Pub Date : 2025-12-01DOI: 10.1016/j.inpa.2025.07.002
Xianyin Ding , Pieter B. Pelser , Cong Xu , Ilga Porth , Mingming Cui , Yousry A. El-Kassaby , Shu Diao , Qifu Luan , Yanjie Li
Advances in high-throughput phenotyping and genomics have accelerated our comprehension of plant functional differentiation. Nevertheless, efficiently phenotyping long-lived tree breeding populations and studying their dynamic response to field conditions remains a challenge, hindering genetic dissection and selective breeding efforts. This study refined and employed a newly developed high-efficiency unmanned aerial vehicle (UAV) imaging system to assess the temporal response of a slash pine (Pinus elliottii) breeding population in field conditions quantitatively over 2 years, identifying six strongly interrelated dynamic growth traits. In a genome-wide association study, 34 trait-associated loci explained between 1.1 % and –14.2 % of temporal phenotypic variation. These genes and regulatory loci influence signal reception, transduction, and transcriptional regulation networks in dynamic growth, impacting metabolic pathways such as cell membrane assembly, cell wall degradation, and cell differentiation. The enhanced UAV imaging system facilitates comprehensive analysis of dynamic growth response in trees, aiding in the discovery of informative alleles to unravel the genetic basis of complex phenotypic variation in conifers.
{"title":"Leveraging close-range UAV phenotyping and GWAS for enhanced understanding of slash pine growth dynamics","authors":"Xianyin Ding , Pieter B. Pelser , Cong Xu , Ilga Porth , Mingming Cui , Yousry A. El-Kassaby , Shu Diao , Qifu Luan , Yanjie Li","doi":"10.1016/j.inpa.2025.07.002","DOIUrl":"10.1016/j.inpa.2025.07.002","url":null,"abstract":"<div><div>Advances in high-throughput phenotyping and genomics have accelerated our comprehension of plant functional differentiation. Nevertheless, efficiently phenotyping long-lived tree breeding populations and studying their dynamic response to field conditions remains a challenge, hindering genetic dissection and selective breeding efforts. This study refined and employed a newly developed high-efficiency unmanned aerial vehicle (UAV) imaging system to assess the temporal response of a slash pine (<em>Pinus elliottii</em>) breeding population in field conditions quantitatively over 2 years, identifying six strongly interrelated dynamic growth traits. In a genome-wide association study, 34 trait-associated loci explained between 1.1 % and –14.2 % of temporal phenotypic variation. These genes and regulatory loci influence signal reception, transduction, and transcriptional regulation networks in dynamic growth, impacting metabolic pathways such as cell membrane assembly, cell wall degradation, and cell differentiation. The enhanced UAV imaging system facilitates comprehensive analysis of dynamic growth response in trees, aiding in the discovery of informative alleles to unravel the genetic basis of complex phenotypic variation in conifers.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 4","pages":"Pages 550-564"},"PeriodicalIF":7.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145697904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.inpa.2025.07.004
Huang Junjie , Ma Zheng , Wu Yuzhu , Bao Yujian , Wang Yizhe , Su Zhongbin , Guo Lifeng
Root mold proliferation presents a significant challenge in the industrial production of hydroponic barley seedlings. The small size, inconspicuous coloration, and indiscernible image of early mold regions pose new demands on detection accuracy. This study constructed a dataset of root mold in barley seedlings throughout their growth cycle and proposed the YOLOv8n-DDS detection model to integrate a lightweight detection model into a three-dimensional cyclic cultivation system. The model incorporates the dynamic sample (DySample) operator, combines deformable ConvNets v2 (DCNv2) with C2f, and reconstructs the detection head using seam carving (SEAM) technology, which enhances its capability to extract multi-scale, minute features of early-stage root mold in barley. To improve the model’s performance on edge-embedded devices, this study employed layer-wise adaptive magnitude pruning and channel-wise knowledge distillation methods, thereby significantly reducing the model’s parameter count and computational load. The pruned and distilled model was subsequently deployed on the Jetson Nano platform for validation. Results indicate that the YOLOv8n-DDS model outperformed the baseline model in terms of precision, recall, and mAP50 by 2.4 %, 5.6 %, and 2.2 %, respectively. The parameter count was reduced by 23.8 %, and the computational complexity (Giga floating-point operators per second) was optimized by 14.8 %. Additionally, the detection latency on resource-constrained embedded devices was further reduced by 25.8 % with TensorRT acceleration. The proposed root mold detection model is lightweight and contributes to the intelligent and technological integration of the industrial production process for high-quality barley seedling forage.
