi-pooling convolutional neural network (MSMP-CNN) model. The proposed model undergoes a pre-training phase, followed by transfer learning and fine-tuning, and ultimately focuses on identifying mango leaf diseases using real-world images. This model exhibits outstanding performance in identifying various mango leaf diseases. The model achieved an accuracy of 95 % on its own. After being enhanced by transfer learning and find-tuning, the model achieved an impressive accuracy of 98.5 %. To compare the classification performance with and without transfer learning and fine-tuning, t-distributed stochastic neighbor embedding (t-SNE) plots were used. Class activation mapping (CAM) heatmaps were also utilized to highlight class-specific regions of images, helping verify whether the model focused on the appropriate parts of the image for disease identification. These findings underscore the strong potential of the model combining with transfer learning and fine-tuning to advance mango leaf disease detection. In the future, the proposed model will evolve into a real-time, precise diagnostic system for mango leaf diseases, thereby transforming mango cultivation management from precision farming to smart agriculture.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109636"},"PeriodicalIF":7.7,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-13DOI: 10.1016/j.compag.2024.109618
Abdullah Alamoodi , Salem Garfan , Muhammet Deveci , O.S. Albahri , A.S. Albahri , Salman Yussof , Raad Z. Homod , Iman Mohamad Sharaf , Sarbast Moslem
Agriculture 4.0 plays a crucial role in shaping sustainable cities and societies by revolutionizing urban food systems. By incorporating advanced technologies like precision farming, vertical gardening, and data analytics, Agriculture 4.0 improves local food production, reduces food transportation, and optimizes resource utilization. This paper introduces an innovative approach using Multi-Criteria Decision Making (MCDM) to assess Agriculture 4.0 Decision Support Systems (ADSS), contributing significantly to the selection of optimal systems that can drive sustainability in smart agriculture. The novelty of this research lies in developing a comprehensive evaluation framework that extends the hyperbolic fuzzy-weighted zero-inconsistency method for criteria weighting, combined with the combinative distance-based assessment method for benchmarking ADSS. The assessment matrix evaluates 13 ADSS across eight key criteria, including “accessibility,” “re-planning,” “expert knowledge,” “interoperability,” “scalability,” “uncertainty and dynamic factors,” “prediction and forecast,” and “historical data analysis”. Results from the hyperbolic fuzzy-weighted zero-inconsistency approach highlight “re-planning” (0.143) and “prediction and forecast” (0.140) as the most significant criteria, while “expert knowledge” ranked lowest (0.113). In the combinative distance-based assessment, the system labelled “OCCASION” achieved the highest score (3.843), positioning it as the most favourable ADSS, whereas the “MOLP-based beef supply chain” system scored lowest (−3.519). Sensitivity analysis, conducted using varying sets of weights, confirms the robustness and reliability of the proposed approach. This research provides a powerful decision-making tool that can guide stakeholders in selecting the best ADSS, ultimately promoting sustainability and resource optimization in Agriculture 4.0. The findings have important implications for farmers, agribusiness, and smart agriculture, demonstrating the potential of the methodology to enhance decision-making processes in a critical sector.
