Pub Date : 2024-11-28DOI: 10.1016/j.compag.2024.109689
Pei Wang , Pengxin Wu , Chao Wang , Xiaofeng Huang , Lihong Wang , Chengsong Li , Qi Niu , Hui Li
Severe diseases in chickens present substantial risks to poultry husbandry industry. Notably, alterations in body temperature serve as critical clinical indicators of these diseases. Consequently, timely and accurate monitoring of body temperature is essential for the early detection of severe health issues in chickens. This study presents a novel method for simultaneous body temperature detection of multiple chickens in caged poultry environments. A dataset of 2896 chicken head images was developed. The YOLOv8n-mvc model was created to accurately detect chicken head positions and extracted temperature data and distance information through the fusion of RGB, thermal infrared, and depth images. The chicken head temperature was calibrated using distance information. The YOLOv8n-mvc model established in this study achieved a precision of 91.6 %, recall of 92.5 %, F1 score of 92.0 %, and [email protected] of 96.0 %. The model was successfully deployed on an edge computing device for validation tests, demonstrating its feasibility for chicken body temperature detection. This study provides a reference for developing a chicken health monitoring system based on body temperature.
{"title":"Chicken body temperature monitoring method in complex environment based on multi-source image fusion and deep learning","authors":"Pei Wang , Pengxin Wu , Chao Wang , Xiaofeng Huang , Lihong Wang , Chengsong Li , Qi Niu , Hui Li","doi":"10.1016/j.compag.2024.109689","DOIUrl":"10.1016/j.compag.2024.109689","url":null,"abstract":"<div><div>Severe diseases in chickens present substantial risks to poultry husbandry industry. Notably, alterations in body temperature serve as critical clinical indicators of these diseases. Consequently, timely and accurate monitoring of body temperature is essential for the early detection of severe health issues in chickens. This study presents a novel method for simultaneous body temperature detection of multiple chickens in caged poultry environments. A dataset of 2896 chicken head images was developed. The YOLOv8n-mvc model was created to accurately detect chicken head positions and extracted temperature data and distance information through the fusion of RGB, thermal infrared, and depth images. The chicken head temperature was calibrated using distance information. The YOLOv8n-mvc model established in this study achieved a precision of 91.6 %, recall of 92.5 %, F1 score of 92.0 %, and [email protected] of 96.0 %. The model was successfully deployed on an edge computing device for validation tests, demonstrating its feasibility for chicken body temperature detection. This study provides a reference for developing a chicken health monitoring system based on body temperature.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"228 ","pages":"Article 109689"},"PeriodicalIF":7.7,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746348","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 : 2024-11-27DOI: 10.1016/j.compag.2024.109463
Xiaorong Wang , Jianping Zhou , Yan Xu , Chao Cui , Zihe Liu , Jinrong Chen
Mechanized safflower harvesting is prone to inaccurate recognition and positioning of safflower filaments, which is influenced by complex environmental factors such as occlusion, lighting, and challenges related to small targets and small samples. To solve this problem, we improved on the Yolov5 algorithm model and developed a two-stage recognition and positioning approach named Yolov5-ABBM. A safflower dataset was established to classify safflower filaments based on their maturity levels. The Swin Transformer attention mechanism was incorporated to improve the feature-extraction capability of the algorithm model, particularly for small samples and small targets. A geometric operation algorithm based on Bbox and Mask (ABBM) was developed to enhance the positioning speed and minimize missed recognition when locating safflower-filament picking points. Experimental results show that the improved model achieved a recognition precision improvement of 5.8% and 7.9% based on Bbox and Mask, respectively, and exhibited a significant enhancement of 15.3% and 19.4% for small samples. The positioning precision reached 98.19%, with an average positioning running time of 0.018 s per frame image. The improved model demonstrated superior accuracy and positioning speed compared with other algorithm models. The results show that the improved model could accurately identify and locate safflower-filament picking points, particularly for small samples, thereby offering technical support for efficient mechanized safflower harvesting.
