Research on a Potato Leaf Disease Diagnosis System Based on Deep Learning

Chunhui Zhang, Shuai Wang, Chunguang Wang, Haichao Wang, Yingjie Du and Zheying Zong ()
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Chunhui Zhang: College of Electromechanical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Shuai Wang: College of Electromechanical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Chunguang Wang: College of Electromechanical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Haichao Wang: College of Electromechanical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Yingjie Du: Department of Mechanical and Electric Power Engineering, Hohhot Vocational College, Hohhot 010018, China
Zheying Zong: College of Electromechanical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China

Agriculture, 2025, vol. 15, issue 4, 1-24

Abstract: Potato is the fourth largest food crop in the world. Disease is an important factor restricting potato yield. Disease detection based on deep learning has strong advantages in network structure, training speed, detection accuracy, and other aspects. This article took potato leaf diseases (early blight and viral disease) as the research objects, collected disease images to construct a disease dataset, and expanded the dataset through data augmentation methods to improve the quantity and diversity of the dataset. Four classic deep learning networks (VGG16, MobilenetV1, Resnet50, and Vit) were used to train the dataset, and the VGG16 network had the highest accuracy of 97.26%; VGG16 was chosen as the basic research network. A new, improved algorithm, VGG16S, was proposed to solve the problem of large network parameters by using three improvement methods: changing the network structure of the VGG16 network from “convolutional layer + flattening layer + fully connected layer” to “convolutional layer + global average pooling”, integrating CBAM attention mechanism, and introducing Leaky ReLU activation function for learning and training. The improved VGG16S network has a parameter size of 15 M (1/10 of VGG16), and the recognition accuracy of the test set is 97.87%. This article used response surface analysis to optimize hyperparameters, and the test results indicated that VGG16S, after hyperparameter tuning, had further improved its diagnostic performance. At last, this article completed ablation experiments and public dataset testing. The research results will provide a theoretical basis for the timely adoption of corresponding prevention and control measures, improving the yield and quality of potatoes and increasing economic benefits.

Keywords: potato; data augmentation; VGG16; network lightweighting; hyperparameter optimization (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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