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Electronic sensing combined with machine learning models for predicting soil nutrient content Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-18 Shuyan Liu, Xuegeng Chen, Xiaomeng Xia, Yvhan Jin, Gang Wang, Honglei Jia, Dongyan Huang
Traditional methods for detecting soil nutrient content usually involve laborious and time-consuming experimental procedures, hindering the efficiency of soil analysis and making them less suitable for large-scale soil testing. Therefore, there exists a pressing need to develop innovative solutions. This study aimed to investigate the potential relationship between soil pyrolysis gas and soil nutrient
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Maize leaf disease recognition based on TC-MRSN model in sustainable agriculture Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-18 Hanming Wang, Xinyao Pan, Yanyan Zhu, Songquan Li, Rongbo Zhu
Maize diseases caused by fungal pathogens are the primary factor resulting in reduced maize yield. However, in practical complex background scenarios, diseases caused by spores, such as gray leaf spot and rust, usually exhibit characteristics including diverse propagation routes, similar lesion appearances at the initial stage of infection, and varying lesion sizes, which raise a challenging task to
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An appetite assessment method for fish in outdoor ponds with anti-shadow disturbance Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-17 Weiqiang Ni, Dan Wei, Zequn Peng, Zhen Ma, Songming Zhu, Rong Tang, Xuelei Tian, Jian Zhao, Zhangying Ye
Accurate quantification of fish appetite can provide effective feeding reference for aquaculture managers. Existing appetite assessment methods are focused on relatively stable indoor or net-cage farming environments, neglecting the interference of complex environmental factors in outdoor ponds on fish appetite assessment. Within the process of gathering video data for the feeding of ponds, the emergence
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Unmanned aerial vehicle-based assessment of rice leaf chlorophyll content dynamics across genotypes Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-17 Qing Gu, Fudeng Huang, Weidong Lou, Yihang Zhu, Hao Hu, Yiying Zhao, Hongkui Zhou, Xiaobin Zhang
Crop breeding programs have long faced the challenge of accurately collecting phenotypic information. The leaf chlorophyll content is an important growth indicator in rice breeding and is generally measured using a portable chlorophyll meter. In this study, a high-resolution RGB camera and a multispectral camera were mounted on unmanned aerial vehicles (UAVs) to obtain images of 216 hybrid rice varieties
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PlantSegNet: 3D point cloud instance segmentation of nearby plant organs with identical semantics Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-17 Ariyan Zarei, Bosheng Li, James C. Schnable, Eric Lyons, Duke Pauli, Kobus Barnard, Bedrich Benes
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Micro-level stress characteristics of rapeseed particle during the seeding process using the MFBD-DEM coupled method Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-17 Wencheng Wu, Xuan Deng, Ju Li, Jianfeng Hu, Hong Cheng, Wei Zhou, Fei Deng, Yong Chen, Wanjun Ren, Xiaolong Lei
Numerical simulation is widely applied in designing seed-metering devices. However, research on the fragility of rapeseed particles and the flexible characteristics of seed-metering device components is relatively limited. A bonded particle model (BPM) was employed to develop an agglomerate model for rapeseed particles, with a deviation of 4.20 % in the crushing force and 11.45 % in the crushing displacement
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Distribution uniformity improvement methods of a large discharge rate disc spreader for UAV fertilizer application Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-17 Wang Xunwei, Zhou Zhiyan, Chen Boqian, Zhong Jinfeng, Fan Xiaolong, Andrew Hewitt
Fertilizer applications from unmanned aerial vehicles (UAVs) have become increasingly common in crop farming. With the rise in UAV capacities for payload and batteries, and increases in application flight speeds, the demands on spreader performance, particularly discharge rate and uniformity, have grown. To address this, a design was developed which incorporates a horizontal screw auger, curved and
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Automatic grain unloading method for track-driven rice combine harvesters based on stereo vision Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-17 Zhihong Cui, Jinpeng Hu, Yang Yu, Guangqiao Cao, Hao Zhang, Xiaoyu Chai, Haiwen Chen, Lizhang Xu