{"title":"Location of safflower filaments picking points in complex environment based on improved Yolov5 algorithm","authors":"Xiaorong Wang , Jianping Zhou , Yan Xu , Chao Cui , Zihe Liu , Jinrong Chen","doi":"10.1016/j.compag.2024.109463","DOIUrl":"10.1016/j.compag.2024.109463","url":null,"abstract":"<div><div>Mechanized safflower harvesting is prone to inaccurate recognition and positioning of safflower filaments, which is influenced by complex environmental factors such as occlusion, lighting, and challenges related to small targets and small samples. To solve this problem, we improved on the Yolov5 algorithm model and developed a two-stage recognition and positioning approach named Yolov5-ABBM. A safflower dataset was established to classify safflower filaments based on their maturity levels. The Swin Transformer attention mechanism was incorporated to improve the feature-extraction capability of the algorithm model, particularly for small samples and small targets. A geometric operation algorithm based on Bbox and Mask (ABBM) was developed to enhance the positioning speed and minimize missed recognition when locating safflower-filament picking points. Experimental results show that the improved model achieved a recognition precision improvement of 5.8% and 7.9% based on Bbox and Mask, respectively, and exhibited a significant enhancement of 15.3% and 19.4% for small samples. The positioning precision reached 98.19%, with an average positioning running time of 0.018 s per frame image. The improved model demonstrated superior accuracy and positioning speed compared with other algorithm models. The results show that the improved model could accurately identify and locate safflower-filament picking points, particularly for small samples, thereby offering technical support for efficient mechanized safflower harvesting.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109463"},"PeriodicalIF":7.7,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721692","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}
Drought-induced stress significantly impacted blueberry production due to the plants’ inefficient water regulation mechanisms to maintain yield and fruit quality under drought stress. Traditional methods of manual phenotyping for drought stress are not only time-consuming but also labor-intensive. To address the need for accurate and large-scale assessment of drought tolerance, we developed a high-throughput phenotyping (HTP) system to capture hyperspectral images of blueberry plants under drought conditions. A novel transformer-based model, LWC-former was introduced to predict leaf water content (LWC) utilizing spectral reflectance from hyperspectral images obtained from the developed HTP system. The LWC-former transformed the spectral reflectance into patch representations and embedded these patches into a lower dimensional to address multicollinearity issues. These patches were then passed to the transformer encoder to learn distributed features, followed by a regression head to predict LWC. To train the model, spectral reflectance data were extracted from hyperspectral images and pre-processed using log(1/R), mean scatter correction (MSC), and mean centering (MC). The results showed that our model achieved a coefficient of determination (R2) of 0.81 on the test dataset. The performance of the proposed model was also compared with TabTransformer, DeepRWC, multilayer perceptron (MLP), partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF), achieving R2 values of 0.65, 0.73, 0.71, 0.47, and 0.58, respectively. The results demonstrated that LWC-former outperformed other deep learning and statistical-based models. The high-throughput phenotyping system effectively facilitated large-scale data collection, while the LWC-former model addressed multicollinearity issues, significantly improving the prediction of LWC. These results demonstrate the potential of our approach for large-scale drought tolerance assessment in blueberries.