{"title":"YOLOv8-DDS: A lightweight model based on pruning and distillation for early detection of root mold in barley seedling","authors":"Huang Junjie , Ma Zheng , Wu Yuzhu , Bao Yujian , Wang Yizhe , Su Zhongbin , Guo Lifeng","doi":"10.1016/j.inpa.2025.07.004","DOIUrl":"10.1016/j.inpa.2025.07.004","url":null,"abstract":"<div><div>Root mold proliferation presents a significant challenge in the industrial production of hydroponic barley seedlings. The small size, inconspicuous coloration, and indiscernible image of early mold regions pose new demands on detection accuracy. This study constructed a dataset of root mold in barley seedlings throughout their growth cycle and proposed the YOLOv8n-DDS detection model to integrate a lightweight detection model into a three-dimensional cyclic cultivation system. The model incorporates the dynamic sample (DySample) operator, combines deformable ConvNets v2 (DCNv2) with C2f, and reconstructs the detection head using seam carving (SEAM) technology, which enhances its capability to extract multi-scale, minute features of early-stage root mold in barley. To improve the model’s performance on edge-embedded devices, this study employed layer-wise adaptive magnitude pruning and channel-wise knowledge distillation methods, thereby significantly reducing the model’s parameter count and computational load. The pruned and distilled model was subsequently deployed on the Jetson Nano platform for validation. Results indicate that the YOLOv8n-DDS model outperformed the baseline model in terms of precision, recall, and mAP50 by 2.4 %, 5.6 %, and 2.2 %, respectively. The parameter count was reduced by 23.8 %, and the computational complexity (Giga floating-point operators per second) was optimized by 14.8 %. Additionally, the detection latency on resource-constrained embedded devices was further reduced by 25.8 % with TensorRT acceleration. The proposed root mold detection model is lightweight and contributes to the intelligent and technological integration of the industrial production process for high-quality barley seedling forage.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 4","pages":"Pages 581-594"},"PeriodicalIF":7.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145697906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the pursuit of enhancing agricultural forecasting in Pakistan, this research integrates remote sensing indices and climatic variables through advanced machine learning algorithms. By meticulously examining ten model combinations within different wheat season scenarios, the study employs nonlinear models, such as Random Forest (RF) and Support Vector Machines (SVM), and linear models, like Least Absolute Shrinkage and Selection Operator (LASSO) and Ridge. This research aims to predict wheat yield in Pakistan by integrating five remote sensing indices, including the Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Atmospherically Resistant Vegetation Index (ARVI) with five climatic variables: Maximum Temperature (), Minimum Temperature (), Rainfall (R), Soil Moisture (SM), and Windspeed (WS) alongside the drought index and standardized Precipitation Evapotranspiration Index (SPEI). Ten model combinations were created within two wheat season scenarios: Full Seasonal Mean Scenario 1 (FSM) (SC1) and Peak Seasonal Mean Scenario 2 (PSM) (SC2). Two nonlinear ML algorithms, RF and SVM, and two linear models, LASSO and Ridge, were employed in both scenarios. Results indicated that in SC1, the RF model combination (GNDVI + SPEI + WS + SM) outperformed other models (R2 = 0.75, RMSE = 2.40, MAE = 1.98). Similarly, in SC2, the RF regression surpassed SVM, with the model combination (GNDVI + SPEI + WS + SM) demonstrating the highest performance, achieving R2 = 0.78, RMSE = 2.25, and MAE = 1.88, followed by (NDVI + + + PPT + PET + WS + SM; R2 = 0.75). The linear LASSO model also performed similarly to RF, achieving R2 = 0.74–0.69 in both scenarios. The findings advocate for utilizing SC2 for yield prediction in ML models. Overall, this study underscores the significance and potential of ML methodologies in timely crop yield prediction across various crop growth stages, thereby establishing a robust foundation for ensuring regional food security.
