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Assessing radiometric calibration methods for multispectral UAV imagery and the influence of illumination, flight altitude and flight time on reflectance, vegetation index and inversion of winter wheat AGB and LAI Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-13 Honglei Zhu, Yanwei Huang, Zhaokang An, Han Zhang, Yongyue Han, Zihui Zhao, Feifan Li, Chan Zhang, Cuicui Hou
Radiometric correction is essential to unmanned aerial vehicle (UAV) precision agriculture applications, especially for crop multi-period analysis and quantitative inversion of phenotypic and physiological parameters. In this study, we investigated the performance of four radiometric correction methods: “Camera only” (A), “Camera and sun irradiance” (B) and “Camera, sun irradiance and sun angle” (C)
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Digitalization in agriculture. Towards an integrative approach Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-13 A.J. Romera, M. Sharifi, S. Charters
Farm systems around the world have become less complex, dominated by monocultures, but at the same time much more complicated due to the increasing demands from society, leading to ever increasing compliance and regulatory requirements. Digital technologies have the potential to help alleviate such complications by virtue of their capacity to handle large quantities of information and by automating
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Prototyping and evaluation of a novel machine vision system for real-time, automated quality grading of sweetpotatoes Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-12 Jiajun Xu, Yuzhen Lu
Rapid advances in machine vision and artificial intelligence (AI) technologies have generated high interest in developing automated grading and sorting solutions for fresh produce of specialty crops. Sweetpotatoes are an economically important crop in the United States and beyond, but grading and sorting the commodity, especially for surface defects, remains a labor-intensive operation at commercial
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On-animal sensors may predict paddock level pasture mass in rotationally grazed dairy systems Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-12 J.P. Edwards, M. Qasim, R.H. Bryant, C. Thomas, C. Wright-Watson, G. Zobel, M.B. Neal, C.R. Eastwood
Precision livestock farming aims to improve animal welfare and farm management using digital technology. We investigated the potential of individual on-animal sensors to predict paddock-level pasture mass, an important metric for grazing management in pasture-based dairy systems. The study consisted of four groups of 25 cows assigned to different pasture allocations (ranging from an estimated 80% to
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Remote sensing of soil ridge height to visualize windbreak effectiveness in wind erosion control: A strategy for sustainable agriculture Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-12 Kenta Iwasaki, Seiji Shimoda, Yasutaka Nakata, Masato Hayamizu, Kazuki Nanko, Hiroyuki Torita
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Design and experiment of an adaptive cruise weeding robot for paddy fields based on improved YOLOv5 Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-11 Jinyan Ju, Guoqing Chen, Zhenyang Lv, Minyi Zhao, Liang Sun, Zhentao Wang, Jinfeng Wang
Weed control in paddy fields is a critical agronomic practice for enhancing rice yield, in which mechanical weed control is widely used due to its high weed control rate and convenient operation. However, traditional mechanical weed control methods require manual operation, leading to increased operational costs. Therefore, adaptive cruise weeding robots hold significant application promise and market
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A new bi-level mathematical model for government-farmer interaction regarding food security and environmental damages of pesticides and fertilizers: Case study of rice supply chain in Iran Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-11 Mohammad Jalal Kazemi, Parvaneh Samouei
As an important crop, rural farmers mainly plant rice on small agricultural lands. The rice supply chain provides specific features in Agricultural Supply Chain due to high demand, product diversity, and wide production area. Therefore, it is one of the most significant policymaking challenges to make a balance between the demand of different members and stakeholders of the rice supply chain. The extant
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High-throughput measurement method for rice seedling based on improved UNet model Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-11 Sicheng Liu, Ze Huang, Zhihui Xu, Fujun Zhao, Dongliang Xiong, Shaobing Peng, Jianliang Huang
Seedling growth is essential for the subsequent development and yield formation. Monitoring the status of seedlings is essential for plant factories and population assessments. However, current methods lack the ability to rapidly, efficiently, and concurrently evaluate the characteristics of whole seedlings. In this study, we have developed a high-throughput measurement method () for evaluating rice