Traditional track-driven rice combine harvesters, during the grain unloading process, often depend on the operator’s frequent adjustment of the grain unloader’s position while closely monitoring the accumulation of grain within the truck. Because of the structural characteristics of the harvester and the narrowness of the rural terrain, visibility is often obstructed, thereby increasing the difficulty
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Precision control system of rice potting and transplanting machine based on GA-Fuzzy PID controller Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-17 Xin Jin, Jing Liu, Zhuo Chen, Mengnan liu, Mingyong li, Zhenghua Xu, Jiangtao Ji
Aiming at the problems of low transverse conveying accuracy and poor speed adaptability in the mechanized transplanting of rice potted seedlings, this study focused on a precision transplanting machine with an independent motor drive and designed a precision control system using a GA-Fuzzy PID controller. Firstly, an interactive interface for human–machine interaction was developed based on kinematic
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Develop agricultural planting structure prediction model based on machine learning: The aging of the population has prompted a shift in the planting structure toward food crops Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-16 Wei Guo, Yimei Huang, Yudan Huang, Yilun Li, Xiaoxiang Song, Jikai Shen, Xiping Qi, Bicheng Zhang, Zhaolong Zhu, Shouzhang Peng, Shaoshan An
Understanding the driving mechanisms and future trends of crop planting structures was crucial for ensuring food security, but research addressing this issue was lacking. This study focused on the crop planting structure in Qinghai Province. The crop structure from 2000 to 2020 was clarified through remote sensing interpretation, and data on farmers and the environment were collected using remote sensing
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Field detection of pests based on adaptive feature fusion and evolutionary neural architecture search Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-16 Yin Ye, Yaxiong Chen, Shengwu Xiong
Accurate detection of pests is vital in smart agriculture as it is among the main factors that profoundly influence the yield and quality of crops. In the actual field, pests frequently manifest as small objects, thereby presenting a considerable obstacle to effectively detect pests in the field. For the problem of ineffective utilization of plant context information and inadequate design of neural
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AI for crop production – Where can large language models (LLMs) provide substantial value? Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-16 Matheus Thomas Kuska, Mirwaes Wahabzada, Stefan Paulus
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Synergizing meat Science and AI: Enhancing long-chain saturated fatty acids prediction Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-14 Jiarui Cui, Yu Lv, Sijia Liu, Shibo Pan, Kenken Li, Shuang Gao, Ruiming Luo, Hao Wu, Zhongxiong Zhang, Songlei Wang
In the field of the food industry, establishing a global predictive model for the content of long-chain saturated fatty acids (LC-SFAs) in red meat is of profound significance. However, this work requires the accumulation of a large number of diverse samples for model calibration. To address these formidable challenges, the Generative Inference Adversarial Autoencoder (GI-AAE) was adopted to enhance
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Automated detection and counting of broiler behaviors using a video recognition system Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-13 Amin Nasiri, Yang Zhao, Hao Gan
Stretching and preening behaviors in broilers are linked to their comfort and overall welfare. These behaviors signal that broilers engage in natural behaviors and physical activity and that the environment is conducive to their needs. By examining the frequency of stretching and preening, valuable information can be gained regarding broilers' physical health and environmental circumstances. This study
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Algorithm and scale experiment of gyro-based tractor rollover control towards hilly farmland application Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-13 Longlong Wang, Jiahui Zhu, Fuhao Liu, Zhizhu He, Qinghui Lai, Zhongxiang Zhu, Zhenghe Song, Zhen Li
Rollover accidents involving off-road vehicles commonly occur during the steering phase on sloping terrain, especially when influenced by complex road conditions. This paper focuses on small-wheeled tractors and proposes an active rollover control method based on a single gimbal control moment gyro (SGCMG). Considering the two rollover stages that occur before and after the unilateral wheels lose contact
