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Hui Deng, Wenjiang Zhang, Xiaoqian Zheng and Houxi Zhang
The accurate and timely identification of crops holds paramount significance for effective crop management and yield estimation. Unmanned aerial vehicle (UAV), with their superior spatial and temporal resolution compared to satellite-based remote sensing...
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Yujuan Cao, Jianguo Dai, Guoshun Zhang, Minghui Xia and Zhitan Jiang
This paper combines feature selection with machine learning algorithms to achieve object-oriented classification of crops in Gaofen-6 remote sensing images. The study provides technical support and methodological references for research on regional monit...
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Shuqi Jiang, Jiankui Yu, Shenglin Li, Junming Liu, Guang Yang, Guangshuai Wang, Jinglei Wang and Ni Song
This research provides a comprehensive analysis of the spatiotemporal evolution of the regional cropping structure and its influencing factors. Using Landsat satellite images, field surveys, and yearbook data, we developed a planting structure extraction...
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Ruiqing Chen, Liang Sun, Zhongxin Chen, Deji Wuyun and Zheng Sun
The prompt and precise identification of corn and soybeans are essential for making informed decisions in agricultural production and ensuring food security. Nonetheless, conventional crop identification practices often occur after the completion of crop...
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Ana Corceiro, Nuno Pereira, Khadijeh Alibabaei and Pedro D. Gaspar
The global population?s rapid growth necessitates a 70% increase in agricultural production, posing challenges exacerbated by weed infestation and herbicide drawbacks. To address this, machine learning (ML) models, particularly convolutional neural netwo...
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Zhichao Chen, Guoqiang Wang, Tao Lv and Xu Zhang
Diseases of tomato leaves can seriously damage crop yield and financial rewards. The timely and accurate detection of tomato diseases is a major challenge in agriculture. Hence, the early and accurate diagnosis of tomato diseases is crucial. The emergenc...
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Yunsong Jia, Qingxin Zhao, Yi Xiong, Xin Chen and Xiang Li
The issues of inadequate digital proficiency among agricultural practitioners and the suboptimal image quality captured using mobile smart devices have been addressed by providing appropriate guidance to photographers to properly position their mobile de...
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Pierre Schambri, Didier Kleiber and Cecile Levasseur-Garcia
This study delves into the detection of the mycotoxin zearalenone (ZEA) in popcorn, aligning with the broader goal of ensuring food safety and security. Employing fast, non-destructive near-infrared spectroscopy, the research analyzes 88 samples collecte...
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Che-Hao Chang, Jason Lin, Jia-Wei Chang, Yu-Shun Huang, Ming-Hsin Lai and Yen-Jen Chang
Recently, data-driven approaches have become the dominant solution for prediction problems in agricultural industries. Several deep learning models have been applied to crop yield prediction in smart farming. In this paper, we proposed an efficient hybri...
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Cunxiang Bian, Jinqiang Bai, Guanghe Cheng, Fengqi Hao and Xiyuan Zhao
Field-road mode classification (FRMC) that identifies ?in-field? and ?on-road? categories for Global Navigation Satellite System (GNSS) trajectory points of agricultural machinery containing geographic information is essential for effective crop improvem...
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