{"title":"Transformer-Based hyperspectral image analysis for phenotyping drought tolerance in blueberries","authors":"Md. Hasibur Rahman , Savannah Busby , Sushan Ru , Sajid Hanif , Alvaro Sanz-Saez , Jingyi Zheng , Tanzeel U. Rehman","doi":"10.1016/j.compag.2024.109684","DOIUrl":"10.1016/j.compag.2024.109684","url":null,"abstract":"<div><div>Drought-induced stress significantly impacted blueberry production due to the plants’ inefficient water regulation mechanisms to maintain yield and fruit quality under drought stress. Traditional methods of manual phenotyping for drought stress are not only time-consuming but also labor-intensive. To address the need for accurate and large-scale assessment of drought tolerance, we developed a high-throughput phenotyping (HTP) system to capture hyperspectral images of blueberry plants under drought conditions. A novel transformer-based model, LWC-former was introduced to predict leaf water content (LWC) utilizing spectral reflectance from hyperspectral images obtained from the developed HTP system. The LWC-former transformed the spectral reflectance into patch representations and embedded these patches into a lower dimensional to address multicollinearity issues. These patches were then passed to the transformer encoder to learn distributed features, followed by a regression head to predict LWC. To train the model, spectral reflectance data were extracted from hyperspectral images and pre-processed using log(1/R), mean scatter correction (MSC), and mean centering (MC). The results showed that our model achieved a coefficient of determination (R<sup>2</sup>) of 0.81 on the test dataset. The performance of the proposed model was also compared with TabTransformer, DeepRWC, multilayer perceptron (MLP), partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF), achieving R<sup>2</sup> values of 0.65, 0.73, 0.71, 0.47, and 0.58, respectively. The results demonstrated that LWC-former outperformed other deep learning and statistical-based models. The high-throughput phenotyping system effectively facilitated large-scale data collection, while the LWC-former model addressed multicollinearity issues, significantly improving the prediction of LWC. These results demonstrate the potential of our approach for large-scale drought tolerance assessment in blueberries.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"228 ","pages":"Article 109684"},"PeriodicalIF":7.7,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721378","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 : 2024-11-27DOI: 10.1016/j.compag.2024.109677
Neamat Karimi, Sara Sheshangosht, Maryam Rashtbari, Omid Torabi, Amirhossein Sarbazvatan, Masoumeh Lari, Hossein Aminzadeh, Sina Abolhoseini, Mortaza Eftekhari
This study presents the first high-resolution Land Use/Land Cover dataset for Iran in 2022 (ILULC-2022), with a particular emphasis on the agricultural areas. This research employed a two-level Decision Tree Object-Oriented Image Analysis (OBIA-DT) model which incorporated segmentation of the study area derived from Google Earth images, and classification using multi-temporal information derived from Sentinel-2 satellite imagery. After segmentation of fine resolution images, the first level of the OBIA-DT model established based on the collected field datasets (about 52,000 field data were collected) to build a light LULC map which broadly identified agricultural land components without differentiating between irrigated and non-irrigated cultivations. The second level used multi-temporal indices derived from Sentinel-2 imagery and supplementary data layers to produce a complete LULC map wherein cropland areas was distinguished further into irrigated and rainfed lands, with four distinctive sub-classifications for irrigated lands. By employing this approach, a LULC map of all basins of Iran were classified into sixteen distinct classes, with different agricultural lands divided into two rainfed croplands (rainfed farming and agroforestry) and five irrigated lands (orchards, fall crops, spring crops, multiple crops, and fallow crops). According to the collected field data, the overall accuracy of ILULC-2022 maps exhibited a range from 85 to 97 % for basins with varying climates ranging from cold and temperate to hot and dry, respectively. Results reveal that the major irrigated crop classes had a user’s accuracy and producer’s accuracy ranging from 91 % to 96 %. Based on the findings of this study, the total area of agricultures in Iran encompasses 20.9 ± 2.1 million ha, constituting approximately 13 % of the Iran’s total land area. Within this agricultural expanse, irrigated (comprising irrigated lands and orchards) and rainfed agricultural lands are delineated as 10.2 ± 1.08 and 10.7 × ± 1.02 million ha, respectively, with most agricultural areas located in basins with moderate to humid climates. The ILULC-2022 dataset serves as a benchmark for future LULC change detection and is a valuable reference for efforts aimed at achieving sustainable development goals in Iran.