{"title":"Evaluating agriculture 4.0 decision support systems based on hyperbolic fuzzy-weighted zero-inconsistency combined with combinative distance-based assessment","authors":"Abdullah Alamoodi , Salem Garfan , Muhammet Deveci , O.S. Albahri , A.S. Albahri , Salman Yussof , Raad Z. Homod , Iman Mohamad Sharaf , Sarbast Moslem","doi":"10.1016/j.compag.2024.109618","DOIUrl":"10.1016/j.compag.2024.109618","url":null,"abstract":"<div><div>Agriculture 4.0 plays a crucial role in shaping sustainable cities and societies by revolutionizing urban food systems. By incorporating advanced technologies like precision farming, vertical gardening, and data analytics, Agriculture 4.0 improves local food production, reduces food transportation, and optimizes resource utilization. This paper introduces an innovative approach using Multi-Criteria Decision Making (MCDM) to assess Agriculture 4.0 Decision Support Systems (ADSS), contributing significantly to the selection of optimal systems that can drive sustainability in smart agriculture. The novelty of this research lies in developing a comprehensive evaluation framework that extends the hyperbolic fuzzy-weighted zero-inconsistency method for criteria weighting, combined with the combinative distance-based assessment method for benchmarking ADSS. The assessment matrix evaluates 13 ADSS across eight key criteria, including “<em>accessibility</em>,” “<em>re-planning</em>,” “<em>expert knowledge</em>,” “<em>interoperability</em>,” “<em>scalability</em>,” “<em>uncertainty and dynamic factors</em>,” “<em>prediction and forecast</em>,” and “<em>historical data analysis</em>”. Results from the hyperbolic fuzzy-weighted zero-inconsistency approach highlight “<em>re-planning</em>” (<em>0.143</em>) and “<em>prediction and forecast</em>” (<em>0.140</em>) as the most significant criteria, while “<em>expert knowledge</em>” ranked lowest (<em>0.113</em>). In the combinative distance-based assessment, the system labelled “OCCASION” achieved the highest score (<em>3.843</em>), positioning it as the most favourable ADSS, whereas the “MOLP-based beef supply chain” system scored lowest <em>(−3.519</em>). Sensitivity analysis, conducted using varying sets of weights, confirms the robustness and reliability of the proposed approach. This research provides a powerful decision-making tool that can guide stakeholders in selecting the best ADSS, ultimately promoting sustainability and resource optimization in Agriculture 4.0. The findings have important implications for farmers, agribusiness, and smart agriculture, demonstrating the potential of the methodology to enhance decision-making processes in a critical sector.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109618"},"PeriodicalIF":7.7,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-13DOI: 10.1016/j.compag.2024.109650
Jia-Yong Song , Ze-Sheng Qin , Chang-Wen Xue , Li-Feng Bian , Chen Yang
Hyperspectral microscopy imaging (HMI) is an efficient and non-destructive method to detect microbial contaminants in food, as it can provide both spatial morphology and spectral signature. Aims at reducing thermal effect, low cost, and improving spectral resolution in testing, a pipeline-operated LEDs monochromatic illumination mode is proposed, which integrates the design concepts of both grating-based and LED-based HMI systems. By design of the LED set, shared grating monochromatic optical path, and coordinated control system, an HMI system has been developed that could obtain the hyperspectral data cube with 101 bands in 400–700 nm. Hyperspectral datasets of three species of Aspergillus are prepared using the prototype, and efficient results have been achieved in the training and testing of classical classification algorithms (1D-CNN (97.33 %), k-NN (96.33 %), SVM (97.67 %) and ResNet-18 (95.67 %)). The results demonstrate that the proposed monochromatic illumination mode and associated system are potential detection solutions for foodborne microbial contaminants with low-cost and high-accurate.