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Early detection of rubber tree powdery mildew using UAV-based hyperspectral imagery and deep learning Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-12 Tiwei Zeng, Yong Wang, Yuqi Yang, Qifu Liang, Jihua Fang, Yuan Li, Huiming Zhang, Wei Fu, Juan Wang, Xirui Zhang
Rubber tree powdery mildew(PM) is one of the most critical leaf diseases of rubber trees. The epidemic of this disease can seriously affect natural rubber yields and necessitates timely monitoring, especially in the early stages. In recent years, unmanned aerial vehicle(UAV) hyperspectral imaging technology has been widely used in the field of crop disease identification. Therefore, this paper proposes
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Microscopic FT-IR imaging-based meat and bone meal species discrimination using the screened matrix fragments-related spectral pixels and tableting pretreatment Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-11 Bing Gao, Qingyu Qin, Jiale Liu, Lujia Han, Xian Liu
This study proposes a novel microscopic FT-IR imaging-based analytical strategy for the discrimination of meat and bone meal (MBM) species. The feasibility of correlation coefficient and PLS-DA methods for the screening of bone and meat fragment-related spectral pixels in MBM microscopic FT-IR imaging was validated. Subsequently, PLS-DA models were established for MBM species-specific analysis based
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Improving soil organic carbon estimation in paddy fields using data augmentation algorithm and deep neural network model based on optimal image date Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-10 Chenjie Lin, Zhenhua Liu, Meng Zhang, Zichao Lin, Nan Zhong
Soil organic carbon (SOC) is the largest carbon reservoir in terrestrial ecosystems. Rapid monitoring of SOC is essential for sustainable agricultural management and environmental protection. However, affected by the cost and time of soil sampling, limited datasets remain a main challenge for satellite-driven SOC estimation. Moreover, current studies on satellite-driven SOC estimation in vegetation
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Predicting the growth trajectory and yield of greenhouse strawberries based on knowledge-guided computer vision Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-10 Qi Yang, Licheng Liu, Junxiong Zhou, Mary Rogers, Zhenong Jin
Monitoring and modeling the growth of strawberries at the individual fruit level can open up new opportunities for yield prediction, fruit grading and supply chain optimization. However, existing strawberry growth models mainly focus on plot or plant level and can not simulate the growth of individual fruits, and existing computer vision (CV)-based studies primarily focus on instant tasks but lack
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Prediction of cotton FPAR and construction of defoliation spraying prescription map based on multi-source UAV images Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-10 Lechun Zhang, Binshu Sun, Denan Zhao, Changfeng Shan, Guobin Wang, Cancan Song, Pengchao Chen, Yubin Lan
Efficient and accurate spraying of cotton defoliant is a vital part of cotton production; the traditional way of spraying cotton defoliant will cause waste of pesticides and environmental pollution. The fraction of absorbed photosynthetically active radiation(FPAR) in cotton predicted by multi-source remote sensing information from unmanned aerial vehicles(UAVs) offers the possibility of precise control
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Instance segmentation and stand-scale forest mapping based on UAV images derived RGB and CHM Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-10 Yunhong Xie, Yifu Wang, Zhao Sun, Ruiting Liang, Zhidan Ding, Baoying Wang, Shaodong Huang, Yujun Sun
The tree canopy represents a fundamental element of tree-related information. However, achieving precise canopy information from remote sensing images remains a significant challenge due to varying canopy sizes, mutual overlap, and diverse woodland environments. This study aims to leverage high-resolution Chinese fir images captured by an unmanned aerial vehicle (UAV) from a state forest farm in Jiangle
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Automated collection of facial temperatures in dairy cows via improved UNet Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-10 Hang Shu, Kaiwen Wang, Leifeng Guo, Jérôme Bindelle, Wensheng Wang