{"title":"An advanced high resolution land use/land cover dataset for Iran (ILULC-2022) by focusing on agricultural areas based on remote sensing data","authors":"Neamat Karimi, Sara Sheshangosht, Maryam Rashtbari, Omid Torabi, Amirhossein Sarbazvatan, Masoumeh Lari, Hossein Aminzadeh, Sina Abolhoseini, Mortaza Eftekhari","doi":"10.1016/j.compag.2024.109677","DOIUrl":"10.1016/j.compag.2024.109677","url":null,"abstract":"<div><div>This study presents the first high-resolution Land Use/Land Cover dataset for Iran in 2022 (ILULC-2022), with a particular emphasis on the agricultural areas. This research employed a two-level Decision Tree Object-Oriented Image Analysis (OBIA-DT) model which incorporated segmentation of the study area derived from Google Earth images, and classification using multi-temporal information derived from Sentinel-2 satellite imagery. After segmentation of fine resolution images, the first level of the OBIA-DT model established based on the collected field datasets (about 52,000 field data were collected) to build a light LULC map which broadly identified agricultural land components without differentiating between irrigated and non-irrigated cultivations. The second level used multi-temporal indices derived from Sentinel-2 imagery and supplementary data layers to produce a complete LULC map wherein cropland areas was distinguished further into irrigated and rainfed lands, with four distinctive sub-classifications for irrigated lands. By employing this approach, a LULC map of all basins of Iran were classified into sixteen distinct classes, with different agricultural lands divided into two rainfed croplands (rainfed farming and agroforestry) and five irrigated lands (orchards, fall crops, spring crops, multiple crops, and fallow crops). According to the collected field data, the overall accuracy of ILULC-2022 maps exhibited a range from 85 to 97 % for basins with varying climates ranging from cold and temperate to hot and dry, respectively. Results reveal that the major irrigated crop classes had a user’s accuracy and producer’s accuracy ranging from 91 % to 96 %. Based on the findings of this study, the total area of agricultures in Iran encompasses 20.9 ± 2.1 million ha, constituting approximately 13 % of the Iran’s total land area. Within this agricultural expanse, irrigated (comprising irrigated lands and orchards) and rainfed agricultural lands are delineated as 10.2 ± 1.08 and 10.7 × ± 1.02 million ha, respectively, with most agricultural areas located in basins with moderate to humid climates. The ILULC-2022 dataset serves as a benchmark for future LULC change detection and is a valuable reference for efforts aimed at achieving sustainable development goals in Iran.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"228 ","pages":"Article 109677"},"PeriodicalIF":7.7,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721627","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 : 2024-11-26DOI: 10.1016/j.compag.2024.109683
Cécile M. Levrault , Nico W.M. Ogink , Jan Dijkstra , Peter W.G. Groot Koerkamp , Kelly Nichols , Fred A. van Eeuwijk , Carel F.W. Peeters
Monitoring methane production from individual cows is required for evaluating the success of greenhouse gas reduction strategies. However, converting non-continuous measurements of methane production into daily methane production rates (MPR) remains challenging due to the general non-linearity of the methane production curve. In this paper, we propose a Bayesian hierarchical stochastic kinetic equation approach to address this challenge, enabling the sharing of information across cows for improved modelling. We fit a non-linear curve on climate respiration chamber (CRC) data of 28 dairy cows before computing an area under the curve, thereby providing an estimate of MPR from individual cows, yielding a monitored and predicted population mean of 416.7 ± 36.2 g/d and 407.2 ± 35.0 g/d respectively. The shape parameters of this model were pooled across cows (population-level), while the scale parameter varied between individuals. This allowed for the characterization of variation in MPR within and between cows. Model fit was thoroughly investigated through posterior predictive checking, which showed that the model could reproduce this CRC data accurately. Comparison with a fully pooled model (all parameters constant across cows) was evaluated through cross-validation, where the Hierarchical Methane Rate (HMR) model performed better (difference in expected log predictive density of 1653). Concordance between the values observed in the CRC and those predicted by HMR was assessed with R2 (0.995), root mean square error (10.0 g/d), and Lin’s concordance correlation coefficient (0.961). Overall, the predictions made by the HMR model appeared to reflect individual MPR levels and variation between cows as well as the standard analytical approach taken by scientists with CRC data.
