Plant Disease Detection using Convolutional Neural Network

Kolli, Jahnavi; Vamsi, Dhara Mohana; Manikandan, V. M.

doi:10.1109/ibssc53889.2021.9673493

Cited by 18 publications

(4 citation statements)

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“…It attained an accuracy of 94.87%. [5] In this paper, the authors utilized a DL approach to accomplish automatic plant disease only a few specific crops and their diseases, making it difficult to apply their results to a broader range of plant species. Accurately distinguishing between plant diseases with similar symptoms is also a significant challenge.…”

Section: Literature Surveymentioning

confidence: 99%

A Comparative Analysis of Convolutional Neural Networkbased Transfer Learning Models for Plant Disease Detection

2023

ijfans

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Agriculture holds a significant role in the economy of many countries by providing food security, employment opportunities, and contributing to the overall economic growth of the nation. However, the spread of plant diseases can pose a significant threat to crop production and result in reduced yields, food scarcity, and ecological imbalances. The current manual process of diagnosing plant diseases is not only time-consuming but also prone to errors, relying heavily on the expertise of pathologists. To tackle this challenge, this paper presents a user-friendly and accessible machine learning-based plant disease detection system that uses Convolutional Neural Network (CNN) based transfer-learning models to detect and categorize plants as either healthy or diseased and provides information on the precautions for the identified disease. By comparing the performance of three cutting-edge CNN architectures, VGG16, ResNet50, and EfficientNetV2S, on publicly available plant disease dataset, this paper aims to determine a model for detecting and classifying various plant diseases. We found that ResNet50 achieves the highest accuracy of 96.5%. This proposed system can enable timely and accurate disease detection, helping farmers take preventive measures and reducing the risk of crop loss.

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Section: Literature Surveymentioning

confidence: 99%

A Comparative Analysis of Convolutional Neural Networkbased Transfer Learning Models for Plant Disease Detection

2023

ijfans

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“…The proposed system consists of two parts, a preprocessing step, and a CNN-based classification step. In the pre-processing step, the leaf images are converted into grayscale images, followed by a segmentation process to obtain the leaf regions [4]. The segmented leaf regions are then resized to a fixed size, and a feature extraction process is applied to generate the feature vectors for the training and testing phase.…”

Section: Kolli Et Al(2021)mentioning

confidence: 99%

“…Detection of disease requires intensive labour and monitoring is a tiring job as manual labour is required. Image processing algorithms are developed and used to detect plant infection or disease by identifying the leaf area's colour features [4]. For colour segmentation, K means algorithms are used and GLCM is used for disease classification [3].…”

Section: Introductionmentioning

confidence: 99%

“…Plant pathologists use many different tools to figure out diseases in plants, but it is not convenient and available everywhere and requires time and money [6], to overcome and make it feasible these MLbased models are proposed to be utilized for the same at an early stage. CNN-based deep learning model was proposed for the accurate classification of plant disease [4], this uses RGB images, and pre-processing was undertaken followed by segmen-tation. Several classifiers have been used in the past few years by researchers such as k-nearest neighbour (KNN), support vector machines (SVM), artificial neural net-works (ANN), back propagation neural network (BPNN), Na¨ıve Bayes and Decision tree classifiers [7].…”

Section: Introductionmentioning

confidence: 99%

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