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Mapping sustainability-oriented China’s cropland use stability Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-10 Xinyuan Liang, Xiaobin Jin, Yue Dou, Xiaolin Zhang, Hanbing Li, Shilei Wang, Fei Meng, Shaojun Tan, Yinkang Zhou
Shifting to more sustainable use can curb cropland resource degradation and improve production resilience. However, most cropland use assessments are unilaterally focused on biophysical levels and productivity, ignoring the multi-dimensional aspects of degraded cropland. This study attempts to describe cropland use stability in China by proposing a multi-dimensional assessment framework (including
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Design and optimization of the seed feeding device with DEM-CFD coupling approach for rice and wheat Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-10 Siyu He, Cheng Qian, Youcong Jiang, Wei Qin, Zishun Huang, Daming Huang, Zaiman Wang, Ying Zang
In order to achieve uniform and stable seed supply over a large sowing volume range of seed supplies in an air-assisted centralized metering system for rice and wheat, the study designed a seed supply unit with an equal-width inclined inlet based on the Venturi principle. The parameters of the seed feeding device were determined through theoretical calculation, and the DEM-CFD gas–solid coupling method
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Estimation of the height profile of the path for autonomous driving in terrain Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-10 Tabish Badar, Issouf Ouattara, Juha Backman, Arto Visala
A priori knowledge about the height profile of the path is vital for rollover avoidance in the context of autonomous driving through uneven forest ground. The forest ground is usually covered with either soft vegetation in summertime, or by snow in winter. Thus, the exact solid form of the forest ground cannot be detected by camera or LiDAR. This article, we propose height-odometry and aided height-odometry
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A Hybrid Model that Combines Machine Learning and Mechanistic Models for Useful Grass Growth Prediction Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-09 Eoin M. Kenny, Elodie Ruelle, Mark T. Keane, Laurence Shalloo
Recently, Machine Learning (ML) has been heralded as a panacea for modelling problems across many domains, including Smart Agriculture (SmartAg), often in opposition to traditional mechanistic models arising on decades of scientific discovery. However, mechanistic models are often successful in “real world” problem-domains where ML models encounter difficulties (e.g., where the distribution of test
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Human-exoskeleton cooperation for reducing the musculoskeletal load of manual handling tasks in orchid farms Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-08 Dang Khanh Linh Le, Wei-Chih Lin
Work-related musculoskeletal diseases of the lower back and shoulders are frequently caused by lifting, carrying and trunk bending. Therefore, a hybrid exoskeleton (HExo) that provides external torque to the upper extremities and back may reduce discomfort in the lower back and shoulder tendinopathies. In this study, we designed and fabricated HExo, a novel upper-limb and lower-back combined exoskeleton
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A novel framework combining band selection algorithm and improved 3D prototypical network for tree species classification using airborne hyperspectral images Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-08 Jing Wu, Long Chen, Jiaqi Wang, Yunfan Li, Erxue Chen, Xiaoli Zhang
Fine-grained identification of forest types and tree species represents a critical aspect of forest resource inventory and monitoring. The use of airborne hyperspectral remote sensing imagery stands out for its ability to finely differentiate among tree species, leveraging its exceptional spatial resolution and rich spectral details. However, this approach is limited by several challenges (e.g., high
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Research on torque cooperative control of distributed drive system for fuel cell electric tractor Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-07 Xianzhe Li, Liyou Xu, Mengnan Liu, Xianghai Yan, Mingzhu Zhang
Fuel cell distributed drive electric tractor (FCDET) is one of the effective ways to solve environmental pollution, energy shortage problems and promote sustainable development of agriculture. Excessive slip of the driving wheels is the main reason that restricts the traction efficiency of electric tractors. This paper proposed a cooperative control strategy for fuel cell tractor distributed drive
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Novel intelligent grazing strategy based on remote sensing, herd perception and UAVs monitoring Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-07 Tao Chen, Han Zheng, Jian Chen, Zichao Zhang, Xinhang Huang