{"title":"Modelling methane production of dairy cows: A hierarchical Bayesian stochastic approach","authors":"Cécile M. Levrault , Nico W.M. Ogink , Jan Dijkstra , Peter W.G. Groot Koerkamp , Kelly Nichols , Fred A. van Eeuwijk , Carel F.W. Peeters","doi":"10.1016/j.compag.2024.109683","DOIUrl":"10.1016/j.compag.2024.109683","url":null,"abstract":"<div><div>Monitoring methane production from individual cows is required for evaluating the success of greenhouse gas reduction strategies. However, converting non-continuous measurements of methane production into daily methane production rates (MPR) remains challenging due to the general non-linearity of the methane production curve. In this paper, we propose a Bayesian hierarchical stochastic kinetic equation approach to address this challenge, enabling the sharing of information across cows for improved modelling. We fit a non-linear curve on climate respiration chamber (CRC) data of 28 dairy cows before computing an area under the curve, thereby providing an estimate of MPR from individual cows, yielding a monitored and predicted population mean of 416.7 ± 36.2 g/d and 407.2 ± 35.0 g/d respectively. The shape parameters of this model were pooled across cows (population-level), while the scale parameter varied between individuals. This allowed for the characterization of variation in MPR within and between cows. Model fit was thoroughly investigated through posterior predictive checking, which showed that the model could reproduce this CRC data accurately. Comparison with a fully pooled model (all parameters constant across cows) was evaluated through cross-validation, where the Hierarchical Methane Rate (HMR) model performed better (difference in expected log predictive density of 1653). Concordance between the values observed in the CRC and those predicted by HMR was assessed with R<sup>2</sup> (0.995), root mean square error (10.0 g/d), and Lin’s concordance correlation coefficient (0.961). Overall, the predictions made by the HMR model appeared to reflect individual MPR levels and variation between cows as well as the standard analytical approach taken by scientists with CRC data.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"228 ","pages":"Article 109683"},"PeriodicalIF":7.7,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721631","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 : 2024-11-26DOI: 10.1016/j.compag.2024.109698
Jae-Woo Song , Mingyung Lee , Hyunjin Cho , Dae-Hyun Lee , Seongwon Seo , Wang-Hee Lee
Daily milk yield serves as a physiological indicator in dairy cows and is a primary target for prediction and real-time monitoring in smart livestock farming. This study attempted to develop an individual model for predicting daily milk yield and applied it to monitor the health status of dairy cows by designing a real-time monitoring algorithm. A total of 580 datasets were used for model development after data preprocessing and screening, which were subsequently used to develop the model by modifying the existing models based on nonlinear regression analysis. The developed model was then applied to short-term real-time monitoring of abnormal daily milk yields. The optimal model was able to predict the daily milk yield, with an R2 value of 0.875 and a root mean squared error of 2.192. Real-time monitoring was designed to detect abnormal daily milk yields by collectively considering a 90% confidence interval and the difference between predicted values and expected trends. This study is the first to design a monitoring algorithm for daily milk yield from dairy cows based on an individual model capable of predicting the daily milk yield. This study expects that a platform will be necessary for highly efficient smart livestock farming, enabling high productivity with minimal inputs.
{"title":"Development of individual models for predicting cow milk production for real-time monitoring","authors":"Jae-Woo Song , Mingyung Lee , Hyunjin Cho , Dae-Hyun Lee , Seongwon Seo , Wang-Hee Lee","doi":"10.1016/j.compag.2024.109698","DOIUrl":"10.1016/j.compag.2024.109698","url":null,"abstract":"<div><div>Daily milk yield serves as a physiological indicator in dairy cows and is a primary target for prediction and real-time monitoring in smart livestock farming. This study attempted to develop an individual model for predicting daily milk yield and applied it to monitor the health status of dairy cows by designing a real-time monitoring algorithm. A total of 580 datasets were used for model development after data preprocessing and screening, which were subsequently used to develop the model by modifying the existing models based on nonlinear regression analysis. The developed model was then applied to short-term real-time monitoring of abnormal daily milk yields. The optimal model was able to predict the daily milk yield, with an R<sup>2</sup> value of 0.875 and a root mean squared error of 2.192. Real-time monitoring was designed to detect abnormal daily milk yields by collectively considering a 90% confidence interval and the difference between predicted values and expected trends. This study is the first to design a monitoring algorithm for daily milk yield from dairy cows based on an individual model capable of predicting the daily milk yield. This study expects that a platform will be necessary for highly efficient smart livestock farming, enabling high productivity with minimal inputs.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"228 ","pages":"Article 109698"},"PeriodicalIF":7.7,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721377","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 : 2024-11-26DOI: 10.1016/j.compag.2024.109696
Lixing Liu , Xu Wang , Jinyan Xie , Xiaosa Wang , Hongjie Liu , Jianping Li , Pengfei Wang , Xin Yang
This research proposes an improved ant colony algorithm (BL-ACO) path planning algorithm and a tracking controller based on global optimal sliding mode variable structure control (GO-SMC) for the problem of path planning and tracking control of lawn mowers in quadrilateral orchard environments. The novelty of this research lies in two aspects. On one hand, we analyze the operating scenarios of lawn mowers in standardized orchards, then transform the path planning problem into a traveling salesman problem, and mathematically model the U-shaped and T-shaped turning strategies based on the characteristics of the wheeled lawn mower. In order to make the ant colony algorithm suitable for orchard operation path optimization problems, we modified its pheromone update rules, heuristic functions, state transition probabilities, and other equations. In order to accelerate the convergence speed of the ant colony algorithm, we use the bilayer ant colony algorithm optimization strategy. On the other hand, we establish a kinematic model with the wheeled lawn mower as the control object, and design a control law using a hyperbolic tangent function to ensure the global stability of the trajectory tracking control system. Furthermore, we demonstrate through Lyapunov stability analysis that the GO-SMC controller can ensure the mower tracks the reference path accurately. The simulation experiments of path planning and tracking control show that BL-ACO and GO-SMC perform the best compared to similar algorithms. Field experiments shows that BL-ACO & GO-SMC, with a time reduction rate of 47.58 % and a fuel consumption rate reduction of 47.59 % compared to line by line & SMC.
