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}
Pub Date : 2024-11-12DOI: 10.1016/j.compag.2024.109620
Chuan Li , Dongxing Zhang , Li Yang , Tao Cui , Xiantao He , Zhimin Li , Jiaqi Dong , Shulun Xing , Yeyuan Jiang , Jiyuan Liang
Traditional pneumatic seed metering devices rely on air pressure for seed filling and carrying, resulting in high energy consumption and limited seeding speed. While centrifugal seed metering devices can achieve high-speed seeding, they have a narrow optimal seeding speed range, making low-speed seeding difficult. In this study, the number of hole inserts in a high-speed centrifugal precision seed metering device for maize was set to 2, 4, 6, and 8, enabling precision seeding at higher speeds and across a broader speed range. Different agitator wheel structures were designed based on the number of hole inserts. The motion characteristics of the gas and seeds were analyzed using a combination of Discrete Element Method and Computational Fluid Dynamics to determine the optimal agitator wheel structure. Bench test results indicated that the optimal seeding speed ranges for 2, 4, 6, and 8 hole inserts were 6–9 km/h, 12–18 km/h, 18–27 km/h, and 24–36 km/h, respectively. With 8 hole inserts, the maximum seeding speed reached 36 km/h, achieving a miss rate of 2.75 %, a repeat rate of 3.76 %, and a qualification rate of 93.49 %. The energy consumption of the high-speed centrifugal maize precision seed metering device during seeding was less than 411.71 kJ/ha, which is less than 9 % of the energy consumed per hectare by pneumatic seed metering devices. Additionally, the higher the seeding speed, the lower the energy consumption per hectare. At a seeding speed of 36 km/h, the energy consumption was 90.08 kJ/ha. Compared to pneumatic seed metering devices, the high-speed centrifugal maize precision seed metering device offers higher seeding speeds and lower energy consumption, enabling high-speed and clean production.
{"title":"Research on high-speed and clean production with a high-speed centrifugal maize precision seed metering device featuring variable hole insert numbers","authors":"Chuan Li , Dongxing Zhang , Li Yang , Tao Cui , Xiantao He , Zhimin Li , Jiaqi Dong , Shulun Xing , Yeyuan Jiang , Jiyuan Liang","doi":"10.1016/j.compag.2024.109620","DOIUrl":"10.1016/j.compag.2024.109620","url":null,"abstract":"<div><div>Traditional pneumatic seed metering devices rely on air pressure for seed filling and carrying, resulting in high energy consumption and limited seeding speed. While centrifugal seed metering devices can achieve high-speed seeding, they have a narrow optimal seeding speed range, making low-speed seeding difficult. In this study, the number of hole inserts in a high-speed centrifugal precision seed metering device for maize was set to 2, 4, 6, and 8, enabling precision seeding at higher speeds and across a broader speed range. Different agitator wheel structures were designed based on the number of hole inserts. The motion characteristics of the gas and seeds were analyzed using a combination of Discrete Element Method and Computational Fluid Dynamics to determine the optimal agitator wheel structure. Bench test results indicated that the optimal seeding speed ranges for 2, 4, 6, and 8 hole inserts were 6–9 km/h, 12–18 km/h, 18–27 km/h, and 24–36 km/h, respectively. With 8 hole inserts, the maximum seeding speed reached 36 km/h, achieving a miss rate of 2.75 %, a repeat rate of 3.76 %, and a qualification rate of 93.49 %. The energy consumption of the high-speed centrifugal maize precision seed metering device during seeding was less than 411.71 kJ/ha, which is less than 9 % of the energy consumed per hectare by pneumatic seed metering devices. Additionally, the higher the seeding speed, the lower the energy consumption per hectare. At a seeding speed of 36 km/h, the energy consumption was 90.08 kJ/ha. Compared to pneumatic seed metering devices, the high-speed centrifugal maize precision seed metering device offers higher seeding speeds and lower energy consumption, enabling high-speed and clean production.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109620"},"PeriodicalIF":7.7,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661764","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 : 2024-11-12DOI: 10.1016/j.compag.2024.109567
Peifeng Ma , Aibin Zhu , Yihao Chen , Yao Tu , Han Mao , Jiyuan Song , Xin Wang , Sheng Su , Dangchao Li , Xia Dong
The primary challenge for fruit-harvesting robots in unstructured orchard environments lies in achieving fast and accurate fruit picking while avoiding obstacles like branches. This paper introduces a rapid and efficient multi-objective motion planning method based on the improved BIT* algorithm. Two depth cameras are employed to acquire the locations of both targets and obstacles, and an obstacle map of the harvesting environment is generated using the octree method. For collision detection, a combination of bounding box and grid-based techniques is applied. The proposed bidirectional BIT* (Bi-BIT*) algorithm builds forward and backward trees simultaneously during initialization, alternating searches to reduce the time required for the initial solution. The manipulator’s joint paths are interpolated using a quintic polynomial, and a multi-objective optimization problem is solved to achieve a smooth joint motion trajectory while minimizing energy consumption and pulsation. Both two-dimensional and