Intelligent grazing is a new livestock husbandry development mode based on obtaining the multi-source information, ecological balance is the goal. This paper focuses on intelligent grazing, reviews from grass remote sensing and aerial seeding, unmanned aerial vehicles (UAVs) monitoring and intelligent grazing robot, and summarizing the development of intelligent grazing elements at this stage, exploring
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Towards operational UAV-based forest health monitoring: Species identification and crown condition assessment by means of deep learning Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-06 Simon Ecke, Florian Stehr, Julian Frey, Dirk Tiede, Jan Dempewolf, Hans-Joachim Klemmt, Ewald Endres, Thomas Seifert
Uncrewed Aerial Vehicles (UAVs) have emerged as a promising tool for complementing terrestrial surveys, offering unique advantages for forest health monitoring (FHM). UAVs have the potential to improve or even replace core tasks such as crown condition assessment, bridging the gap between ground-based surveys and traditional remote sensing platforms. However, present approaches have not yet fully exploited
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Analysis of pig activity level and body temperature variation based on ear tag data Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-06 Yigui Huang, Deqin Xiao, Junbin Liu, Youfu Liu, Zujie Tan, Xiangyang Hui, Senpeng Huang
The behavioral and physiological patterns of pigs are key indicators for assessing their health status. This study aims to explore the differences in activity level and body temperature variation patterns between normal and abnormal pigs. In view of the characteristics of pig ear tag data, such as high volatility, unclear features and strong randomness, we adopted various processing and analysis methods
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Prediction of CODMn concentration in lakes based on spatiotemporal feature screening and interpretable learning methods - A study of Changdang Lake, China Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-06 Juan Huan, Yongchun Zheng, Xiangen Xu, Hao Zhang, Bing Shi, Chen Zhang, Qucheng Hu, Yixiong Fan, Ninglong Wu, Jiapeng Lv
The organic pollution of lake water can cause a tremendous threat to the water ecosystem and human health. The COD is one of the crucial indicators of lake water quality and is commonly utilized to gauge the extent of organic pollution in lake water. Therefore, this paper selected COD as the research object and used the water quality monitoring data of Changdang Lake in China and its upstream and downstream
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SugarcaneGAN: A novel dataset generating approach for sugarcane leaf diseases based on lightweight hybrid CNN-Transformer network Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-06 Xuechen Li, Xiuhua Li, Muqing Zhang, Qinghan Dong, Guiying Zhang, Zeping Wang, Peng Wei
Generative Adversarial Networks (GAN) were applied to provide methodological support for efficient sample expansion of crop disease features. Accurate extraction of leaf foreground scenes is crucial for generating high-quality disease features. However, the reported GAN models, such as LeafGAN and STA-GAN, mainly use Grad-CAM to achieve leaf segmentation, which is only suitable for circular-like leaves
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Quantifying the economic and animal welfare trade-offs of classification models in precision livestock farming for sub-optimal mobility management Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-05 Francis Edwardes, Mariska van der Voort, Henk Hogeveen
Precision livestock farming (PLF) offers a sensor-based management approach to potentially mitigate the negative economic and animal welfare consequences of sub-optimal mobility (SOM). Human-based SOM classification is often done using more than two classes (i.e., mobility scores 1–5, where 1 = optimal and 5 = severely impaired mobility), while binary classification is ultimately used in sensor-based
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Three-dimensional area coverage planning model for robotic application Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-05 Mahdi Vahdanjoo, René Gislum, Claus Aage Grøn Sørensen
Due to the increasing global population, the supply of nonrenewable resources such as fossil fuels are limited. Therefore, it is urgent to implement measures to control and reduce energy consumption in agriculture and other sectors. Improving energy efficiency and reducing pollution in agriculture can bring both financial and environmental benefits. By optimizing in-field coverage planning of agricultural
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Improving potato AGB estimation to mitigate phenological stage impacts through depth features from hyperspectral data Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-05 Yang Liu, Haikuan Feng, Jibo Yue, Xiuliang Jin, Yiguang Fan, Riqiang Chen, Mingbo Bian, Yanpeng Ma, Jingbo Li, Bo Xu, Guijun Yang