In cattle, facial temperatures captured by infrared thermography provide useful information from physiological aspects for researchers and local practitioners. Traditional temperature collection requires massive manual operations on relevant software. Therefore, this paper aimed to propose a tool for automated temperature collection from cattle facial landmarks (i.e., eyes, muzzle, nostrils, ears,
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Reconstruction of missing points in agricultural machinery trajectory based on bidirectional adjacent information Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-09 Weixin Zhai, Xinran Kuang, Xiaoyu Cheng, Jiawen Pan, Caicong Wu
Agricultural machinery trajectory data often encounters the phenomenon of missing trajectory points, and reconstructing these missing trajectory points is crucial for subsequent researches that require complete and high-quality agricultural machinery trajectory data. In this paper, we propose a new method called Fast-TRGRU to accurately and quickly reconstruct missing points in the trajectory of agricultural
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Estimating potato above-ground biomass based on vegetation indices and texture features constructed from sensitive bands of UAV hyperspectral imagery Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-09 Yang Liu, Yiguang Fan, Haikuan Feng, Riqiang Chen, Mingbo Bian, Yanpeng Ma, Jibo Yue, Guijun Yang
Above-ground biomass (AGB) estimation is critical for monitoring crop growth and assessing yields. Unmanned aerial vehicle (UAV) optical remote sensing technology offers robust support for crop AGB estimation through vegetation indices (VIs). However, under conditions of high nitrogen or high AGB, most VIs lose their response to the presence of a dense plant canopy. To address the inaccuracy of estimating
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TSANet: A deep learning framework for the delineation of agricultural fields utilizing satellite image time series Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-09 Shuai Yan, Xiaochuang Yao, Jialin Sun, Weiming Huang, Longshan Yang, Chao Zhang, Bingbo Gao, Jianyu Yang, Wenju Yun, Dehai Zhu
Satellite image time series (SITS), such as Sentinel-2 imagery, plays a crucial role in the delineation of agricultural fields by reducing the impacts of ambiguities due to the spatial arrangement of field boundaries. Existing delineate field parcel models rely extensively on spatial features derived from single-date imagery. However, several studies have exploited the potential of SITS to effectively
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Clustering symptomatic pixels in broomrape-infected carrots facilitates targeted evaluations of alterations in host primary plant traits Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-09 Guy Atsmon, Alireza Pourreza, Yuto Kamiya, Mohsen B. Mesgaran, Fadi Kizel, Hanan Eizenberg, Ran Nisim Lati
In this study, we explore spectral heterogeneity within plant canopies, a characteristic often observed in stressed plants where certain leaves or intra-leaf regions exhibit stress symptoms while others remain unaffected. Considering this variability in spectral signatures holds promise for enhancing remote sensing methodologies aimed at plant stress detection. Typically, remote sensing techniques
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Optimal environment control and fruits delivery tracking system using blockchain for greenhouse Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-09 Atif Rizwan, Anam Nawaz Khan, Muhammad Ibrahim, Rashid Ahmad, Naeem Iqbal, Do Hyeun Kim
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LEI: Livestock Event Information schema for enabling data sharing Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-09 Mahir Habib, Muhammad Ashad Kabir, Lihong Zheng, Shawn McGrath
Data-driven advances have resulted in significant improvements in dairy production. However, the meat industry has lagged behind in adopting data-driven approaches, underscoring the crucial need for data standardisation to facilitate seamless data transmission to maximise productivity, save costs, and increase market access. To address this gap, we propose a novel data schema, Livestock Event Information
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Assessment of the influence of UAV-borne LiDAR scan angle and flight altitude on the estimation of wheat structural metrics with different leaf angle distributions Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-09 Yangyang Gu, Yongqing Wang, Tai Guo, Caili Guo, Xue Wang, Chongya Jiang, Tao Cheng, Yan Zhu, Weixing Cao, Qi Chen, Xia Yao