本研究针对四边形果园环境中割草机的路径规划和跟踪控制问题，提出了一种改进的蚁群算法（BL-ACO）路径规划算法和基于全局最优滑模变结构控制（GO-SMC）的跟踪控制器。这项研究的新颖之处在于两个方面。一方面，我们分析了割草机在标准化果园中的作业场景，然后将路径规划问题转化为旅行推销员问题，并根据轮式割草机的特点建立了 U 形和 T 形转弯策略的数学模型。为了使蚁群算法适用于果园作业路径优化问题，我们修改了其信息素更新规则、启发式函数、状态转换概率等方程。为了加快蚁群算法的收敛速度，我们采用了双层蚁群算法优化策略。另一方面，我们建立了以轮式割草机为控制对象的运动学模型，并利用双曲正切函数设计了控制律，以确保轨迹跟踪控制系统的全局稳定性。此外，我们还通过 Lyapunov 稳定性分析证明，GO-SMC 控制器能确保割草机准确跟踪参考路径。路径规划和跟踪控制的仿真实验表明，与同类算法相比，BL-ACO 和 GO-SMC 的性能最佳。现场实验表明，与逐行& SMC相比，BL-ACO& GO-SMC的时间缩短率为47.58%，燃料消耗率为47.59%。
{"title":"Path planning and tracking control of orchard wheel mower based on BL-ACO and GO-SMC","authors":"Lixing Liu , Xu Wang , Jinyan Xie , Xiaosa Wang , Hongjie Liu , Jianping Li , Pengfei Wang , Xin Yang","doi":"10.1016/j.compag.2024.109696","DOIUrl":"10.1016/j.compag.2024.109696","url":null,"abstract":"<div><div>This research proposes an improved ant colony algorithm (BL-ACO) path planning algorithm and a tracking controller based on global optimal sliding mode variable structure control (GO-SMC) for the problem of path planning and tracking control of lawn mowers in quadrilateral orchard environments. The novelty of this research lies in two aspects. On one hand, we analyze the operating scenarios of lawn mowers in standardized orchards, then transform the path planning problem into a traveling salesman problem, and mathematically model the U-shaped and T-shaped turning strategies based on the characteristics of the wheeled lawn mower. In order to make the ant colony algorithm suitable for orchard operation path optimization problems, we modified its pheromone update rules, heuristic functions, state transition probabilities, and other equations. In order to accelerate the convergence speed of the ant colony algorithm, we use the bilayer ant colony algorithm optimization strategy. On the other hand, we establish a kinematic model with the wheeled lawn mower as the control object, and design a control law using a hyperbolic tangent function to ensure the global stability of the trajectory tracking control system. Furthermore, we demonstrate through Lyapunov stability analysis that the GO-SMC controller can ensure the mower tracks the reference path accurately. The simulation experiments of path planning and tracking control show that BL-ACO and GO-SMC perform the best compared to similar algorithms. Field experiments shows that BL-ACO & GO-SMC, with a time reduction rate of 47.58 % and a fuel consumption rate reduction of 47.59 % compared to line by line & SMC.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"228 ","pages":"Article 109696"},"PeriodicalIF":7.7,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-26DOI: 10.1016/j.compag.2024.109671
Menno Sytsma, Bart M. van Marrewijk, Toon Tielen, Arjan Vroegop, Jos Ruizendaal
The harvesting of gerbera flowers, like many horticultural products, is a labor-intensive task for which automated solutions are highly desirable. While robotic harvesting of gerbera flowers has previously been attempted, it has not been tested under commercial greenhouse conditions. This study presents a design process based on realistic requirements derived from detailed measurements of the crop. We introduce a specialized end-effector for gerbera flower harvesting that leverages passive components alongside specific plant characteristics to enable precise positioning and effective cutting. An integrated testing setup is also presented, combining the end-effector with a robust, high-speed sensing and processing pipeline for field trials. Performance evaluations of the complete system under real greenhouse conditions indicate an overall harvest success rate of 78%, with minimal flower collisions and reliable positioning and cutting actions by the end-effector.