three-dimensional simulations demonstrate that the Bi-BIT* algorithm consistently outperforms three other algorithms, achieving the highest overall scores. In the harvesting experiment of Scenario 1, the Bi-BIT* algorithm had an average execution time of 7.32 s—36.4% faster than the Informed RRT* algorithm, 19.0% faster than the RRT-Connect algorithm, and 28.7% faster than the BIT* algorithm. Additionally, the Bi-BIT* algorithm achieved a 96% planning success rate and an 84% execution success rate, surpassing the other three algorithms. In Experiment Scenario 2, the Bi-BIT* algorithm had an average execution time of 8.59 s, which is 41.0% faster than the Informed RRT* algorithm, 6.3% faster than the RRT-Connect algorithm, and 19.5% faster than the BIT* algorithm. Furthermore, the Bi-BIT* algorithm demonstrated superior planning and execution success rates of 92% and 88%, respectively, compared to the other algorithms. These experimental results confirm that the proposed multi-objective motion planning method enables the harvesting manipulator to avoid obstacles efficiently and accurately, completing the harvesting task with high performance.
{"title":"Multi objective motion planning of fruit harvesting manipulator based on improved BIT* algorithm","authors":"Peifeng Ma , Aibin Zhu , Yihao Chen , Yao Tu , Han Mao , Jiyuan Song , Xin Wang , Sheng Su , Dangchao Li , Xia Dong","doi":"10.1016/j.compag.2024.109567","DOIUrl":"10.1016/j.compag.2024.109567","url":null,"abstract":"<div><div>The primary challenge for fruit-harvesting robots in unstructured orchard environments lies in achieving fast and accurate fruit picking while avoiding obstacles like branches. This paper introduces a rapid and efficient multi-objective motion planning method based on the improved BIT* algorithm. Two depth cameras are employed to acquire the locations of both targets and obstacles, and an obstacle map of the harvesting environment is generated using the octree method. For collision detection, a combination of bounding box and grid-based techniques is applied. The proposed bidirectional BIT* (Bi-BIT*) algorithm builds forward and backward trees simultaneously during initialization, alternating searches to reduce the time required for the initial solution. The manipulator’s joint paths are interpolated using a quintic polynomial, and a multi-objective optimization problem is solved to achieve a smooth joint motion trajectory while minimizing energy consumption and pulsation. Both two-dimensional and three-dimensional simulations demonstrate that the Bi-BIT* algorithm consistently outperforms three other algorithms, achieving the highest overall scores. In the harvesting experiment of Scenario 1, the Bi-BIT* algorithm had an average execution time of 7.32 s—36.4% faster than the Informed RRT* algorithm, 19.0% faster than the RRT-Connect algorithm, and 28.7% faster than the BIT* algorithm. Additionally, the Bi-BIT* algorithm achieved a 96% planning success rate and an 84% execution success rate, surpassing the other three algorithms. In Experiment Scenario 2, the Bi-BIT* algorithm had an average execution time of 8.59 s, which is 41.0% faster than the Informed RRT* algorithm, 6.3% faster than the RRT-Connect algorithm, and 19.5% faster than the BIT* algorithm. Furthermore, the Bi-BIT* algorithm demonstrated superior planning and execution success rates of 92% and 88%, respectively, compared to the other algorithms. These experimental results confirm that the proposed multi-objective motion planning method enables the harvesting manipulator to avoid obstacles efficiently and accurately, completing the harvesting task with high performance.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109567"},"PeriodicalIF":7.7,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658569","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 : 2024-11-12DOI: 10.1016/j.compag.2024.109610
Ernesto Grande , Raffaella Franceschini
In-situ tests and numerical models represent valuable tools for deriving the main dynamic characteristics of trees and for studying their response to dynamic actions. Regarding the numerical models, a key aspect is their calibration. Most procedures available in the literature generally suggest the use of a significant number of instruments (accelerometers placed on both the trunk and branches), which results in high costs and is time-consuming. The aim of this paper is to propose a two-phase approach to calibrate multiple mass-spring-damper systems for studying the dynamics of trees. The proposal aims to support the monitoring and stability assessment of trees through an efficient procedure that combines techniques and methods derived from the field of structural dynamics. Some of these techniques are already used for trees, while others are newly applied in this context. In particular, the experimental data deduced from pull-release tests performed using a single accelerometer placed only on the trunk are assumed as the input data for the approach. The approach is presented in the first part of the paper. In the second part, the approach is implemented in the computer code Matlab to validate it with reference to both numerical models and real tree cases. Finally, a user-friendly graphical application of the approach is developed to make it a practical and expedient tool for researchers and practitioners, allowing real-time evaluation of the dynamics of trees, conducted simultaneously with in-situ tests.
