Plant Disease Classification using Convolution Neural Network

Bhargav, Ganipisetty Akhil; Pathak, Ajita

doi:10.22214/ijraset.2022.47970

Cited by 2 publications

(2 citation statements)

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“…The proposed approach holds the potential to promote smart agriculture and precision agriculture. Ganipisetty Akhil Bhargav et al [18] proposed a CNNbased plant disease detection system using Arduino and smartphone technology. The integration of CNN and lowcost microcontrollers presents an affordable and efficient solution for real-time disease detection.…”

Section: Literature Reviewmentioning

confidence: 99%

A Convolutional Neural Network Approach to Robust Crop Health Monitoring

Jariwala,

Jain

2024

Preprint

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Convolutional Neural Networks (CNNs) have opened up new possibilities in a variety of fields, including plant pathology. Through image analysis, this study aims to take advantage of CNNs’ capacity to recognize and categorize plant illnesses. Automated disease identification has potential for prompt intervention and crop preservation in modern times, when agricultural output assumes critical relevance. This work elaborates on the application and assessment of a CNN-based model designed for the detection of plant diseases. The technology separates healthy plants from those with problems by taking pictures of plants and putting them through the trained model. The growing demand for effective disease monitoring and management in the agriculture industry highlights the urgency of this research. The suggested model shows promising accuracy and dependability by utilising a large dataset and stringent evaluation metrics. This research highlights the potential of CNNs as a workable tool for upgrading agricultural practices by using a systematic approach. The results of this study shed light on both the strengths and weaknesses of CNNs in the context of automated plant disease diagnosis.

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Section: Literature Reviewmentioning

confidence: 99%

A Convolutional Neural Network Approach to Robust Crop Health Monitoring

Jariwala,

Jain

2024

Preprint

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“…Another CNN based model was introduced to classify diseases on Maize data set claiming 97% accuracy [25]. Utkarha N Fulari et al [26] proposed an AlexNet based plant leaf disease identification and classification in which about 12949 open database images were used.…”

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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Plant Disease Classification using Convolution Neural Network

Cited by 2 publications

References 0 publications

A Convolutional Neural Network Approach to Robust Crop Health Monitoring

A Convolutional Neural Network Approach to Robust Crop Health Monitoring

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

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