The accurate estimation of above ground biomass (AGB) is valuable in grasping the status of potato growth and assessing yield. Spectral analysis techniques play an important role in AGB estimation by virtue of its non-destructive and rapid advantages. However, the models constructed based on spectral features at the entire growth stages due to differences in phenological stages, such as vegetation
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Exploration of data for analysis using boundary line methodology Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-04 C. Miti, A.E. Milne, K.E. Giller, V.O. Sadras, R.M. Lark
The boundary line model has been proposed for interpretation of the plot of a biological response (such as crop yield) against a potentially-limiting variable from observations in a large set of scenarios across which other factors show uncontrolled variation. Under this model the upper bound of the distribution of data represents the limiting effect of the potential factor on the response. Methods
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Plant disease recognition in a low data scenario using few-shot learning Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-04 Masoud Rezaei, Dean Diepeveen, Hamid Laga, Michael G.K. Jones, Ferdous Sohel
Plant disease is one of the major problems in agriculture. Diseases damage plants, reduce yields and lower the quality of the produce. Traditional approaches to detecting plant diseases are usually based on visual inspection and laboratory testing, which can be expensive and time-consuming. They require trained plant pathologists as well as specialised equipment. Several studies demonstrate that artificial
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Comparative analysis of different machine learning algorithms for predicting trace metal concentrations in soils under intensive paddy cultivation Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-02 Mehmet Taşan, Yusuf Demir, Sevda Taşan, Elif Öztürk
Contamination of agricultural soils with trace metals is of concern as it poses potential long-term threats to water resources, aquatic species, and human health. Therefore, fast, accurate and reliable methods should be developed to monitor trace metal content of agricultural soils. This study was conducted to compare performance of different machine learning models (Artificial Neural Network – ANN
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Early detection of wilt in Cajanus cajan using satellite hyperspectral images: Development and validation of disease-specific spectral index with integrated methodology Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-02 Amitava Dutta, Rashi Tyagi, Anirudha Chattopadhyay, Debtoru Chatterjee, Ankita Sarkar, Brejesh Lall, Shilpi Sharma
Pigeonpea (), a legume of nutritional significance, is highly prone to wilt disease caused by fungal pathogen, , that leads to 15–30 % of crop mortality in India. While early detection of wilts in legume is crucial for remedial measures, it has been poorly addressed till date using traditional field based manual methods. The present study aimed to design an integrated two-step wilt detection methodology
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ORP-Byte: A multi-object tracking method of pigs that combines Oriented RepPoints and improved Byte Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-02 Jisheng Lu, Zhe Chen, Xuan Li, Yuhua Fu, Xiong Xiong, Xiaolei Liu, Haiyan Wang
The use of the multi-object tracking (MOT) algorithm in pig farming is profitable for identifying individual pigs automatically and monitoring their health status. However, tracking pigs in complex scenarios remains a challenge because of occlusion, overlapping, and shape deformation. To enhance the adaptability of tracking technology for group-housed pigs and to reduce pig identity switching (IDSW)
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WeedVision: A single-stage deep learning architecture to perform weed detection and segmentation using drone-acquired images Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-02 Nitin Rai, Xin Sun
Deep learning (DL) inspired models have achieved tremendous success in locating target weed species through bounding-box approach (single-stage models) or pixel-wise semantic segmentation (two-stage models), but not both. Therefore, the goal of this research study was to develop a single-stage DL architecture that not only locate weed presence through bounding-boxes but also achieves pixel-wise instance
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A DETR-like detector-based semi-supervised object detection method for Brassica Chinensis growth monitoring Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-02 Haoyan Li, Fanhuai Shi
Object detection technology plays a crucial role in crop growth monitoring within smart agriculture. However, data labeling is a costly process necessary for constructing a large-scale dataset, which is essential to prevent overfitting in deep learning-based object detection models. Semi-Supervised Object Detection (SSOD) presents a cost-effective solution to reduce labeling and model training expenses;