{"title":"Winter wheat yield prediction using linear and nonlinear machine learning algorithms based on climatological and remote sensing data","authors":"Muhammad Haseeb , Zainab Tahir , Syed Amer Mahmood , Aqil Tariq","doi":"10.1016/j.inpa.2025.02.004","DOIUrl":"10.1016/j.inpa.2025.02.004","url":null,"abstract":"<div><div>In the pursuit of enhancing agricultural forecasting in Pakistan, this research integrates remote sensing indices and climatic variables through advanced machine learning algorithms. By meticulously examining ten model combinations within different wheat season scenarios, the study employs nonlinear models, such as Random Forest (RF) and Support Vector Machines (SVM), and linear models, like Least Absolute Shrinkage and Selection Operator (LASSO) and Ridge. This research aims to predict wheat yield in Pakistan by integrating five remote sensing indices, including the Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Atmospherically Resistant Vegetation Index (ARVI) with five climatic variables: Maximum Temperature (<span><math><msub><mi>T</mi><mrow><mi>max</mi></mrow></msub></math></span>), Minimum Temperature (<span><math><msub><mi>T</mi><mrow><mi>min</mi></mrow></msub></math></span>), Rainfall (R), Soil Moisture (SM), and Windspeed (WS) alongside the drought index and standardized Precipitation Evapotranspiration Index (SPEI). Ten model combinations were created within two wheat season scenarios: Full Seasonal Mean Scenario 1 (FSM) (SC1) and Peak Seasonal Mean Scenario 2 (PSM) (SC2). Two nonlinear ML algorithms, RF and SVM, and two linear models, LASSO and Ridge, were employed in both scenarios. Results indicated that in SC1, the RF model combination (GNDVI + SPEI + WS + SM) outperformed other models (R<sup>2</sup> = 0.75, RMSE = 2.40, MAE = 1.98). Similarly, in SC2, the RF regression surpassed SVM, with the model combination (GNDVI + SPEI + WS + SM) demonstrating the highest performance, achieving R<sup>2</sup> = 0.78, RMSE = 2.25, and MAE = 1.88, followed by (NDVI + <span><math><msub><mi>T</mi><mrow><mi>max</mi></mrow></msub></math></span> + <span><math><msub><mi>T</mi><mrow><mi>min</mi></mrow></msub></math></span> + PPT + PET + WS + SM; R<sup>2</sup> = 0.75). The linear LASSO model also performed similarly to RF, achieving R<sup>2</sup> = 0.74–0.69 in both scenarios. The findings advocate for utilizing SC2 for yield prediction in ML models. Overall, this study underscores the significance and potential of ML methodologies in timely crop yield prediction across various crop growth stages, thereby establishing a robust foundation for ensuring regional food security.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 4","pages":"Pages 431-444"},"PeriodicalIF":7.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145697950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.inpa.2025.05.001
Gianluca Manduca , Lloyd T. Wilson , Cesare Stefanini , Donato Romano
The black soldier fly (BSF) Hermetia illucens has garnered significant attention for its potential in sustainable waste management, nutrient recycling, and the production of valuable resources such as protein-rich animal feed and biofuels. Traditional mass production methods remain labor-intensive and error-prone, needing automated solutions. A critical challenge is the precise identification of BSF different life stages which is essential for optimizing feeding strategies, harvesting, and overall system efficiency. This study explores the use of deep learning, combined with optical flow analysis, to identify BSF life stages, particularly larvae, prepupae, and pupae. A Convolutional Neural Network (CNN) model was employed for real-time BSF larval stages detection. Training, validation, and test were performed on a comprehensive custom dataset of 2130 images. Evaluation metrics including precision, recall, and mean Average Precision (mAP) were assessed. Overall, the CNN model showed a precision of 0.96, a recall of 0.95, and a [email protected] of 0.97 on the test set, confirming its generalization capability and effectiveness in real-world scenarios. The integration of optical flow enhanced the model’s performance by leveraging prior knowledge of motor activity, particularly for identifying and correcting false positives in pupae classification. Automated identification of BSF larval stages optimizes resource management, reduces operational costs, and enhances the economic viability of BSF-based systems. The proposed system extends beyond terrestrial concerns, with potential implications for bioregenerative life-support systems, a promising space technology.