{"title":"Calibration of mass-spring-damper equivalent systems for real time assessment of the dynamics of trees","authors":"Ernesto Grande , Raffaella Franceschini","doi":"10.1016/j.compag.2024.109610","DOIUrl":"10.1016/j.compag.2024.109610","url":null,"abstract":"<div><div>In-situ tests and numerical models represent valuable tools for deriving the main dynamic characteristics of trees and for studying their response to dynamic actions. Regarding the numerical models, a key aspect is their calibration. Most procedures available in the literature generally suggest the use of a significant number of instruments (accelerometers placed on both the trunk and branches), which results in high costs and is time-consuming. The aim of this paper is to propose a two-phase approach to calibrate multiple mass-spring-damper systems for studying the dynamics of trees. The proposal aims to support the monitoring and stability assessment of trees through an efficient procedure that combines techniques and methods derived from the field of structural dynamics. Some of these techniques are already used for trees, while others are newly applied in this context. In particular, the experimental data deduced from pull-release tests performed using a single accelerometer placed only on the trunk are assumed as the input data for the approach. The approach is presented in the first part of the paper. In the second part, the approach is implemented in the computer code Matlab to validate it with reference to both numerical models and real tree cases. Finally, a user-friendly graphical application of the approach is developed to make it a practical and expedient tool for researchers and practitioners, allowing real-time evaluation of the dynamics of trees, conducted simultaneously with in-situ tests.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109610"},"PeriodicalIF":7.7,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661766","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 : 2024-11-12DOI: 10.1016/j.compag.2024.109644
Xiaofei Kuang, Zhe Zhu, Jiao Guo, Shiyu Xiang
Wheat moisture content is a critical indicator for evaluating quality. The microwave free space measurement method can achieve nondestructive and efficient measurement of wheat moisture. Regarding microwave detection technology for wheat moisture content, further validation is needed for establishing a prediction model using multi-frequency and full-frequency data within a specific band. Due to the excellent penetration capability of microwaves in the L and S bands, this study explores the potential of utilizing multi-frequency and full-frequency signals in these bands to develop a prediction system for wheat water content. The paper analyzes the relationship between different microwave frequencies, temperatures, moisture contents, and bulk densities on dielectric properties. Temperature, bulk density, and dielectric properties serve as characteristic parameters for the regression model, and a moisture prediction model incorporating single frequency, multi-frequency, and full-frequency data is established. The moisture content detection model integrates three regression methods: Partial Least Squares (PLS), Support Vector Regression (SVR), and Extreme Learning Machine (ELM). Results show that among the nine different prediction models, the SVR model under full-frequency conditions performs the best. The correlation coefficient, root mean square error, and residual prediction bias for moisture prediction on the validation set are 0.9838, 0.3511%, and 6.3245, respectively. To enable online detection of wheat moisture content, a low-cost frequency modulated continuous wave (FMCW) detection system was designed based on the optimal prediction model. Experiments have confirmed that within the moisture content range of 11.35% to 17.79%, the average determination coefficient between the moisture content obtained through drying methods and the measurement results from the FMCW system can reach 0.9493. These endeavors have the potential to provide reliable and cost-effective solutions for precision agriculture applications.