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Intelligent decision-making framework for agriculture supply chain in emerging economies: Research opportunities and challenges Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-01 T. Kalimuthu, P. Kalpana, Saravanan Kuppusamy, V. Raja Sreedharan
In emerging economies such as India, Pakistan, and China, the Agricultural Supply Chain (ASC) holds significant importance. While developed countries have embraced advanced technologies like Blockchain and Artificial Intelligence to streamline their supply chains, we find that emerging economies have yet to catch up. This lag contributes to poor visibility and inefficiencies across the supply chain
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Universal modeling for optimizing leafy vegetable production in an environment-controlled vertical farm Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-01 Jim Junhui Huang, Charmaine Xinying Tan, Weibiao Zhou
An empirical growth-response model (GRM) that can accurately predict leafy vegetable (e.g., kailan) shoot fresh weight, in terms of photosynthetic photon flux density (PPFD) and certain cultivation duration counted from sowing, in an environment-controlled vertical farm, was developed. This GRM was constructed as the product of three independent models including light-time-biomass response model (LTBRM)
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Automated segmentation of individual leafy potato stems after canopy consolidation using YOLOv8x with spatial and spectral features for UAV-based dense crop identification Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-01 Hanhui Jiang, Bryan Gilbert Murengami, Liguo Jiang, Chi Chen, Ciaran Johnson, Fernando Auat Cheein, Spyros Fountas, Rui Li, Longsheng Fu
High throughput phenotyping of potatoes after canopy consolidation is crucial to crop breeding and management. A prior step is to segment their leafy potato stems, which is challenging after canopy consolidation because potato stems are dense and intertwined. Current methods for dense crop segmentation are manual. This study equipped unmanned aerial vehicles with a high-resolution RGB sensor in ultra-low
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An improved adaptive data rate algorithm of LoRaWAN for agricultural mobile sensor nodes Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-01 Hao Wang, Xihai Zhang, Jianxin Liao, Yu Zhang, Hongbo Li
Wireless sensor network (WSN) is an important research area in smart agriculture. As an emerging technology, LoRa is used for communication between wireless sensor nodes. When LoRa is used as a mobile terminal node (mobile robot, drone, etc.), the Quality of Service (QoS) of LoRa network will be greatly reduced, which will have an impact on the quantitative decision-making and intelligent control in
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A novel cascaded multi-task method for crop prescription recommendation based on electronic medical record Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-01 Chang Xu, Lei Zhao, Haojie Wen, Yiding Zhang, Lingxian Zhang
Research on diagnosis of crop diseases and pests becomes a hot topic of the application of artificial intelligence technology in smart agriculture. Plant electronic medical records (PEMRs) formed by Beijing Plant Clinic provides a new idea for the diagnosis and prevention of crop diseases and pests. PEMRs are stored in the form of heterogeneous data, containing a wealth of plant information, disease
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From lab to orchard use for models of hand-held NIRS instrument: A case for navel orange quality assessment considering ambient light correction Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-01 Xudong Sun, Fulong Guo, Jiacheng Liu, Zhaoxing Chen, Waleed Fouad Abobatta, Muhammad Azher Nawaz, Shaoran Feng
The models for hand-held near infrared spectroscopy (NIRS) instrument calibrated in the laboratory are prone to failure during orchard application because of measurement conditions change especially on sunlight interference. The models transfer from lab to orchard for assessment of soluble solids content (SSC) of navel orange on tree was investigated with consideration of sunlight influence to the
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Multi-source data fusion improved the potential of proximal fluorescence sensors in predicting nitrogen nutrition status across winter wheat growth stages Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-03-01 Qing Liu, Cuicun Wang, Jie Jiang, Jiancheng Wu, Xue Wang, Qiang Cao, Yongchao Tian, Yan Zhu, Weixing Cao, Xiaojun Liu
Rapid and accurate nitrogen (N) diagnosis plays a crucial role in precise N fertilizer management of wheat. However, most existing N diagnosis models based on proximal fluorescence sensors’ indicators are confined to a single growth stage, thereby limiting the accuracy and universality of models. Therefore, this study aimed to construct and evaluate wheat N diagnosis models based on proximal fluorescence