{"title":"Automated detection of larval stages of the black soldier fly (Hermetia illucens Linnaeus) through deep learning augmented with optical flow","authors":"Gianluca Manduca , Lloyd T. Wilson , Cesare Stefanini , Donato Romano","doi":"10.1016/j.inpa.2025.05.001","DOIUrl":"10.1016/j.inpa.2025.05.001","url":null,"abstract":"<div><div>The black soldier fly (BSF) <em>Hermetia illucens</em> has garnered significant attention for its potential in sustainable waste management, nutrient recycling, and the production of valuable resources such as protein-rich animal feed and biofuels. Traditional mass production methods remain labor-intensive and error-prone, needing automated solutions. A critical challenge is the precise identification of BSF different life stages which is essential for optimizing feeding strategies, harvesting, and overall system efficiency. This study explores the use of deep learning, combined with optical flow analysis, to identify BSF life stages, particularly larvae, prepupae, and pupae. A Convolutional Neural Network (CNN) model was employed for real-time BSF larval stages detection. Training, validation, and test were performed on a comprehensive custom dataset of 2130 images. Evaluation metrics including precision, recall, and mean Average Precision (mAP) were assessed. Overall, the CNN model showed a precision of 0.96, a recall of 0.95, and a [email protected] of 0.97 on the test set, confirming its generalization capability and effectiveness in real-world scenarios. The integration of optical flow enhanced the model’s performance by leveraging prior knowledge of motor activity, particularly for identifying and correcting false positives in pupae classification. Automated identification of BSF larval stages optimizes resource management, reduces operational costs, and enhances the economic viability of BSF-based systems. The proposed system extends beyond terrestrial concerns, with potential implications for bioregenerative life-support systems, a promising space technology.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 4","pages":"Pages 501-510"},"PeriodicalIF":7.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145697900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.inpa.2025.06.001
Yang Liu , Yiguang Fan , Jiejie Fan , Jibo Yue , Riqiang Chen , Yanpeng Ma , Mingbo Bian , Fuqin Yang , Haikuan Feng
Aboveground biomass (AGB) reflects the accumulation of crop photosynthesis, and AGB data guide agricultural production and field management practices. AGB can be estimated using UAV hyperspectral data; however, external factors and high-dimensional data lead to uncertainties. To address these issues, a cascading spectral preprocessing and band-optimized AGB estimation framework are proposed. We collected canopy hyperspectral reflectance and potato AGB data across two varieties, three planting densities, four nitrogen levels, and two potassium treatments during three growth stages. Then, we systematically compared the performance of Savitzky-Golay (SG) smoothing, multiplicative scatter correction (MSC), first-order differentiation (FOD) and their cascaded combinations. We also rigorously evaluated the ability of competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA) and their cascaded combination (CARS-SPA) to identify sensitive bands. The results indicated that cascaded spectral preprocessing methods significantly enhance the accuracy of potato AGB estimation. Among these approaches, the SG-MSC-FOD cascade performed most effectively. The combination of CARS and SPA yielded the fewest model variables while achieving the highest estimation accuracy. Furthermore, the integration of SG-MSC-FOD and CARS-SPA with partial least squares regression achieved the highest accuracy in AGB estimation across multiple growth stages, with a coefficient of determination (R2) of 0.73, root mean square error (RMSE) of 256.09 kg/hm2, and normalized root mean square error (NRMSE) of 21.51 %. We validated the proposed method under different varieties, planting densities, and nitrogen and potassium treatments. This approach effectively reduces noise, lowers dimensionality, and enhances AGB estimation accuracy, providing a reliable solution for monitoring potato crop growth using hyperspectral remote sensing.
