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Chenwei Deng, Yingxin Zhang, Beifang Wang, Hong Wang, Pao Xue, Yongrun Cao, Lianping Sun, Shihua Cheng, Liyong Cao and Daibo Chen
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Sitao Liu, Shenghui Fu, Anrui Hu, Pan Ma, Xianliang Hu, Xinyu Tian, Hongjian Zhang and Shuangxi Liu
Aiming at difficult image acquisition and low recognition accuracy of two rice canopy pests, rice stem borer and rice leaf roller, we constructed a GA-Mask R-CNN (Generative Adversarial Based Mask Region Convolutional Neural Network) intelligent recognit...
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Hongjun Ni, Zhiwei Shi, Stephen Karungaru, Shuaishuai Lv, Xiaoyuan Li, Xingxing Wang and Jiaqiao Zhang
Rice, a staple food crop worldwide, is pivotal in agricultural productivity and public health. Automatic classification of typical rice pests and diseases is crucial for optimizing rice yield and quality in practical production. However, infrequent occur...
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Yeyang Fan, Zhenhua Zhang, Derun Huang, Tingxu Huang, Hongfei Wang, Jieyun Zhuang and Yujun Zhu
Rice blast is arguably the most devastating fungal disease of rice. Utilization of resistance genes to breed resistant cultivars is one of the most economical and environmentally friendly approaches to combat the disease. Pi25, a major resistance gene co...
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Jizhong Deng, Chang Yang, Kanghua Huang, Luocheng Lei, Jiahang Ye, Wen Zeng, Jianling Zhang, Yubin Lan and Yali Zhang
The realization that mobile phones can detect rice diseases and insect pests not only solves the problems of low efficiency and poor accuracy from manually detection and reporting, but it also helps farmers detect and control them in the field in a timel...
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