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Near-infrared spatially-resolved spectroscopy for milk quality analysis Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-02-29 Jose A. Diaz-Olivares, Martin J. Gote, Wouter Saeys, Ines Adriaens, Ben Aernouts
To support in-line quality control of raw milk and ensure the close monitoring of the metabolic and udder health of dairy cows, we propose a fiber-optic spatially-resolved spectroscopy (SRS) setup. This setup allows to vary the interaction of long-wave near-infrared (LW-NIR) light with the milk by submerging a separate optical illumination and detection fiber into the sample and altering their relative
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An improved target detection method based on YOLOv5 in natural orchard environments Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-02-29 Jiachuang Zhang, Mimi Tian, Zengrong Yang, Junhui Li, Longlian Zhao
The recognition and localization of fruit tree trunks in orchard are important for orchard operation robots, which are the bases for automatic navigation, fruit tree spraying and fertilization etc. A method was proposed based on machine vision to detect target objects such as fruit tree trunks, person and supporters in orchard by improving the YOLOv5 deep learning algorithm in this paper, which is
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Map-sensor-based site-specific manure application in wheat Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-02-29 Jian Zhang, Ajit Borundia, Abdul M. Mouazen
Site-specific manure application is a promising approach to optimise manure use by accounting for spatial variability of soil nutrients within the field. Traditionally, variable-rate management can be achieved by the map-based approach and the sensor-based approach, which account for the soil or manure characteristics, respectively. In this work, a new map-sensor-based methodology for variable-rate
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Monitoring of key Camellia Oleifera phenology features using field cameras and deep learning Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-02-28 Haoran Li., Enping Yan, Jiawei Jiang, Dengkui Mo
A rapid and accurate yield estimation is of great significance to the management and sustainable development of Camellia Oleifera forests. Consequently, the simultaneous and accurate detection of key phenology features of Camellia Oleifera (buds, flowers, fruits) is crucial for precise yield estimation. This not only enables robotic harvesting but also allows for the prediction of peak flowering and
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A novel perception and semantic mapping method for robot autonomy in orchards Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-02-28 Yaoqiang Pan, Kewei Hu, Hao Cao, Hanwen Kang, Xing Wang
Agricultural robots must navigate challenging dynamic and semi-structured environments. Recently, environmental modelling using LiDAR-based SLAM has shown promise in providing highly accurate geometry. However, how this chaotic environmental information can be used to achieve effective robot automation in the agricultural sector remains unexplored. In this study, we propose a novel semantic mapping
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A systematic review of open data in agriculture Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-02-28 Jorge Chamorro-Padial, Roberto García, Rosa Gil
In this work, we perform a systematic literature review of Open Data and Public Domain datasets in Agriculture. We use the PRISMA method to analyze the existing academic literature about open data in agriculture, concretely 1401 papers from the IEEE Xplore and Web of Science collections, published from 2012 to 2022. Many of these articles use or make available datasets of very different typologies
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Predicting bovine daily milk yield by leveraging genomic breeding values Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-02-27 Andrea Mario Vergani, Alessandro Bagnato, Marco Masseroli
The main goal of this work, conducted on a herd of 502 Holstein cows situated in Italy, is to propose a machine learning-based approach to forecast the individual bovine daily milk production by explicitly leveraging genotypic information. As part of our study, we also evaluated the importance in the prediction of genotypic and phenotypic variables usually available within herd. The methodology we
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Rice seed classification by hyperspectral imaging system: A real-world dataset and a credible algorithm Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-02-26 Yufei Ge, Shaozhong Song, Shuang Yu, Xiaoli Zhang, Xiongfei Li
Rice is one of the three major crops in the world. The hyperspectral identification of rice seeds is of great significance to the development of agriculture and hence has made remarkable achievements. However, the lack of publicly available benchmark datasets with a large number of rice seeds hinders the generalization of models and limits the advancement of the field. To address this issue, this study
