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

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. S...

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Autores principales:	Yan Zhang, Shiyun Wa, Yutong Liu, Xiaoya Zhou, Pengshuo Sun, Qin Ma
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	maize leaf disease detection activation functions generative adversarial network convolutional neural network Science Q
Acceso en línea:	https://doaj.org/article/d9dc4bcade9441d6a7bf487342f623a7
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spelling	oai:doaj.org-article:d9dc4bcade9441d6a7bf487342f623a72021-11-11T18:50:24ZHigh-Accuracy Detection of Maize Leaf Diseases CNN Based on Multi-Pathway Activation Function Module10.3390/rs132142182072-4292https://doaj.org/article/d9dc4bcade9441d6a7bf487342f623a72021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4218https://doaj.org/toc/2072-4292Maize 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.Yan ZhangShiyun WaYutong LiuXiaoya ZhouPengshuo SunQin MaMDPI AGarticlemaize leaf disease detectionactivation functionsgenerative adversarial networkconvolutional neural networkScienceQENRemote Sensing, Vol 13, Iss 4218, p 4218 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	maize leaf disease detection activation functions generative adversarial network convolutional neural network Science Q
spellingShingle	maize leaf disease detection activation functions generative adversarial network convolutional neural network Science Q Yan Zhang Shiyun Wa Yutong Liu Xiaoya Zhou Pengshuo Sun Qin Ma High-Accuracy Detection of Maize Leaf Diseases CNN Based on Multi-Pathway Activation Function Module
description	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.
format	article
author	Yan Zhang Shiyun Wa Yutong Liu Xiaoya Zhou Pengshuo Sun Qin Ma
author_facet	Yan Zhang Shiyun Wa Yutong Liu Xiaoya Zhou Pengshuo Sun Qin Ma
author_sort	Yan Zhang
title	High-Accuracy Detection of Maize Leaf Diseases CNN Based on Multi-Pathway Activation Function Module
title_short	High-Accuracy Detection of Maize Leaf Diseases CNN Based on Multi-Pathway Activation Function Module
title_full	High-Accuracy Detection of Maize Leaf Diseases CNN Based on Multi-Pathway Activation Function Module
title_fullStr	High-Accuracy Detection of Maize Leaf Diseases CNN Based on Multi-Pathway Activation Function Module
title_full_unstemmed	High-Accuracy Detection of Maize Leaf Diseases CNN Based on Multi-Pathway Activation Function Module
title_sort	high-accuracy detection of maize leaf diseases cnn based on multi-pathway activation function module
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d9dc4bcade9441d6a7bf487342f623a7
work_keys_str_mv	AT yanzhang highaccuracydetectionofmaizeleafdiseasescnnbasedonmultipathwayactivationfunctionmodule AT shiyunwa highaccuracydetectionofmaizeleafdiseasescnnbasedonmultipathwayactivationfunctionmodule AT yutongliu highaccuracydetectionofmaizeleafdiseasescnnbasedonmultipathwayactivationfunctionmodule AT xiaoyazhou highaccuracydetectionofmaizeleafdiseasescnnbasedonmultipathwayactivationfunctionmodule AT pengshuosun highaccuracydetectionofmaizeleafdiseasescnnbasedonmultipathwayactivationfunctionmodule AT qinma highaccuracydetectionofmaizeleafdiseasescnnbasedonmultipathwayactivationfunctionmodule
_version_	1718431733252096000

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

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