Diagnosis of Crop Disease Based on Multi-task Learning
In order to judge the severity of crop diseases quickly and accurately,a novel online remote diagnosis method was proposed based on multi-task learning in this paper.The classic model MobileNetV3 was improved by introducing convolutional block attention module and feature pyramid module to boost the performance of the recognition of crops,diseases and pests,and disease levels.Besides,some data augment methods were adopted to extend the small samples.The performance of the improved model and other image recognition models in the identification of crop disease was tested,and the performance of different models with and without data enhancement processing was explored.The results showed that the mean average precision of proposed method on such 3 tasks was more than that of the original model by 1.38,2.24 and 2.03 percentage points,respectively,and the average recall of proposed method on such 3 tasks was more than that of the original model by 2.38,1.62 and 1.18 percentage points,respectively.The proposed method outperformed the state-of-the-art methods,such as MobileNetV3,InceptionV3 and YOLOv7.
国家科技期刊平台
NSTL
万方数据
维普