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Improving the estimation accuracy of soil organic matter based on the fusion of near-infrared and Raman spectroscopy using the outer-product analysis Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-02-26 Yu Bai, Wei Yang, Zhaoyang Wang, Yongyan Cao, Minzan Li
Accurate estimation of soil organic matter (SOM) content is of great significance for advancing precision agriculture and assessing carbon storage. Proximal sensing techniques, such as near-infrared spectroscopy (NIR) and Raman spectroscopy, provide effective means for rapidly acquiring soil information. However, quantitative estimation of soil parameters using Raman spectroscopy has been challenged
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Occluded apples orientation estimator based on deep learning model for robotic harvesting Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-02-26 Eugene Kok, Chao Chen
Apple harvesting robot is in rapid development in the recent years due to the shortage of manual labor. Robotics vision system has been one of the main focus in the development to achieve successful grasping. Information such as the apple orientation is valuable for developing more effective fruit detachment strategies. Occlusions in the orchard environment and the lack of distinct features in an apple
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Cotton-YOLO: Improved YOLOV7 for rapid detection of foreign fibers in seed cotton Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-02-24 Qingxu Li, Wenjing Ma, Hao Li, Xuedong Zhang, Ruoyu Zhang, Wanhuai Zhou
In China, the widespread adoption of machine-picked cotton has greatly improved the efficiency of cotton harvesting. However, this has significantly increased the presence of foreign fibers in seed cotton. Failure to promptly eliminate foreign fibers can have severe consequences for the subsequent quality of cotton processing and textile production. Currently, manual labor is predominantly used for
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A recommendation model of rice fertilization using knowledge graph and case-based reasoning Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-02-24 Weixi Ge, Jun Zhou, Pengyuan Zheng, Licun Yuan, Luke Toroitich Rottok
Rice fertilization management plays an important role in rice yield and quality; however, making automatic fertilization plans according to the rice life cycle is difficult. This study proposes a rice fertilization recommendation model using a knowledge graph and a case-based reasoning method. To build the recommendation model, an initial fertilization scheme was first obtained by retrieving information
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Accurate and robust pollinations for watermelons using intelligence guided visual servoing Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-02-24 Khubaib Ahmad, Ji-Eun Park, Talha Ilyas, Jong-Hoon Lee, Ji-Hoon Lee, Sangcheol Kim, Hyongsuk Kim
With a significant decline in the bee population, there is an increasing demand for automated robotic pollination. This study proposes a novel approach for automating watermelon pollination using visual intelligence-guided servo control. On the control loop, the sizes and orientations of flowers are estimated by leveraging the inference capability of Deep Learning. The estimated sizes of flowers are
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Robot driven combined site-specific maize seeding and N fertilization: An agro-economic investigation Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-02-24 Muhammad Abdul Munnaf, Yongjing Wang, Abdul Mounem Mouazen
Autonomous agricultural management combats the labor crisis in farming industry and ensures efficient farm operations, whereas variable rate technology has proven to increase resource use efficiency and reduce environmental impacts. This study developed an autonomous robot-driven combined site-specific seeding and nitrogen (N) fertilization solution, whose agro-economic and environmental viability
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Optical properties of cotton and mulching film and feature bands selection in the 400 to 1120 nm range Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-02-24 Jie Wang, Mengyun Zhang, Zhenxuan Zhao, Zikai Wei, Ruoyu Zhang
Cotton is prone to being mixed with mulching film during the harvesting and packing process in China, which can significantly decrease the quality of cotton fiber and impact the quality of subsequent textile products. Mulching film is a translucent material that makes it challenging to detect optically, which presents an urgent challenge for cotton quality testing due to the lack of research on its
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Object detection and tracking in Precision Farming: a systematic review Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-02-23 Mar Ariza-Sentís, Sergio Vélez, Raquel Martínez-Peña, Hilmy Baja, João Valente
Object Detection and Tracking have gained importance in recent years because of the great advances in image and video analysis techniques and the accurate results these technologies are producing. Moreover, they have successfully been applied to multiple fields, including the agricultural domain since they offer real-time monitoring of the status of the crops and animals while counting how many are