{"title":"Combining multiple spectral preprocessing and wavelength optimization methods improves potato aboveground biomass estimation","authors":"Yang Liu , Yiguang Fan , Jiejie Fan , Jibo Yue , Riqiang Chen , Yanpeng Ma , Mingbo Bian , Fuqin Yang , Haikuan Feng","doi":"10.1016/j.inpa.2025.06.001","DOIUrl":"10.1016/j.inpa.2025.06.001","url":null,"abstract":"<div><div>Aboveground biomass (AGB) reflects the accumulation of crop photosynthesis, and AGB data guide agricultural production and field management practices. AGB can be estimated using UAV hyperspectral data; however, external factors and high-dimensional data lead to uncertainties. To address these issues, a cascading spectral preprocessing and band-optimized AGB estimation framework are proposed. We collected canopy hyperspectral reflectance and potato AGB data across two varieties, three planting densities, four nitrogen levels, and two potassium treatments during three growth stages. Then, we systematically compared the performance of Savitzky-Golay (SG) smoothing, multiplicative scatter correction (MSC), first-order differentiation (FOD) and their cascaded combinations. We also rigorously evaluated the ability of competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA) and their cascaded combination (CARS-SPA) to identify sensitive bands. The results indicated that cascaded spectral preprocessing methods significantly enhance the accuracy of potato AGB estimation. Among these approaches, the SG-MSC-FOD cascade performed most effectively. The combination of CARS and SPA yielded the fewest model variables while achieving the highest estimation accuracy. Furthermore, the integration of SG-MSC-FOD and CARS-SPA with partial least squares regression achieved the highest accuracy in AGB estimation across multiple growth stages, with a coefficient of determination (R<sup>2</sup>) of 0.73, root mean square error (RMSE) of 256.09 kg/hm<sup>2</sup>, and normalized root mean square error (NRMSE) of 21.51 %. We validated the proposed method under different varieties, planting densities, and nitrogen and potassium treatments. This approach effectively reduces noise, lowers dimensionality, and enhances AGB estimation accuracy, providing a reliable solution for monitoring potato crop growth using hyperspectral remote sensing.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 4","pages":"Pages 511-521"},"PeriodicalIF":7.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145697901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.inpa.2025.06.002
Ning Wang , Zhiwen Jin , Man Zhang , Jianxing Xiao , Tianhai Wang , Qiang Sheng , Hao Wang , Han Li
Efficient coordination of machinery fleets in regional farmland operations remains a significant challenge due to the lack of scientifically grounded scheduling management strategies, high modeling complexity, and elevated operational costs. This study proposed an integrated solution for collaborative scheduling of heterogeneous agricultural machines of different types, aiming to address the collaborative scheduling of harvesters and grain trucks in harvest-transport scenarios. Firstly, an electronic farm map was constructed to facilitate path planning and generate unloading points within plots. The study then developed a collaborative scheduling model involving multiple machines, which incorporated heterogeneous parameters such as harvester harvesting speeds and grain truck hopper capacities. The model aims to minimize the total operational time of the machinery fleet. The scheduling problem was addressed by introducing a hybrid greedy heuristic-based improved genetic algorithm. Simulation and experimental validation were conducted using the electronic map of the Shanghai Qingpu unmanned farm. The results demonstrated that the proposed algorithm outperforms three algorithms in optimizing total operational time. For example, when the number of tasks is 20, the average total operational time is reduced by 32.4 min, an improvement of approximately 11.45% compared to the standard genetic algorithm. Additionally, parameter comparison experiments validate the algorithm’s compatibility with heterogeneous parameter settings, thereby substantiating its efficacy in addressing task allocation problems for heterogeneous machinery. The effectiveness of the proposed method in facilitating efficient collaboration among heterogeneous agricultural machines of different types is demonstrated through a case study on collaborative scheduling in harvest-transport scenarios. The findings validate the feasibility and applicability of the proposed approach in effectively addressing real-world agricultural scheduling challenges.