小麦水分含量是评价质量的一个重要指标。微波自由空间测量方法可以实现小麦水分的无损和高效测量。关于小麦水分含量的微波检测技术，需要进一步验证，以便利用特定波段内的多频和全频数据建立预测模型。由于 L 波段和 S 波段的微波具有出色的穿透能力，本研究探讨了利用这些波段的多频和全频信号开发小麦含水量预测系统的潜力。本文分析了不同微波频率、温度、含水量和容重对介电性质的影响。温度、容重和介电性质作为回归模型的特征参数，建立了一个包含单频、多频和全频数据的水分预测模型。含水率检测模型集成了三种回归方法：部分最小二乘法（PLS）、支持向量回归法（SVR）和极限学习机（ELM）。结果表明，在九种不同的预测模型中，全频条件下的 SVR 模型表现最佳。验证集上水分预测的相关系数、均方根误差和剩余预测偏差分别为 0.9838、0.3511% 和 6.3245。为了实现小麦水分含量的在线检测，根据最优预测模型设计了一种低成本的频率调制连续波（FMCW）检测系统。实验证实，在 11.35% 至 17.79% 的水分含量范围内，通过干燥方法获得的水分含量与 FMCW 系统测量结果之间的平均确定系数可达 0.9493。这些努力有可能为精准农业应用提供可靠且经济高效的解决方案。
{"title":"Non-destructive detection of wheat moisture content with frequency modulated continuous wave system under L and S bands","authors":"Xiaofei Kuang, Zhe Zhu, Jiao Guo, Shiyu Xiang","doi":"10.1016/j.compag.2024.109644","DOIUrl":"10.1016/j.compag.2024.109644","url":null,"abstract":"<div><div>Wheat moisture content is a critical indicator for evaluating quality. The microwave free space measurement method can achieve nondestructive and efficient measurement of wheat moisture. Regarding microwave detection technology for wheat moisture content, further validation is needed for establishing a prediction model using multi-frequency and full-frequency data within a specific band. Due to the excellent penetration capability of microwaves in the L and S bands, this study explores the potential of utilizing multi-frequency and full-frequency signals in these bands to develop a prediction system for wheat water content. The paper analyzes the relationship between different microwave frequencies, temperatures, moisture contents, and bulk densities on dielectric properties. Temperature, bulk density, and dielectric properties serve as characteristic parameters for the regression model, and a moisture prediction model incorporating single frequency, multi-frequency, and full-frequency data is established. The moisture content detection model integrates three regression methods: Partial Least Squares (PLS), Support Vector Regression (SVR), and Extreme Learning Machine (ELM). Results show that among the nine different prediction models, the SVR model under full-frequency conditions performs the best. The correlation coefficient, root mean square error, and residual prediction bias for moisture prediction on the validation set are 0.9838, 0.3511%, and 6.3245, respectively. To enable online detection of wheat moisture content, a low-cost frequency modulated continuous wave (FMCW) detection system was designed based on the optimal prediction model. Experiments have confirmed that within the moisture content range of 11.35% to 17.79%, the average determination coefficient between the moisture content obtained through drying methods and the measurement results from the FMCW system can reach 0.9493. These endeavors have the potential to provide reliable and cost-effective solutions for precision agriculture applications.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109644"},"PeriodicalIF":7.7,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661776","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 : 2024-11-12DOI: 10.1016/j.compag.2024.109553
Zhang Yongnian , Chen Yinhe , Bao Yihua , Wang Xiaochan , Xian Jieyu
This paper proposes a method for detecting tomato maturity to address issues related to post-harvest storage and transportation. The method utilizes bioimpedance spectroscopy to study tomato maturity, construct the Double-R-Cole equivalent circuit model, and obtain electrical parameters through fitting the Levenberg-Marquardt optimization algorithm. We analyze the change rule of electrical parameters in different ripening periods, use Fisher’s discriminant to reduce the dimensionality of features such as biological variables, fitted electrical parameters, and storage days of tomato, and combine the advantages of support vector machine and random forest to classify the input features. The classification algorithm utilizes the gorilla troop optimization algorithm to address issues with traditional iterative algorithms, such as difficulty assigning initial values and susceptibility to local optima. The study finds that: the Levenberg-Marquardt algorithm fitted an R^2 mean value of 0.997 and the significance level of p < 0.001 between the two constant-phase components of the fitted electrical parameters and the number of storage days proved that the established Double-R-Cole model could effectively characterize the postharvest situation of tomato; the Fisher’s discriminant based SVM-RF-GTO’s maturity classification algorithm achieves 97.26 % effectiveness in tomato maturity classification. This research provides valuable insights for tomato postharvest storage and transportation.