高光谱显微成像（HMI）可提供空间形态和光谱特征，是检测食品中微生物污染物的一种高效、非破坏性方法。为了在检测中减少热效应、降低成本并提高光谱分辨率，我们提出了一种流水线操作的 LED 单色照明模式，它集成了基于光栅和基于 LED 的 HMI 系统的设计理念。通过对 LED 组、共享光栅单色光路和协调控制系统的设计，开发出了一种高光谱人机界面系统，可获得 400-700 nm 范围内 101 个波段的高光谱数据立方体。利用该原型系统制备了三种曲霉菌的高光谱数据集，并在经典分类算法（1D-CNN (97.33 %)、k-NN (96.33 %)、SVM (97.67 %) 和 ResNet-18 (95.67 %)）的训练和测试中取得了高效的结果。结果表明，所提出的单色照明模式和相关系统是低成本、高精度的食源性微生物污染物潜在检测解决方案。
{"title":"A monochrome pipelined HMI system for foodborne microorganisms testing","authors":"Jia-Yong Song , Ze-Sheng Qin , Chang-Wen Xue , Li-Feng Bian , Chen Yang","doi":"10.1016/j.compag.2024.109650","DOIUrl":"10.1016/j.compag.2024.109650","url":null,"abstract":"<div><div>Hyperspectral microscopy imaging (HMI) is an efficient and non-destructive method to detect microbial contaminants in food, as it can provide both spatial morphology and spectral signature. Aims at reducing thermal effect, low cost, and improving spectral resolution in testing, a pipeline-operated LEDs monochromatic illumination mode is proposed, which integrates the design concepts of both grating-based and LED-based HMI systems. By design of the LED set, shared grating monochromatic optical path, and coordinated control system, an HMI system has been developed that could obtain the hyperspectral data cube with 101 bands in 400–700 <em>nm</em>. Hyperspectral datasets of three species of Aspergillus are prepared using the prototype, and efficient results have been achieved in the training and testing of classical classification algorithms (1D-CNN (97.33 %), k-NN (96.33 %), SVM (97.67 %) and ResNet-18 (95.67 %)). The results demonstrate that the proposed monochromatic illumination mode and associated system are potential detection solutions for foodborne microbial contaminants with low-cost and high-accurate.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109650"},"PeriodicalIF":7.7,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-13DOI: 10.1016/j.compag.2024.109581
Yunsong Jia, Li’ao Qu, Shuaiqi Huang, Xin Chen, Xiang Li
Extreme greenhouse temperatures can lead to irreversible damage to crops inside the greenhouse, resulting in yield reduction and even crop failure. Predicting such extreme temperatures and intervening in advance can mitigate the economic losses caused by these conditions. Existing models demonstrate relatively accurate predictions within the normal temperature range of the greenhouse, but they exhibit significant deviations when forecasting extreme temperature intervals, leading to narrow temperature prediction ranges, which hinders their ability to address the aforementioned scenarios effectively. In this paper, we propose a novel approach that combines the weighted idea for handling class imbalance and introduces a loss function suitable for multiple models. By ensuring the accuracy of normal temperature predictions, our proposed method significantly enhances the precision of predicting extreme greenhouse temperatures and expands the model’s temperature prediction range. Experimental results demonstrate the effectiveness of this loss function in various models such as LGB (LightGBM), LSTM (Long Short-Term Memory), and BPNN (Backpropagation Neural Network), leading to a significant reduction in false positive and false negative predictions of extreme temperatures.
{"title":"Better prediction of greenhouse extreme temperature base on improved loss function","authors":"Yunsong Jia, Li’ao Qu, Shuaiqi Huang, Xin Chen, Xiang Li","doi":"10.1016/j.compag.2024.109581","DOIUrl":"10.1016/j.compag.2024.109581","url":null,"abstract":"<div><div>Extreme greenhouse temperatures can lead to irreversible damage to crops inside the greenhouse, resulting in yield reduction and even crop failure. Predicting such extreme temperatures and intervening in advance can mitigate the economic losses caused by these conditions. Existing models demonstrate relatively accurate predictions within the normal temperature range of the greenhouse, but they exhibit significant deviations when forecasting extreme temperature intervals, leading to narrow temperature prediction ranges, which hinders their