{"title":"An integrated solution for collaborative scheduling of heterogeneous agricultural machines of different types in harvesting-transportation scenarios","authors":"Ning Wang , Zhiwen Jin , Man Zhang , Jianxing Xiao , Tianhai Wang , Qiang Sheng , Hao Wang , Han Li","doi":"10.1016/j.inpa.2025.06.002","DOIUrl":"10.1016/j.inpa.2025.06.002","url":null,"abstract":"<div><div>Efficient coordination of machinery fleets in regional farmland operations remains a significant challenge due to the lack of scientifically grounded scheduling management strategies, high modeling complexity, and elevated operational costs. This study proposed an integrated solution for collaborative scheduling of heterogeneous agricultural machines of different types, aiming to address the collaborative scheduling of harvesters and grain trucks in harvest-transport scenarios. Firstly, an electronic farm map was constructed to facilitate path planning and generate unloading points within plots. The study then developed a collaborative scheduling model involving multiple machines, which incorporated heterogeneous parameters such as harvester harvesting speeds and grain truck hopper capacities. The model aims to minimize the total operational time of the machinery fleet. The scheduling problem was addressed by introducing a hybrid greedy heuristic-based improved genetic algorithm. Simulation and experimental validation were conducted using the electronic map of the Shanghai Qingpu unmanned farm. The results demonstrated that the proposed algorithm outperforms three algorithms in optimizing total operational time. For example, when the number of tasks is 20, the average total operational time is reduced by 32.4 min, an improvement of approximately 11.45% compared to the standard genetic algorithm. Additionally, parameter comparison experiments validate the algorithm’s compatibility with heterogeneous parameter settings, thereby substantiating its efficacy in addressing task allocation problems for heterogeneous machinery. The effectiveness of the proposed method in facilitating efficient collaboration among heterogeneous agricultural machines of different types is demonstrated through a case study on collaborative scheduling in harvest-transport scenarios. The findings validate the feasibility and applicability of the proposed approach in effectively addressing real-world agricultural scheduling challenges.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 4","pages":"Pages 522-538"},"PeriodicalIF":7.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145697902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.inpa.2025.04.001
Antonino GALATI, Serena SOFIA, Maria CRESCIMANNO
Precision farming technologies are revolutionising the wine-growing sector thanks to their ability to manage crop variability, increase economic benefits, reduce the environmental impact, and improve grape yields and quality. Most earlier studies focused on the effects of precision technology adoption on plant health and canopy development—and therefore grape quality—neglecting the profitability impact. This study aims to fill this gap by presenting a systematic literature analysis discussing advancements in the economics of precision viticulture technologies. The results show how technologies such as unmanned aerial vehicles, precision irrigation, and robotics can increase efficiency in resource management, helping to reduce costs and improve vineyard profitability. However, the findings also emphasise the need for tailored approaches to integrate these advances. Furthermore, the analysis highlights the main barriers related to the cost of adopting precision technologies and the skills required to read and interpret the data. The results of this study hold interest to academics, vine growers, and farmers, providing a basis for future research into the cost-effectiveness of adopting precision technologies.
{"title":"Economics and barriers of precision viticulture technologies: A comprehensive systematic literature review","authors":"Antonino GALATI, Serena SOFIA, Maria CRESCIMANNO","doi":"10.1016/j.inpa.2025.04.001","DOIUrl":"10.1016/j.inpa.2025.04.001","url":null,"abstract":"<div><div>Precision farming technologies are revolutionising the wine-growing sector thanks to their ability to manage crop variability, increase economic benefits, reduce the environmental impact, and improve grape yields and quality. Most earlier studies focused on the effects of precision technology adoption on plant health and canopy development—and therefore grape quality—neglecting the profitability impact. This study aims to fill this gap by presenting a systematic literature analysis discussing advancements in the economics of precision viticulture technologies. The results show how technologies such as unmanned aerial vehicles, precision irrigation, and robotics can increase efficiency in resource management, helping to reduce costs and improve vineyard profitability. However, the findings also emphasise the need for tailored approaches to integrate these advances. Furthermore, the analysis highlights the main barriers related to the cost of adopting precision technologies and the skills required to read and interpret the data. The results of this study hold interest to academics, vine growers, and farmers, providing a basis for future research into the cost-effectiveness of adopting precision technologies.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 4","pages":"Pages 487-500"},"PeriodicalIF":7.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145697899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.inpa.2025.07.001
Xinhao Zhang , Guangpeng Zhang , Jiayi Wang , Jinqi Yang , Quanqu Ge , Ran Zhao , Yang Wang
The labor cost in agriculture is gradually increasing, making it necessary to develop robots for strawberry picking. These robots require accurate strawberry localization, which remains challenging using machine vision. While instance segmentation can improve positioning accuracy, current algorithms are inefficient on edge computing devices during robot navigation and ineffective for recognizing strawberries in elevated cultivation. This paper proposes an improved YOLOv8n model (YOLOv8n-MCP) optimized for edge computing during robot navigation. The network implements three key improvements: 1) MobileNetV3 as the backbone, enhancing strawberry feature extraction under varied lighting while reducing parameters and GFLOPs; 2) a new Cross-scale Feature Fusion Module (CCFM) as the Neck, improving detection of strawberries at varying distances; and 3) Partial Convolution (PConv) to enhance C2f and Head components, further reducing network parameters and GFLOPs while improving FPS. Experimental results show that compared to YOLOv8n, YOLOv8n-MCP reduces parameters by 69 %, GFLOPs by 56 %, and increases FPS by 42 %. Tests on Nvidia Jetson Xavier NX demonstrate that YOLOv8n-MCP achieves 49.5 FPS, significantly outperforming the original YOLOv8n’s 37.6 FPS, effectively meeting the requirements for strawberry instance segmentation during robot navigation with edge devices.