{"title":"Tomato maturity detection based on bioelectrical impedance spectroscopy","authors":"Zhang Yongnian , Chen Yinhe , Bao Yihua , Wang Xiaochan , Xian Jieyu","doi":"10.1016/j.compag.2024.109553","DOIUrl":"10.1016/j.compag.2024.109553","url":null,"abstract":"<div><div>This paper proposes a method for detecting tomato maturity to address issues related to post-harvest storage and transportation. The method utilizes bioimpedance spectroscopy to study tomato maturity, construct the Double-R-Cole equivalent circuit model, and obtain electrical parameters through fitting the Levenberg-Marquardt optimization algorithm. We analyze the change rule of electrical parameters in different ripening periods, use Fisher’s discriminant to reduce the dimensionality of features such as biological variables, fitted electrical parameters, and storage days of tomato, and combine the advantages of support vector machine and random forest to classify the input features. The classification algorithm utilizes the gorilla troop optimization algorithm to address issues with traditional iterative algorithms, such as difficulty assigning initial values and susceptibility to local optima. The study finds that: the Levenberg-Marquardt algorithm fitted an R^2 mean value of 0.997 and the significance level of <em>p</em> < 0.001 between the two constant-phase components of the fitted electrical parameters and the number of storage days proved that the established Double-R-Cole model could effectively characterize the postharvest situation of tomato; the Fisher’s discriminant based SVM-RF-GTO’s maturity classification algorithm achieves 97.26 % effectiveness in tomato maturity classification. This research provides valuable insights for tomato postharvest storage and transportation.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109553"},"PeriodicalIF":7.7,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661779","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 : 2024-11-11DOI: 10.1016/j.compag.2024.109593
Mansoor Ahmad Kirmani, Yasir Afaq
Apple tree leaf diseases (ATLDs) can be accurately identified and addressed early to prevent the diseases from spreading, minimize the need for chemical pesticides and fertilizers, increase apple quality and production, and preserve the healthy growth of apple varieties. To overcome such challenges, different Deep Learning (DL) approaches have been developed to early detect apple leaf diseases. In this paper, the data from 2010 to 2024 has been taken for analysis, and it has been observed that many of the researchers have utilized different types of datasets for disease detection. Moreover, Deep Learning (DL) and Machine Learning (ML) have been mostly utilized for the detection and identification of apple leaf Alternaria diseases. It has also been observed from the previous work that Support Vector Machines (SVM), Random Forests (RF), XGBoost, and many more are the most common approaches utilized by the researchers. On the other hand, DenseNet, MobileNet, Convolutional Neural Network (CNN), and Vision Transformer are the deep learning approaches utilized by the researchers. Furthermore, we have also given a brief analysis of each approach along with a comparative analysis such as lightweight CNNs and Attention-based mechanisms, Transfer Learning (TL), Localization techniques, Vision Transformer (ViT), and Severity estimation techniques. Emphasizing their methods, datasets, performance metrics, and real-world applications. This study explores the proposed models’ approaches, feature selection and extraction techniques, data capturing conditions, accuracy, types of datasets used in the experiments, and their resources. Our research findings indicate that although DL approaches have significant potential for improving disease management in agriculture. There is a crucial need for a more scalable, robust, and flexible solution to handle numerous agricultural conditions and disease complexities. By methodically and comprehensively analyzing the collected data, this study aims to facilitate valuable resources for researchers aiming to design, develop, and implement DL-based systems for apple leaf disease detection and identification, ultimately contributing to sustainable agriculture and improved food security.