Effective plant area index (ePAI) and vertical ePAI profile are important metrics in the description of vegetation canopy structure. Rapid, accurate, and high-throughput acquisition of crop ePAI and vertical ePAI profiles using uncrewed aerial vehicle-borne LiDAR (UAV-borne LiDAR) is significant in screening high-yielding crop varieties. However, the influence of the flight altitude and scan angle
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Hybrid attention network for citrus disease identification Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-08 Fukai Zhang, Xiaobo Jin, Gang Lin, Jie Jiang, Mingzhi Wang, Shan An, Junhua Hu, Qiang Lyu
Accurate identification and timely prevention of citrus diseases will effectively protect the interests of the citrus industry. However, the citrus disease identification models currently used in the industry have unsatisfactory performance due to low robustness. In this study, we comprehensively study the problem of citrus disease identification from both data and algorithm perspectives. In order
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A lightweight improved YOLOv5s model and its deployment for detecting pitaya fruits in daytime and nighttime light-supplement environments Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-06 Hongwei Li, Zenan Gu, Deqiang He, Xicheng Wang, Junduan Huang, Yongmei Mo, Peiwei Li, Zhihao Huang, Fengyun Wu
Precise detection and low-cost deployment are the technological basis of intelligent fruit picking. This study proposes a lightweight improved YOLOv5s model to detect pitaya fruits in daytime and nighttime light-supplement environments, and make it successfully deploy in an Android device. This model first uses the module of shufflenetv2 to reconstruct the YOLOv5s backbone network. Then, the study
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Dynamic temperature distribution characteristics of a large glasshouse with cooling system during the start-stop stage Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-06 Qianjun Mao, Chenchen Ji, Hongwei Li, You Peng, Tao Li
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Developing a hybrid convolutional neural network for automatic aphid counting in sugar beet fields Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-06 Xumin Gao, Wenxin Xue, Callum Lennox, Mark Stevens, Junfeng Gao
Aphids can cause direct damage and indirect virus transmission to crops. Timely monitoring and control of their populations are thus critical. However, the manual counting of aphids, which is the most common practice, is labor-intensive and time-consuming. Additionally, two of the biggest challenges in aphid counting are that aphids are small objects and their density distributions are varied in different
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Prediction of soil organic carbon in black soil based on a synergistic scheme from hyperspectral data: Combining fractional-order derivatives and three-dimensional spectral indices Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-06 Jing Geng, Junwei Lv, Jie Pei, Chunhua Liao, Qiuyuan Tan, Tianxing Wang, Huajun Fang, Li Wang
Monitoring soil organic carbon (SOC) content is crucial for climate change mitigation and sustaining ecological balance. Despite the unparalleled advantages of hyperspectral data in capturing nuanced variations in soil properties through its high spectral resolution, effectively extracting useful features from numerous bands via spectral processing techniques remains a formidable challenge. This study
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A review of global precision land-leveling technologies and implements: Current status, challenges and future trends Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-06 Gaolong Chen, Lian Hu, Xiwen Luo, Pei Wang, Jie He, Peikui Huang, Runmao Zhao, Dawen Feng, Tuanpeng Tu
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Morphological estimation of primary branch length of individual apple trees during the deciduous period in modern orchard based on PointNet++ Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-06 Xiaoming Sun, Leilei He, Hanhui Jiang, Rui Li, Wulan Mao, Dong Zhang, Yaqoob Majeed, Nikita Andriyanov, Vladimir Soloviev, Longsheng Fu
Primary branch length is an important morphological trait of individual apple tree phenotypes. This study presents a novel method for estimating the primary branch lengths of individual apple trees during the deciduous period by distinguishing their instances, i.e., merging those belonging to the same primary branch based on part segmentation outputs of PointNet++. Firstly, colored and colorless 3D-datasets
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Udder thermogram-based deep learning approach for mastitis detection in Murrah buffaloes Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-05 S.L. Gayathri, M. Bhakat, T.K. Mohanty, K.K. Chaturvedi, R.R. Kumar, A. Gupta, S. Kumar