ability to address the aforementioned scenarios effectively. In this paper, we propose a novel approach that combines the weighted idea for handling class imbalance and introduces a loss function suitable for multiple models. By ensuring the accuracy of normal temperature predictions, our proposed method significantly enhances the precision of predicting extreme greenhouse temperatures and expands the model’s temperature prediction range. Experimental results demonstrate the effectiveness of this loss function in various models such as LGB (LightGBM), LSTM (Long Short-Term Memory), and BPNN (Backpropagation Neural Network), leading to a significant reduction in false positive and false negative predictions of extreme temperatures.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109581"},"PeriodicalIF":7.7,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-13DOI: 10.1016/j.compag.2024.109651
Xinting Ding , Wei Hao , Kui Liu , Binbin Wang , Zhi He , Weixin Li , Yongjie Cui , Qichang Yang
Addressing the limitations of the traditional air suction plug tray seeder regarding versatility, clogging, noise, and energy consumption, a novel plug tray seeding method suitable for a broader range of small seed sizes has been proposed. A universal plug tray seeder has also been designed based on electrostatic adsorption for small seeds. Key factors affecting seed electrostatic adsorption were analyzed through electrostatic simulation, determining the optimal manufacturing method for the suction needle and the best range for the electrostatic voltage. Leveraging the theory of granular dynamics, a seed vibration box was designed using the principle of microphone vibration to enhance seed flowability and reduce the multiple seeding rate. Furthermore, the control system achieved seed recognition based on YOLOv8n and adaptive matching of seeding parameters, enhancing the universality of the seeder. The seeder was optimized and validated through practical experiments, with a comparative analysis of energy consumption and sound intensity conducted. The results indicated that the electrostatic suction needle, made with a single copper electrode of 1 mm diameter and coated with a 1 mm thick planar epoxy resin adsorption layer, along with an electrostatic voltage of 5 ∼ 10 kV, could effectively adsorb seeds. The vibration box significantly improved the seeding effect by vibrating seeds of tomato, pepper, and muskmelon at frequencies of 10 ∼ 25 Hz, and seeds of broccoli, cabbage, and eggplant at frequencies of 30 ∼ 50 Hz. The combined action of the electrostatic suction needle and the vibrating seed box resulted in an 83.20 % reduction in energy consumption and a significant decrease in sound intensity. Although the single seeding rate for muskmelon and cabbage seeds slightly decreased due to higher rates of leakage seeding and multiple seeding, the single seeding rate for other seeds remained around 90 %. This study provides a theoretical foundation for the universal seeding method of small seeds and offers significant reference value for the design of low-energy, low-noise plug tray seeders.
{"title":"Development of a universal plug tray seeder for small seeds based on electrostatic adsorption","authors":"Xinting Ding , Wei Hao , Kui Liu , Binbin Wang , Zhi He , Weixin Li , Yongjie Cui , Qichang Yang","doi":"10.1016/j.compag.2024.109651","DOIUrl":"10.1016/j.compag.2024.109651","url":null,"abstract":"<div><div>Addressing the limitations of the traditional air suction plug tray seeder regarding versatility, clogging, noise, and energy consumption, a novel plug tray seeding method suitable for a broader range of small seed sizes has been proposed. A universal plug tray seeder has also been designed based on electrostatic adsorption for small seeds. Key factors affecting seed electrostatic adsorption were analyzed through electrostatic simulation, determining the optimal manufacturing method for the suction needle and the best range for the electrostatic voltage. Leveraging the theory of granular dynamics, a seed vibration box was designed using the principle