Open AccessIdentification of Tomato Pests and Diseases Based on Transfer Learning
Author(s) -
Yang Liu,
Limin Yu,
Shiquan Tao,
Zhenghong Yang,
Wanlin Gao,
Yuanhang Ren
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2025/1/012076
Subject(s) - identification (biology) , transfer of learning , computer science , artificial intelligence , table (database) , pattern recognition (psychology) , set (abstract data type) , convolutional neural network , simple (philosophy) , machine learning , test set , data mining , biology , botany , philosophy , epistemology , programming language
There are numerous kinds of tomato diseases and insect pests. Their pathology is complex and different. It is hard to rely on manual identification purely and the error rate is high. After collecting a mass of leaf table pictures, our aim is to classify nine kinds of common tomato diseases in China. The idea of transfer learning is applied to achieve recognition and classification of tomato data set by the lightweight convolutional neural MobileNet. Finally, the model can obtain test classification accuracy of 97.19%. Experiments have proved that this method is not only simple to operate and easy to implement, but also can achieve high accuracy on plant diseases.