{"title":"Efficient instance segmentation for strawberry in greenhouses using YOLOv8n-MCP on edge devices","authors":"Xinhao Zhang , Guangpeng Zhang , Jiayi Wang , Jinqi Yang , Quanqu Ge , Ran Zhao , Yang Wang","doi":"10.1016/j.inpa.2025.07.001","DOIUrl":"10.1016/j.inpa.2025.07.001","url":null,"abstract":"<div><div>The labor cost in agriculture is gradually increasing, making it necessary to develop robots for strawberry picking. These robots require accurate strawberry localization, which remains challenging using machine vision. While instance segmentation can improve positioning accuracy, current algorithms are inefficient on edge computing devices during robot navigation and ineffective for recognizing strawberries in elevated cultivation. This paper proposes an improved YOLOv8n model (YOLOv8n-MCP) optimized for edge computing during robot navigation. The network implements three key improvements: 1) MobileNetV3 as the backbone, enhancing strawberry feature extraction under varied lighting while reducing parameters and GFLOPs; 2) a new Cross-scale Feature Fusion Module (CCFM) as the Neck, improving detection of strawberries at varying distances; and 3) Partial Convolution (PConv) to enhance C2f and Head components, further reducing network parameters and GFLOPs while improving FPS. Experimental results show that compared to YOLOv8n, YOLOv8n-MCP reduces parameters by 69 %, GFLOPs by 56 %, and increases FPS by 42 %. Tests on Nvidia Jetson Xavier NX demonstrate that YOLOv8n-MCP achieves 49.5 FPS, significantly outperforming the original YOLOv8n’s 37.6 FPS, effectively meeting the requirements for strawberry instance segmentation during robot navigation with edge devices.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 4","pages":"Pages 539-549"},"PeriodicalIF":7.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145697903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.inpa.2025.02.006
Xincai Yu , Shuangyin Liu , Chenjiaozi Wang , Binbin Jiao , Cong Huang , Bo Liu , Conghui Liu , Liping Yin , Fanghao Wan , Wanqiang Qian , Xi Qiao
Citrus fruit fungal disease is a major reason for the serious decline in citrus production and quality. Due to its highly contagious nature, timely and effective detection is an important means of prevention and control. Given the high similarity between citrus quarantine diseases and local similar diseases after invading citrus fruits, this study utilizes hyperspectral imaging technology to acquire hyperspectral images of citrus diseases caused by three types of fungi (Phytophthora citrophthora, Phytophthora citricola, Phytophthora syringae). By studying the spectral features of different regions affected by citrus diseases, the competitive adaptive resampling algorithm (CARS) was used to extract 44 feature bands for reconstructing the spectral image, aiming to reduce information redundancy without losing critical information. A simple deep learning model architecture was proposed, which achieved an accuracy of 92.50% in the test dataset. This study provides a new perspective and method for citrus disease detection, offering theoretical and scientific support for the detection of citrus diseases using deep learning and hyperspectral imaging technology.
