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Yaoqiang Pan, Xvlin Xiao, Kewei Hu, Hanwen Kang, Yangwen Jin, Yan Chen and Xiangjun Zou
In an unmanned orchard, various tasks such as seeding, irrigation, health monitoring, and harvesting of crops are carried out by unmanned vehicles. These vehicles need to be able to distinguish which objects are fruit trees and which are not, rather than...
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Huizhong Xiong, Xiaotong Gao, Ningyi Zhang, Haoxiong He, Weidong Tang, Yingqiu Yang, Yuqian Chen, Yang Jiao, Yihong Song and Shuo Yan
A novel deep learning model, DiffuCNN, is introduced in this paper, specifically designed for counting tobacco lesions in complex agricultural settings. By integrating advanced image processing techniques with deep learning methodologies, the model signi...
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Yifei Zhang, Yuxin Lu, Haiou Guan, Jiao Yang, Chunyu Zhang, Song Yu, Yingchao Li, Wei Guo and Lihe Yu
Background: Low-temperature stress significantly restricts maize germination, seedling growth and development, and yield formation. However, traditional methods of evaluating maize seedling quality are inefficient. This study established a method of grad...
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Ping Dong, Kuo Li, Ming Wang, Feitao Li, Wei Guo and Haiping Si
In addition to the conventional situation of detecting a single disease on a single leaf in corn leaves, there is a complex phenomenon of multiple diseases overlapping on a single leaf (compound diseases). Current research on corn leaf disease detection ...
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Min Hao, Quan Sun, Chuanzhong Xuan, Xiwen Zhang, Minghui Zhao and Shuo Song
To achieve automated farming management, including the recording, tracking, and statistics of sheep, we harness deep learning technology for sheep face recognition research, and the further development of lightweight sheep face recognition models. Deep l...
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