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This project uses lightweight convolutional neural network algorithm to solve the problem that traditional models cannot extract more detailed features and train high-precision models to accurately identify crop diseases. Through the use of Microsoft intelligent cloud Azure cloud platform, based on the MobileNetV3 lightweight model for training, the obtained model can reach more than 90% recognition rate. At the same time, in order to ensure the portability of the program, the team developed an efficient and intelligent small program for identifying crop diseases and pests -- "Recognize agricultural diseases", which is committed to promoting the solution of crop yield reduction caused by diseases and pests.
Zhiquan Huang is a Grade 2020 undergraduate majoring in Software Engineering, College of Software, Henan University of Science and Technology. Can Li,2020 student majoring in software engineering, School of Software, Henan University of Science and Technology. Yu Fang is an English major from the School of Foreign Languages, Henan University of Science and Technology, Grade 2019. Pu Gao is an English major from the School of Foreign Languages, Henan University of Science and Technology, Grade 2020.
Technologies we are looking to use in our projects
Artificial Neural Networks