{"title":"Design and evaluation of a robotic prototype for gerbera harvesting, performing actions at never-seen locations","authors":"Menno Sytsma, Bart M. van Marrewijk, Toon Tielen, Arjan Vroegop, Jos Ruizendaal","doi":"10.1016/j.compag.2024.109671","DOIUrl":"10.1016/j.compag.2024.109671","url":null,"abstract":"<div><div>The harvesting of gerbera flowers, like many horticultural products, is a labor-intensive task for which automated solutions are highly desirable. While robotic harvesting of gerbera flowers has previously been attempted, it has not been tested under commercial greenhouse conditions. This study presents a design process based on realistic requirements derived from detailed measurements of the crop. We introduce a specialized end-effector for gerbera flower harvesting that leverages passive components alongside specific plant characteristics to enable precise positioning and effective cutting. An integrated testing setup is also presented, combining the end-effector with a robust, high-speed sensing and processing pipeline for field trials. Performance evaluations of the complete system under real greenhouse conditions indicate an overall harvest success rate of 78%, with minimal flower collisions and reliable positioning and cutting actions by the end-effector.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"228 ","pages":"Article 109671"},"PeriodicalIF":7.7,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-26DOI: 10.1016/j.compag.2024.109682
Longfei Wang , Le Yang , Huiying Xu , Xinzhong Zhu , Wouladje Cabrel , Golden Tendekai Mumanikidzwa , Xinyu Liu , Weijian Jiang , Hao Chen , Wenhang Jiang
In modern agricultural science research, high-fidelity three-dimensional (3D) leaf models are crucial for crop growth analysis. However, reconstructing the complex morphology and texture of leaves from a single viewpoint under varying natural lighting conditions poses a significant challenge. To address the issues associated with this challenge, this paper presents a diffusion model-based method for single-view leaf reconstruction using potato leaves as the experimental subject. In the camera prediction process, the combination of an explicit point cloud generation technique and an implicit 3D Gaussian rendering technique enables the accurate prediction of camera parameters and the effective capture of leaf phenotypic features. In the synthesis of the 3D model of the leaf, a strategy for optimizing the coarse model UV texture is designed with the objective of achieving spatial consistency of texture details. Furthermore, the model was successfully applied to the reconstruction of other crop leaves and lamellar structural objects, and innovatively constructed a leaf reconstruction model with disease characteristics, aiming to provide a reference for the early 3D detection of crop diseases, as well as a reference for the 3D reconstruction and visualization of other lamellar objects. The results demonstrate that the method is effective in reconstructing the morphological structure and texture details of leaves, as well as thin sheet-like structured objects, achieving fast and high-fidelity single-view reconstruction.