of microphone vibration to enhance seed flowability and reduce the multiple seeding rate. Furthermore, the control system achieved seed recognition based on YOLOv8n and adaptive matching of seeding parameters, enhancing the universality of the seeder. The seeder was optimized and validated through practical experiments, with a comparative analysis of energy consumption and sound intensity conducted. The results indicated that the electrostatic suction needle, made with a single copper electrode of 1 mm diameter and coated with a 1 mm thick planar epoxy resin adsorption layer, along with an electrostatic voltage of 5 ∼ 10 kV, could effectively adsorb seeds. The vibration box significantly improved the seeding effect by vibrating seeds of tomato, pepper, and muskmelon at frequencies of 10 ∼ 25 Hz, and seeds of broccoli, cabbage, and eggplant at frequencies of 30 ∼ 50 Hz. The combined action of the electrostatic suction needle and the vibrating seed box resulted in an 83.20 % reduction in energy consumption and a significant decrease in sound intensity. Although the single seeding rate for muskmelon and cabbage seeds slightly decreased due to higher rates of leakage seeding and multiple seeding, the single seeding rate for other seeds remained around 90 %. This study provides a theoretical foundation for the universal seeding method of small seeds and offers significant reference value for the design of low-energy, low-noise plug tray seeders.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109651"},"PeriodicalIF":7.7,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-13DOI: 10.1016/j.compag.2024.109653
Jibo Yue , Jian Wang , Zhaoying Zhang , Changchun Li , Hao Yang , Haikuan Feng , Wei Guo
The crop leaf area index (LAI) and leaf chlorophyll content (LCC) are essential indicators that reflect crop growth status, and their accurate estimation is helpful for agricultural management decision-making. Traditional hyperspectral estimation methods for crop LAI and LCC from canopy spectra face challenges due to intricate soil backgrounds, canopy structural environments, and varying observational conditions. This paper proposes an LAI and LCC estimation method based on hyperspectral remote sensing, a radiative transfer model (RTM), and a leaf area index and leaf chlorophyll content deep learning network (LACNet). The LACNet architecture was developed utilizing deep and shallow feature fusion, blocks, and a hyperspectral-to-image transform (HIT) concept, aiming to improve LAI and LCC estimation. We used a field-based spectrometer to collect a dataset comprising 1,234 spectral measurements across five crop types: wheat, maize, potato, rice, and soybean. We used properties optique spectrales des feuilles and scattering by arbitrarily inclined leaves (PROSAIL) to generate a simulated spectra dataset (n = 145,152) representing complex farmland conditions for the five abovementioned crops, considering the variations in soil type, soil moisture, LAI, LCC, etc. The LACNet deep learning model sequentially uses RTM simulated and field-based spectra datasets for training, achieving higher universality and validation accuracy. We also analyzed the LACNet model’s interpretability for LAI and LCC estimation based on the gradient-weighted class activation mapping theory. From our research, we drew the following conclusions: (1) The shallow network features are sensitive to the LAI and LCC in the entire visible band, consistent with our correlation analysis results, while the deep network sensitive areas are mainly concentrated in the RE + VIS and RE + NIR regions of the HIT images. (2) The LACNet deep learning model (LAI: coefficient of determination (R2) = 0.770, root mean square error (RMSE) = 0.968 m2/m2; LCC: R2 = 0.765, RMSE = 4.547 Dualex readings) can provide higher crop LAI and LCC estimation accuracy than widely used spectral feature and statistical regression methods (LCC: R2 = 0.491–0.620, RMSE = 5.804–6.691 Dualex readings; LAI: R2 = 0.476–0.716, RMSE = 1.089–1.482 m2/m2). The results of this study highlight the potential of the LACNet deep learning model as an effective and robust tool for accurately estimating crop LAI and LCC.