Mastitis, a production disease with multiple etiologies, inflicts significant economic losses among dairy farmers around the globe. In this study, an attempt has been made to detect mastitis through a Convolutional Neural Networks (CNN)-based deep learning model using 7615 udder thermograms of 40 Murrah buffaloes. The thermograms were grouped separately as healthy, sub-clinical (SCM), and clinical
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Moving toward smart breeding: A robust amodal segmentation method for occluded Oudemansiella raphanipes cap size estimation Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-05 Hua Yin, Quan Wei, Yang Gao, Haijing Hu, Yinglong Wang
High-throughput acquisition of phenotypic parameters based on machine vision is important for intelligent breeding, digital cultivation and automated harvesting of . However, due to the occlusion among in the growing bed, it is challenging to accurately and rapidly capture their full shape with conventional methods, resulting in a low measurement success rate. The overall goal of this study is to propose
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Development, integration, and field evaluation of an autonomous Agaricus bisporus picking robot Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-05 Ming Zhong, Ruiqing Han, Yan Liu, Bo Huang, Xiujuan Chai, Yaxin Liu
The disorganized and densely growing Agaricus bisporus remains challenging for robotic harvesting. Aiming at the lower efficiency, lower success rate, and higher breakage rate in the robotic harvesting process, we propose a fully integrated, autonomous, and innovative harvesting robot to overcome the challenges due to the dense growth characteristics of Agaricus bisporus. An overlapping target detection
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Rapidly count crop seedling emergence based on waveform Method(WM) using drone imagery at the early stage Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-05 Jie Yuan, Xu Li, Meng Zhou, Hengbiao Zheng, Zhitao Liu, Yang Liu, Ming Wen, Tao Cheng, Weixing Cao, Yan Zhu, Xia Yao
Effectively monitoring seedling emergence is critical to identify missing cotton seedling at early stages, allowing the prompt replenishment of the seedlings to maintain crop yield. However, traditional manual inspections have the limitations with low efficiency, poor timeliness, and large counting errors. Although UAV imagery has been applied in seedling monitoring, there are still chance in cotton
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Design and validation of novel maize grain cleaning loss detection system based on classification models of particle time-domain signals Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-04 Yibo Li, Danielle S Tan, Tao Cui, Hongfei Fan, Yang Xu, Dongxing Zhang, Mengmeng Qiao, Yuxin Hou, Lijian Xiong
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Real-time statistical algorithm for cherry tomatoes with different ripeness based on depth information mapping Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-04 Zhichao Meng, Xiaoqiang Du, Jingyuan Xia, Zenghong Ma, Tianxue Zhang
Rapid and non-destructive automatic statistics of cherry tomatoes at different ripeness stages help better manage resources during harvesting, storage, and transportation processes. Currently, the inspection of cherry tomatoes (ripeness assessment and counting) still faces challenges, such as excluding background cherry tomatoes, detecting heavily obscured ones, and tracking similar feature extraction
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Developing remote sensing- and crop model-based methods to optimize nitrogen management in rice fields Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-04 Dong Wang, Paul C. Struik, Lei Liang, Xinyou Yin
Physiological principles-based crop modelling and sensor technology provide opportunities for smart nitrogen (N) management for sustainable agricultural production. We propose two N-management optimization methods, in which the mathematical ‘bisection algorithm’ is combined either with the crop modelling (CM method) or with an integrated remote sensing-crop modelling by data assimilation (RSCM method)
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Behavior classification and spatiotemporal analysis of grazing sheep using deep learning Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-04 Zhongming Jin, Hang Shu, Tianci Hu, Chengxiang Jiang, Ruirui Yan, Jingwei Qi, Wensheng Wang, Leifeng Guo