{"title":"Developments in deep learning approaches for apple leaf Alternaria disease identification: A review","authors":"Mansoor Ahmad Kirmani, Yasir Afaq","doi":"10.1016/j.compag.2024.109593","DOIUrl":"10.1016/j.compag.2024.109593","url":null,"abstract":"<div><div>Apple tree leaf diseases (ATLDs) can be accurately identified and addressed early to prevent the diseases from spreading, minimize the need for chemical pesticides and fertilizers, increase apple quality and production, and preserve the healthy growth of apple varieties. To overcome such challenges, different Deep Learning (DL) approaches have been developed to early detect apple leaf diseases. In this paper, the data from 2010 to 2024 has been taken for analysis, and it has been observed that many of the researchers have utilized different types of datasets for disease detection. Moreover, Deep Learning (DL) and Machine Learning (ML) have been mostly utilized for the detection and identification of apple leaf Alternaria diseases. It has also been observed from the previous work that Support Vector Machines (SVM), Random Forests (RF), XGBoost, and many more are the most common approaches utilized by the researchers. On the other hand, DenseNet, MobileNet, Convolutional Neural Network (CNN), and Vision Transformer are the deep learning approaches utilized by the researchers. Furthermore, we have also given a brief analysis of each approach along with a comparative analysis such as lightweight CNNs and Attention-based mechanisms, Transfer Learning (TL), Localization techniques, Vision Transformer (ViT), and Severity estimation techniques. Emphasizing their methods, datasets, performance metrics, and real-world applications. This study explores the proposed models’ approaches, feature selection and extraction techniques, data capturing conditions, accuracy, types of datasets used in the experiments, and their resources. Our research findings indicate that although DL approaches have significant potential for improving disease management in agriculture. There is a crucial need for a more scalable, robust, and flexible solution to handle numerous agricultural conditions and disease complexities. By methodically and comprehensively analyzing the collected data, this study aims to facilitate valuable resources for researchers aiming to design, develop, and implement DL-based systems for apple leaf disease detection and identification, ultimately contributing to sustainable agriculture and improved food security.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109593"},"PeriodicalIF":7.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661364","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 : 2024-11-10DOI: 10.1016/j.compag.2024.109623
Jianjun Guo , Beibei Zhang , Lijun Lin , Yudian Xu , Piao Zhou , Shangwen Luo , Yuhan Zhuo , Jingyu Ji , Zhijie Luo , Shahbaz Gul Hassan
With the rapid development of greenhouse agriculture, accurate prediction of environmental parameters such as temperature, humidity, and carbon dioxide concentration is crucial for optimal crop growth. Traditional forecasting models struggle with the nonlinear and complex nature of greenhouse data, leading to challenges in model robustness. This study addresses these issues by proposing a multi-model fusion strategy for predicting CO2 concentration in greenhouse tomatoes. The proposed method integrates wavelet denoising (WT), variational mode decomposition (VMD), and long short-term memory networks (LSTM). This innovative nonlinear ensemble model effectively extracts key time series features and removes noise, while an introduced attention mechanism enhances the model’s focus on essential time steps, improving prediction accuracy. Experimental results demonstrate that the multi-model fusion approach significantly outperforms single models in terms of accuracy and stability, achieving mean absolute error (MAE) and root mean square error (RMSE) of 0.0117 and 0.0194, respectively. The proposed method offers significant advantages for CO2 prediction in greenhouse crops, providing a theoretical basis and technical support for optimizing and controlling greenhouse parameters. This contributes to the advancement of smart agriculture by offering an efficient environmental monitoring and prediction tool. Additionally, the study presents new ideas and technical solutions for addressing similar agricultural environment prediction challenges, optimizing greenhouse environment control strategies, and improving crop production efficiency.
{"title":"Multi-model fusion method for predicting CO2 concentration in greenhouse tomatoes","authors":"Jianjun Guo , Beibei Zhang , Lijun Lin , Yudian Xu , Piao Zhou , Shangwen Luo , Yuhan Zhuo , Jingyu Ji , Zhijie Luo , Shahbaz Gul Hassan","doi":"10.1016/j.compag.2024.109623","DOIUrl":"10.1016/j.compag.2024.109623","url":null,"abstract":"<div><div>With the rapid development of greenhouse agriculture, accurate prediction of environmental parameters such as temperature, humidity, and carbon dioxide concentration is crucial for optimal crop growth. Traditional forecasting models struggle with the nonlinear and complex nature of greenhouse data, leading to challenges in model robustness. This study addresses these issues by proposing a multi-model fusion strategy for predicting CO2 concentration in greenhouse tomatoes. The proposed method integrates wavelet denoising (WT), variational mode decomposition (VMD), and long short-term memory networks (LSTM). This innovative nonlinear ensemble model effectively extracts key time series features and removes noise, while an introduced attention mechanism enhances the model’s focus on essential time steps, improving prediction accuracy. Experimental results demonstrate that the multi-model fusion approach significantly outperforms single models in terms of accuracy and stability, achieving mean absolute error (MAE) and root mean square error (RMSE) of 0.0117 and 0.0194, respectively. The proposed method offers significant advantages for CO2 prediction in greenhouse crops, providing a theoretical basis and technical support for optimizing and controlling greenhouse parameters. This contributes to the advancement of smart agriculture by offering an efficient environmental monitoring and prediction tool. Additionally, the study presents new ideas and technical solutions for addressing similar agricultural environment prediction challenges, optimizing greenhouse environment control strategies, and improving crop production efficiency.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109623"},"PeriodicalIF":7.7,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661767","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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