Early detection of tomato leaf diseases is critical to prevent their spread, but manual detection methods for the same are time-consuming, inconsistent, and labor-intensive. To address this problem, researchers from China developed a novel deep learning network architecture called PLPNet that can accurately detect and distinguish different leaf diseases in real time. Their approach effectively addresses the limitations of previous models and paves the way to smarter agricultural practices.
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China , Xinyu He , Jiangsu , Yahui , Guizhou , Aibin Chen , Yanfeng Wang , Zhiwen Tang , Guoxiong Zhou , Liujun Li , Central South University Of Forestry , Method Of Research , Department Of Soil , Plant Protection Research Institute , University Of Idaho , Academy Of Agricultural Sciences , College Of Computer Information Engineering , College Of Bangor , National University Of Defense Technology , China Central South University Of Forestry , Professor Guoxiong Zhou , Central South University , Plant Village , Information Engineering , Water Systems , Protection Research Institute ,
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