High-Accuracy Detection of Maize Leaf Diseases CNN Based on Multi-Pathway Activation Function Module

Yan Zhang,Shiyun Wa,Yutong Liu,Pengshuo Sun,Qin Ma,Xiaoya Zhou

doi:10.3390/rs13214218

Abstract

Maize leaf disease detection is an essential project in the maize planting stage. This paper proposes the convolutional neural network optimized by a Multi-Activation Function (MAF) module to detect maize leaf disease, aiming to increase the accuracy of traditional artificial intelligence methods. Since the disease dataset was insufficient, this paper adopts image pre-processing methods to extend and augment the disease samples. This paper uses transfer learning and warm-up method to accelerate the training. As a result, three kinds of maize diseases, including maculopathy, rust, and blight, could be detected efficiently and accurately. The accuracy of the proposed method in the validation set reached 97.41%. This paper carried out a baseline test to verify the effectiveness of the proposed method. First, three groups of CNNs with the best performance were selected. Then, ablation experiments were conducted on five CNNs. The results indicated that the performances of CNNs have been improved by adding the MAF module. In addition, the combination of Sigmoid, ReLU, and Mish showed the best performance on ResNet50. The accuracy can be improved by 2.33%, proving that the model proposed in this paper can be well applied to agricultural production.

Highlights

Maize belongs to Gramineae, whose cultivated area and total output rank third only to wheat and rice
This study proposed the Multi-Activation Function (MAF) module based on the CNN framework, and experiments showed excellent performance
The subsequent experiments to test the accuracy of different activation function combinations, which consisted of different sub-models and different functions, were too complicated

Summary

Introduction

Maize belongs to Gramineae, whose cultivated area and total output rank third only to wheat and rice. In addition to food for humans, maize is an excellent feed for animal husbandry. It is an important raw material for the light industry and medical industry. Some diseases such as sheath blight, rust, northern leaf blight, curcuma leaf spot, stem base rot, head smut, etc., occur widely and cause serious consequences. Among these diseases, the lesions of sheath blight, rust, northern leaf blight are found in maize leaves, whose characteristics are apparent. With the development of machine vision and deep learning technology, machine vision can quickly and accurately identify these maize leaf diseases

Methods

Results

Discussion

Conclusion