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Early detection of infestation by mustard aphid, vegetable thrips and two-spotted spider mite in bok choy with deep neural network (DNN) classification model using hyperspectral imaging data Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-04 Derrick Nguyen, Arinah Tan, Ronjin Lee, Wei Feng Lim, Tin Fat Hui, Fadhlina Suhaimi
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SoybeanNet: Transformer-based convolutional neural network for soybean pod counting from Unmanned Aerial Vehicle (UAV) images Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-04 Jiajia Li, Raju Thada Magar, Dong Chen, Feng Lin, Dechun Wang, Xiang Yin, Weichao Zhuang, Zhaojian Li
Soybean is a critical source of food, protein, and oil, and thus has received extensive research aimed at enhancing their yield, refining cultivation practices, and advancing soybean breeding techniques. Within this context, soybean pod counting plays an essential role in understanding and optimizing production. Despite recent advancements, the development of a robust pod-counting algorithm capable
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Soft bionic gripper with tactile sensing and slip detection for damage-free grasping of fragile fruits and vegetables Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-03 Yuchen Liu, Jintao Zhang, Yuanxin Lou, Baohua Zhang, Jun Zhou, Jiajie Chen
The robot gripper, as an interface for physical-information interaction between agricultural robots and the operating environment, has been widely used in agricultural production. The potential slipping risk during the grasping process is an important factor affecting safe gripping. Therefore, detecting the initial slipping during the gripping process and optimizing the force applied during gripping
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Non-destructive classification of sturgeon stress using cross-modal data fusion and multi-input deep learning models Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-03 Wentao Huang, Yangfeng Wang, Jie Xia, Xinyi Jin, Hongliang Zhu, Branko Glamuzina, Wenyong Yu, Xiaoshuan Zhang
In the aquaculture phase, ensuring the safe transportation of sturgeon is crucial. The stress levels experienced during transit directly impact the fish quality and the economic returns for farmers. To address this, distributors enlist fishery farming experts to evaluate sturgeon stress. Our investigation identified three critical parameters for grading: sturgeon physiological, environmental, and visual
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A Vis/NIR device for detecting moldy apple cores using spectral shape features Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-03 Haoling Liu, Ziyuan Wei, Miao Lu, Pan Gao, Jiangkuo Li, Juan Zhao, Jin Hu
Moldy core is a disease that significantly affects apple yield. However, discriminating slightly moldy cores is a substantial challenge in actual production. In this study, a device for detecting moldy cores in apples is developed. The device is supported by a C12880MA sensor and an STM32F103 microcontroller to detect Vis/NIR signals of apples. Experimentally obtained spectral data of Fuji apples were
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A predictive model for hypocalcaemia in dairy cows utilizing behavioural sensor data combined with deep learning Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-03 Meike van Leerdam, Peter R. Hut, Arno Liseune, Elena Slavco, Jan Hulsen, Miel Hostens
(Sub)clinical hypocalcaemia occurs frequently in the dairy industry, and is one of the earliest symptoms of an impaired transition period. Calcium deficiency is accompanied by changes in cows’ daily behavioural variables, which can be measured by sensors. The goal of this study was to construct a predictive model to identify cows at risk of hypocalcaemia in dairy cows using behavioural sensor data
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Seeding detection and distribution evaluation using the developed automatic maize seeding machine Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-03 Yunxia Li, Zhao Zhang, Afshin Azizi, Muhammad Hilal Kabir, C. Igathinathane, Xiqing Wang, Muhammad Naveed Tahir, Xiongzhe Han, Man Zhang
Seeding is a time-consuming and labour-intensive process in the detection of maize seeds germination rate. Automatic seeding can reduce labour requirements and improve efficiency. The quality of automatic seeding plays an important role in collecting accurate germination rates. In this research, a newly developed automatic maize seeding machine (AMSM) was used to detect the maize seeds and evaluate
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SC-Net: A new strip convolutional network model for rice seedling and weed segmentation in paddy field Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-03 Juan Liao, Minhui Chen, Kai Zhang, Huiyu Zhou, Yu Zou, Wei Xiong, Shun Zhang, Fuming Kuang, Dequan Zhu
