Identification of Maize Leaf Diseases Based On AlexNet and ResNet50 Convolutional Neural Networks

Micheni, Maurice; Birithia, Rael; Mugambi, Cyrus; Too, Boaz; Kinyua, Margaret

doi:10.33022/ijcs.v12i4.3270

Cited by 1 publication

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References 26 publications

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“…The trained model out performs other CNN based transfer learning approaches giving out an accuracy of 95.99%. M. Micheni et al [28] carried out an experiment on maize data set using AlexNet and ResNet-50 with the help of transfer learning along with SVM, amounting accuracies of 98.3%, 96.6% and 88.5% respectively. Paddy leaves were used by Naware et al [29] to classify diseases using KNN and SVM giving 96.2% and 98.56% accuracies respectively.…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Networks Fusion with Support Vector Machines and K-Nearest Neighbors for Precise Crop Leaf Disease Classification

2024

IJACSA

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Maize and Paddy are pivotal crops in India, playing a vital role in ensuring food security. Timely detection of diseases and the implementation of remedial measures are crucial for securing optimal crop yield and profitability for farmers. This study utilizes a dataset encompassing images of diseased maize and paddy leaves, addressing various conditions such as corn blight, common rust, gray leaf spot, brown spot, hispa, and leaf blast, alongside images of healthy leaves. The dataset used here is a combination of online repository as well as manually collected samples from neighborhood farmlands at different growth stages. A machine vision approach that is accessible, quick, robust and cost effective to determine crop leaf diseases is need of the hour. In the proposed work, using transfer-learning approach, many Deep Convolutional Neural Networks (DCNN) and hybrid DCNNs have been developed, trained, validated and tested. To achieve better accuracy, integration of DCNNs and machine learning classifiers like multiclass Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms is carried out. The research is carried out in four stages, in the first stage, DCNNs have been used as classifiers. Subsequently, these same DCNNs are repurposed as feature extractors, and the extracted features are input into classifiers such as multiclass SVM and KNN. In the third stage, an ensemble of DCNNs is performed for networks exhibiting excellent performance during first stage. At a fourth stage, features extracted from these ensemble networks are fed into the same multiclass SVM and KNN classifiers to assess accuracy. A total of 1600 images for training and 400 images for testing are used. For maize data set, we achieved a 100% accuracy in AlexNet plus VGG-16 hybrid network for multiclass SVM with 75:25 split ratio and for paddy dataset 99.51% accuracy is achieved in ResNet-50 plus Darknet-53 hybrid network for multiclass SVM with 75:25 split ratio. In the proposed study a comprehensive analysis is conducted, exploring features from various layers and adjusting data split ratios.

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Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Networks Fusion with Support Vector Machines and K-Nearest Neighbors for Precise Crop Leaf Disease Classification

2024

IJACSA

View full text Add to dashboard Cite

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Identification of Maize Leaf Diseases Based On AlexNet and ResNet50 Convolutional Neural Networks

Cited by 1 publication

References 26 publications

Deep Convolutional Neural Networks Fusion with Support Vector Machines and K-Nearest Neighbors for Precise Crop Leaf Disease Classification

Deep Convolutional Neural Networks Fusion with Support Vector Machines and K-Nearest Neighbors for Precise Crop Leaf Disease Classification

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