{"title":"Single-view-based high-fidelity three-dimensional reconstruction of leaves","authors":"Longfei Wang , Le Yang , Huiying Xu , Xinzhong Zhu , Wouladje Cabrel , Golden Tendekai Mumanikidzwa , Xinyu Liu , Weijian Jiang , Hao Chen , Wenhang Jiang","doi":"10.1016/j.compag.2024.109682","DOIUrl":"10.1016/j.compag.2024.109682","url":null,"abstract":"<div><div>In modern agricultural science research, high-fidelity three-dimensional (3D) leaf models are crucial for crop growth analysis. However, reconstructing the complex morphology and texture of leaves from a single viewpoint under varying natural lighting conditions poses a significant challenge. To address the issues associated with this challenge, this paper presents a diffusion model-based method for single-view leaf reconstruction using potato leaves as the experimental subject. In the camera prediction process, the combination of an explicit point cloud generation technique and an implicit 3D Gaussian rendering technique enables the accurate prediction of camera parameters and the effective capture of leaf phenotypic features. In the synthesis of the 3D model of the leaf, a strategy for optimizing the coarse model UV texture is designed with the objective of achieving spatial consistency of texture details. Furthermore, the model was successfully applied to the reconstruction of other crop leaves and lamellar structural objects, and innovatively constructed a leaf reconstruction model with disease characteristics, aiming to provide a reference for the early 3D detection of crop diseases, as well as a reference for the 3D reconstruction and visualization of other lamellar objects. The results demonstrate that the method is effective in reconstructing the morphological structure and texture details of leaves, as well as thin sheet-like structured objects, achieving fast and high-fidelity single-view reconstruction.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109682"},"PeriodicalIF":7.7,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721693","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 : 2024-11-26DOI: 10.1016/j.compag.2024.109679
Wanyuan Huang, Haolin Wang, Wei Dai, Ming Zhang, Dezhi Ren, Wei Wang
An innovative residual film recycling machine for the plough layer (RFRMPL) is proposed in view of difficulty in picking up residual film and the easy missing out on picking up fine residual film. In this study, the soil throwing device is designed and optimized, as the soil throwing efficiency of the throwing device is essential for residual film separation efficiency of the RFRMPL. The soil throwing efficiency is selected as evaluation index, and a mechanical simulation model of throwing device based on Discrete Element Method (DEM) and Rocky is built up according to structure and working principle of the soil throwing device. The optimal combination of working parameters of the throwing device is obtained via theoretical calculations, single and multi-factorial simulation test. The results show that the optimal working parameters of rotation speed of the rotary tilling mechanism, speed of the soil elevating mechanism and the distance between the rotary tilling mechanism and soil elevating mechanism are 200 rpm, 320 rpm and 130 mm respectively. The field validation test is carried out based on the optimal combination parameters. The results show that soil throwing efficiency of the soil throwing device is 87.45 %. The error between the field validation test results and the simulation results (90.42 %) is 3.4 %, which proves the correctness of the simulation model. It can provide theoretical reference for the design and optimization of the RFRMPL.
{"title":"Study on the throwing device of residual film recycling machine for the plough layer","authors":"Wanyuan Huang, Haolin Wang, Wei Dai, Ming Zhang, Dezhi Ren, Wei Wang","doi":"10.1016/j.compag.2024.109679","DOIUrl":"10.1016/j.compag.2024.109679","url":null,"abstract":"<div><div>An innovative residual film recycling machine for the plough layer (RFRMPL) is proposed in view of difficulty in picking up residual film and the easy missing out on picking up fine residual film. In this study, the soil throwing device is designed and optimized, as the soil throwing efficiency of the throwing device is essential for residual film separation efficiency of the RFRMPL. The soil throwing efficiency is selected as evaluation index, and a mechanical simulation model of throwing device based on Discrete Element Method (DEM) and Rocky is built up according to structure and working principle of the soil throwing device. The optimal combination of working parameters of the throwing device is obtained via theoretical calculations, single and multi-factorial simulation test. The results show that the optimal working parameters of rotation speed of the rotary tilling mechanism, speed of the soil elevating mechanism and the distance between the rotary tilling mechanism and soil elevating mechanism are 200 rpm, 320 rpm and 130 mm respectively. The field validation test is carried out based on the optimal combination parameters. The results show that soil throwing efficiency of the soil throwing device is 87.45 %. The error between the field validation test results and the simulation results (90.42 %) is 3.4 %, which proves the correctness of the simulation model. It can provide theoretical reference for the design and optimization of the RFRMPL.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109679"},"PeriodicalIF":7.7,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700045","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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