作物叶面积指数（LAI）和叶片叶绿素含量（LCC）是反映作物生长状况的重要指标，对它们的准确估算有助于农业管理决策。由于复杂的土壤背景、冠层结构环境和不同的观测条件，传统的高光谱冠层光谱作物叶面积指数和叶绿素含量估算方法面临挑战。本文提出了一种基于高光谱遥感、辐射传递模型（RTM）以及叶面积指数和叶绿素含量深度学习网络（LACNet）的 LAI 和 LCC 估算方法。LACNet 架构是利用深层和浅层特征融合、块和高光谱到图像转换（HIT）概念开发的，旨在改进 LAI 和 LCC 估算。我们使用田间光谱仪收集了一个数据集，其中包括对小麦、玉米、马铃薯、水稻和大豆五种作物类型的 1,234 次光谱测量。考虑到土壤类型、土壤湿度、LAI、LCC 等因素的变化，我们利用任意倾斜叶片的光谱和散射特性（PROSAIL）生成了一个模拟光谱数据集（n = 145,152），代表了上述五种作物的复杂农田条件。LACNet 深度学习模型依次使用 RTM 模拟数据集和田间光谱数据集进行训练，实现了更高的普适性和验证精度。我们还基于梯度加权类激活映射理论，分析了 LACNet 模型在 LAI 和 LCC 估算中的可解释性。通过研究，我们得出以下结论：（1）浅层网络特征对整个可见光波段的 LAI 和 LCC 敏感，这与我们的相关性分析结果一致，而深层网络敏感区域主要集中在 HIT 图像的 RE + VIS 和 RE + NIR 区域。(2) LACNet 深度学习模型（LAI：决定系数 (R2) = 0.770，均方根误差 (RMSE) = 0.968 m2/m2；LCC：R2 = 0.765，均方根误差 = 4.547 Dualex 读数）与广泛使用的光谱特征和统计回归方法（LCC：R2 = 0.491-0.620, RMSE = 5.804-6.691 Dualex 读数；LAI：R2 = 0.476-0.716, RMSE = 1.089-1.482 m2/m2）。这项研究的结果凸显了 LACNet 深度学习模型作为准确估算作物 LAI 和 LCC 的有效、稳健工具的潜力。
{"title":"Estimating crop leaf area index and chlorophyll content using a deep learning-based hyperspectral analysis method","authors":"Jibo Yue , Jian Wang , Zhaoying Zhang , Changchun Li , Hao Yang , Haikuan Feng , Wei Guo","doi":"10.1016/j.compag.2024.109653","DOIUrl":"10.1016/j.compag.2024.109653","url":null,"abstract":"<div><div>The crop leaf area index (LAI) and leaf chlorophyll content (LCC) are essential indicators that reflect crop growth status, and their accurate estimation is helpful for agricultural management decision-making. Traditional hyperspectral estimation methods for crop LAI and LCC from canopy spectra face challenges due to intricate soil backgrounds, canopy structural environments, and varying observational conditions. This paper proposes an LAI and LCC estimation method based on hyperspectral remote sensing, a radiative transfer model (RTM), and a leaf area index and leaf chlorophyll content deep learning network (LACNet). The LACNet architecture was developed utilizing deep and shallow feature fusion, blocks, and a hyperspectral-to-image transform (HIT) concept, aiming to improve LAI and LCC estimation. We used a field-based spectrometer to collect a dataset comprising 1,234 spectral measurements across five crop types: wheat, maize, potato, rice, and soybean. We used properties optique spectrales des feuilles and scattering by arbitrarily inclined leaves (PROSAIL) to generate a simulated spectra dataset (n = 145,152) representing complex farmland conditions for the five abovementioned crops, considering the variations in soil type, soil moisture, LAI, LCC, etc. The LACNet deep learning model sequentially uses RTM simulated and field-based spectra datasets for training, achieving higher universality and validation accuracy. We also analyzed the LACNet model’s interpretability for LAI and LCC estimation based on the gradient-weighted class activation mapping theory. From our research, we drew the following conclusions: (1) The shallow network features are sensitive to the LAI and LCC in the entire visible band, consistent with our correlation analysis results, while the deep network sensitive areas are mainly concentrated in the RE + VIS and RE + NIR regions of the HIT images. (2) The LACNet deep learning model (LAI: coefficient of determination (<em>R<sup>2</sup></em>) = 0.770, root mean square error (RMSE) = 0.968 m<sup>2</sup>/m<sup>2</sup>; LCC: <em>R</em><sup>2</sup> = 0.765, RMSE = 4.547 Dualex readings) can provide higher crop LAI and LCC estimation accuracy than widely used spectral feature and statistical regression methods (LCC: <em>R</em><sup>2</sup> = 0.491–0.620, RMSE = 5.804–6.691 Dualex readings; LAI: <em>R</em><sup>2</sup> = 0.476–0.716, RMSE = 1.089–1.482 m<sup>2</sup>/m<sup>2</sup>). The results of this study highlight the potential of the LACNet deep learning model as an effective and robust tool for accurately estimating crop LAI and LCC.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109653"},"PeriodicalIF":7.7,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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