Weeds are among the major factors that could harm the yield and quality of rice. Accurately recognizing and localizing crops and weeds are essential for realizing automated weed management in precision agriculture. Semantic segmentation techniques based on deep learning have the capability to automatically discern various types of objects. However, effectively extracting image features to distinguish
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Research on automatic 3D reconstruction of plant phenotype based on Multi-View images Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-01 Danni Yang, Huijun Yang, Dongfeng Liu, Xianlin Wang
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Fast and stable pedicel detection for robust visual servoing to harvest shaking fruits Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-01 Yonghyun Park, Changjo Kim, Hyoung Il Son
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Economically optimal operation of recirculating aquaculture systems under uncertainty Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-04-01 Gabriel D. Patrón, Luis Ricardez-Sandoval
This study presents a novel economical operation scheme for recirculating aquaculture systems (RASs). The proposed approach is comprised of a moving horizon estimator and an economic model predictive controller (EMPC), which, respectively, provide the necessary feedback for the EMPC and make economically optimal decisions for the RAS. The scheme is enabled by a mechanistic RAS model, which is coupled
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SoybeanTracer: An In-Field scene property-based framework for high-throughput soybean canopy coverage extraction and evaluation Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-30 Tianyu Wan, Xiu Jin, Yuan Rao, Jiajia Li, Tan Wang, Zhaohui Jiang, Wu Zhang, Shaowen Li, Tong Zhang, Xiaobo Wang
Soybean is a crucial plant-based protein and vegetable oil source for the global population and a significant cereal-oil dual-purpose crop. Extracting soybean canopy coverage, subsequently implementing evaluation in a low-cost, high-throughput and accurate manner, is highly valuable for soybean breeding and yield increase. Although the utilization of RGB remote sensing images for soybean analysis offers
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WOFOST-N: An improved WOFOST model with nitrogen module for simulation of Korla Fragrant pear tree growth and nitrogen dynamics Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-30 Lichao Xu, Haiqi Liu, Liang Jiang, Feilong Zhang, Xiaoli Li, Xuping Feng, Jianxi Huang, Tiecheng Bai
Few studies have focused on simulating fruit tree growth under fertilization constraints and evaluating nitrogen utilization efficiency. This study aimed to improve the WOFOST (World Food Studies) model by embedding the nitrogen dynamics module into the original WOFOST model (WOFOST-N) to simulate the pear tree growth, nitrogen transport, nitrogen use efficiency and nitrogen stress. In addition, we
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Live chicken body fat measurement technology based on bio-electrical impedance Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-29 Jiaming Zuo, Jinping Liang, Shangshang Cheng, Yuelin Deng, Zhenhui Li, Qinghua Nie, Dexiang Zhang, Xiquan Zhang, Zhen Li, Hongmei Li
In chicken breeding, body fat distribution is an important genetic indicator. To address the issues of low accuracy, high workload, and limited experimental environment in traditional chicken body fat measurement methods, this paper proposes an online measurement method for live chicken body fat based on the principle of bio-electrical impedance. The system uses an STM32F103C8T6 processor, AD5933 impedance
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Simulation and experiment of air-duct heating equipment using electricity in a sheep barn using computational fluid dynamics Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-29 Zichen Liu, Honglei Cen, Min Lu, Jingbin Li, Qiang Cai, Jing Nie, Baoqin Wen, Yalei Xu
The temperature inside the sheep barn has a significant effect on sheep growth; therefore, providing a suitable thermal environment for the barn is essential. Considering the uneven heating problem of existing heating equipment in livestock barns, we designed an air-duct heating equipment using electricity to ensure a comfortable temperature for sheep with a suitable airflow rate. A computational fluid
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AI-enabled IoT-based pest prevention and controlling system using sound analytics in large agricultural field Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-29 Md. Akkas Ali, Rajesh Kumar Dhanaraj, Seifedine Kadry