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Dynamic visual servo control methods for continuous operation of a fruit harvesting robot working throughout an orchard Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-02-23 Mingyou Chen, Zengxing Chen, Lufeng Luo, Yunchao Tang, Jiabing Cheng, Huiling Wei, Jinhai Wang
Fruit-picking robots are crucial for achieving efficient orchard harvesting. To genuinely meet the commercial production needs of farmers, the new generation of fruit-picking robots must be capable of demonstrating complete and continuous observation, movement, and picking behaviors throughout complex orchards, akin to real human employees. This poses systematic challenges, as many prior researches
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Technical Report: Development and validation of continuous monitoring system for calves based on commercially available sensor for humans Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-02-23 Florian Debruyne, Jade Bokma, Tom Staessens, Alberto Peña Fernández, Daniel Berckmans, Bart Pardon, Glenn Van Steenkiste
In the last decade, development of continuous sensor based monitoring systems for farm animals (Precision Livestock Farming (PLF)) has markedly increased with the aim to automate and improve disease detection. In calves, multiple technologies exist, but most detect only a single parameter, and none are available for continuous heart rate and electrocardiogram (ECG). The objective of this paper was
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Machine learning based plot level rice lodging assessment using multi-spectral UAV remote sensing Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-02-22 Mukesh Kumar, Bimal K. Bhattacharya, Mehul R. Pandya, B.K. Handique
Rice plant lodging leads to change in canopy structure, yield loss and creates a menace in harvest operations. In situ assessment of lodging is time consuming, labour intensive, inefficient and inaccurate. Its assessment contributes greatly in in-situ field management and damage analysis. In this study, imaging observations from ten-band (within 444–842 nm) multispectral camera at 0.06 m Ground Sampling
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The effect of dataset construction and data pre-processing on the eXtreme Gradient Boosting algorithm applied to head rice yield prediction in Australia Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-02-22 A. Clarke, D. Yates, C. Blanchard, M.Z. Islam, R. Ford, S. Rehman, R. Walsh
Dataset quality heavily impacts the predictive performance of data-driven modelling. This issue can be exacerbated in the prediction of agricultural production due to the complex interactions between the climate, the environment and the way the plant is affected by these conditions during the season. This study aims to create an empirical model to predict Head Rice Yield (HRY), the primary quality
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Detection of water content in tomato stems by electrical impedance spectroscopy: Preliminary study Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-02-22 Benhua Zhang, Zhanwei Dong, Xunan Sui, Jiale Gao, Longlong Feng
Tomatoes lack drought-resistant genes, rendering seedlings susceptible to slow growth and death during drought stress. To gauge the water deficit in tomato plants, the electrical impedance spectra of tomato seedlings were measured at distinct water content levels (pristine, 92%, 91%, 90%, and 89%). These measurements tracked changes in water state, microstructure, and shear stress during water loss
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Efficient extraction of corn rows in diverse scenarios: A grid-based selection method for intelligent classification Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-02-22 Longzhe Quan, Zhiming Guo, Lili Huang, Yi Xue, Deng Sun, Tianbao Chen, Tianyu Geng, Jianze Shi, Pengbiao Hou, Jinbin He, Zhaoxia Lou
In various complex field environments, machine learning-based crop row detection faces challenges like rigidity and low adaptability. To address this issue, we integrated deep learning into agricultural analysis and established a diverse dataset of corn fields across various scenarios. By employing an end-to-end CNN model and predicting row and column anchors, we created a grid-like understanding of
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Robotics in greenhouses. Scoping review Comput. Electron. Agric. (IF 8.3) Pub Date : 2024-02-22 J.A. Sánchez-Molina, F. Rodríguez, J.C. Moreno, J. Sánchez-Hermosilla, A. Giménez
One of the main initiatives to increase the profit, production, and quality of greenhouse horticulture, as well as its sustainability, is the use of robots. This paper focusses on the state-of-the-art of the use of robots in greenhouses since the 1980 s on the basis of the PRISMA methodology. It is really a scoping review because the main objective is to map the literature on the application of robotics