{"title":"Detection of fungal disease in citrus fruit based on hyperspectral imaging","authors":"Xincai Yu , Shuangyin Liu , Chenjiaozi Wang , Binbin Jiao , Cong Huang , Bo Liu , Conghui Liu , Liping Yin , Fanghao Wan , Wanqiang Qian , Xi Qiao","doi":"10.1016/j.inpa.2025.02.006","DOIUrl":"10.1016/j.inpa.2025.02.006","url":null,"abstract":"<div><div>Citrus fruit fungal disease is a major reason for the serious decline in citrus production and quality. Due to its highly contagious nature, timely and effective detection is an important means of prevention and control. Given the high similarity between citrus quarantine diseases and local similar diseases after invading citrus fruits, this study utilizes hyperspectral imaging technology to acquire hyperspectral images of citrus diseases caused by three types of fungi (<em>Phytophthora citrophthora</em>, <em>Phytophthora citricola</em>, <em>Phytophthora syringae</em>). By studying the spectral features of different regions affected by citrus diseases, the competitive adaptive resampling algorithm (CARS) was used to extract 44 feature bands for reconstructing the spectral image, aiming to reduce information redundancy without losing critical information. A simple deep learning model architecture was proposed, which achieved an accuracy of 92.50% in the test dataset. This study provides a new perspective and method for citrus disease detection, offering theoretical and scientific support for the detection of citrus diseases using deep learning and hyperspectral imaging technology.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 4","pages":"Pages 456-465"},"PeriodicalIF":7.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145697843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Automated vision-based detection and counting are critical for accurate tomato yield estimation, which contribute to precise yield management strategies and an efficient food supply chains. Special conditions, including background clutter, occlusion, and varying sunlight, affect the accuracy of crop detection and counting. To determine the most suitable algorithms for this yield estimation context, we herein establish a public multi-object tracking (MOT) dataset for tomato cluster counts, while evaluating and comparing state-of-the-art target detection and MOT-based algorithms. The evaluated detectors consist of YOLOv8 and RT-DETR, which represent algorithms that achieve a balance between accuracy and speed. The tracking algorithms included state-of-the-art methodologies such as SORT, DeepSort, ByteTrack, and BotSort. Initially, the performance of the detectors was rigorously evaluated, followed by a comprehensive assessment of the four tracking algorithms within a multi-target tracking database tailored for this research and structured in the MOT context. The findings reveal that YOLOv8 and RT-DETR achieve 93.6% and 94.9% results at mAP@75, respectively, with RT-DETR exhibiting fewer false detections. When combined with the RT-DETR detector, the ByteTrack-based algorithm registers the highest counting accuracy at 95.5%, whereas BotSort achieves the highest MOTA score with 84.6%. Notably, the trackers without the ReID module (i.e., SORT and ByteTrack) demonstrate greater adaptability to frame rate variations in the test videos. At a 30-fps frame rate, the incorporation of ReID modules in DeepSort and BotSort algorithms significantly enhances the MOTA metric. Looking ahead, we plan to leverage these algorithms into an autonomous inspection platform that aims to estimate crop yield in real-time.
{"title":"Assessment of the tomato cluster yield estimation algorithms via tracking-by-detection approaches","authors":"Zhongxian Qi , Tianxue Zhang , Ting Yuan , Wei Zhou , Wenqiang Zhang","doi":"10.1016/j.inpa.2025.02.005","DOIUrl":"10.1016/j.inpa.2025.02.005","url":null,"abstract":"<div><div>Automated vision-based detection and counting are critical for accurate tomato yield estimation, which contribute to precise yield management strategies and an efficient food supply chains. Special conditions, including background clutter, occlusion, and varying sunlight, affect the accuracy of crop detection and counting. To determine the most suitable algorithms for this yield estimation context, we herein establish a public multi-object tracking (MOT) dataset for tomato cluster counts, while evaluating and comparing state-of-the-art target detection and MOT-based algorithms. The evaluated detectors consist of YOLOv8 and RT-DETR, which represent algorithms that achieve a balance between accuracy and speed. The tracking algorithms included state-of-the-art methodologies such as SORT, DeepSort, ByteTrack, and BotSort. Initially, the performance of the detectors was rigorously evaluated, followed by a comprehensive assessment of the four tracking algorithms within a multi-target tracking database tailored for this research and structured in the MOT context. The findings reveal that YOLOv8 and RT-DETR achieve 93.6% and 94.9% results at mAP@75, respectively, with RT-DETR exhibiting fewer false detections. When combined with the RT-DETR detector, the ByteTrack-based algorithm registers the highest counting accuracy at 95.5%, whereas BotSort achieves the highest MOTA score with 84.6%. Notably, the trackers without the ReID module (i.e., SORT and ByteTrack) demonstrate greater adaptability to frame rate variations in the test videos. At a 30-fps frame rate, the incorporation of ReID modules in DeepSort and BotSort algorithms significantly enhances the MOTA metric. Looking ahead, we plan to leverage these algorithms into an autonomous inspection platform that aims to estimate crop yield in real-time.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 4","pages":"Pages 445-455"},"PeriodicalIF":7.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145697898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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