Plant Leaf Diseases Detection Using Deep Learning Algorithms

Binnar, Vikki; Sharma, Sanjeev

doi:10.1007/978-981-19-5868-7_17

Cited by 27 publications

(9 citation statements)

References 16 publications

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“…RFC combines the predictions of multiple trees, tends to generalize well to unseen data, and reduces the risk of overfitting. Notably, the proposed model with RFC has classified the tomato leaf dataset with 98.02% accuracy, and the near competitor [40] has obtained 97.52%. Compared to this model the proposed model achieved approximately 0.5% improvement in accuracy and even outperforming other stateof-the-art methods.…”

Section: Discussionmentioning

confidence: 96%

Plant Leaf Disease Detection Using Efficient Image Processing and Machine Learning Algorithms

Kiran,

Chandrappa

2023

Journal of Robotics and Control

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India is often described as a country of villages, where a majority of the population depends on agriculture for their livelihood. The landscape of Indian agriculture is approximately 159.7 million hectares. Agriculture plays a pivotal role in India's Gross Domestic Product (GDP), accounting for about 18% of the nation's economic output. Diseases and pests can have detrimental effects on crops, leading to reduced yields. These challenges can include the spread of plant diseases, infestations by insects or other pests, and the overall degradation of crop health. Early detection of diseases in crops is crucial for several reasons. Detecting diseases at an early stage allows for prompt intervention, such as applying appropriate pesticides or taking preventive measures. The main aim of this study is to develop a highly effective method for plant leaf disease detection using computer vision techniques. Here, leaf disease detection comprises histogram equalization, denoising, image color threshold masking, feature descriptors such as Haralick textures, Hu moments, and color histograms to extract the salient features of leaf images. These features are then used to classify the images by training Logistic Regression, Linear Discriminant Analysis, K-nearest neighbor, decision tree, Random Forest, and Support Vector Machine algorithms using K-fold validation. K-fold validation is used to separate the validation samples from the training samples, and the K indicates the number of times this is repeated for the generalization. The training and validation processes are performed in two approaches. The first approach uses default hyperparameters with segmented and non-segmented images. In the second approach, all hyperparameters of the models are optimized to train segmented datasets. The classification accuracy improved by 2.19% by utilizing segmentation and hyperparameter tuning further improved by 0.48%. The highest average classification accuracy of 97.92% is achieved using the Random Forest classifier to classify 40 classes of 10 different plant species. Accurate detection of plant disease leads to the sustained growth of plants throughout the growing span of the plants.

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Section: Discussionmentioning

confidence: 96%

Plant Leaf Disease Detection Using Efficient Image Processing and Machine Learning Algorithms

Kiran,

Chandrappa

2023

Journal of Robotics and Control

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“…Vikki et al [10] (2023) had proposed a solution to the challenges faced by agriculturist like diagnosing plant leaf diseases and destructive insects. The work employed models such as sequential model, InceptionV3, AlexNet and MobileNet to find out the plant leaf diseases by processing the images.…”

Section: Literature Surveymentioning

confidence: 99%

An Accurate Plant Disease Detection Technique Using Machine Learning

Raman

2024

EAI Endorsed Trans IoT

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INTRODUCTION: Plant diseases pose a significant threat to agriculture, causing substantial crop and financial losses. Modern technologies enable precise monitoring of plant health and early disease identification. Employing image processing, particularly Convolutional Neural Network (CNN) techniques, allows accurate prediction of plant diseases. The aim is to provide an automated, reliable disease detection system, aiding professionals and farmers in timely action to prevent infections and reduce crop losses. Integrating cutting-edge technologies in agriculture holds vast potential to enhance profitability and production. OBJECTIVES: The primary focus lies in developing an automated system proficient in analysing plant images to detect disease symptoms and classify plants as healthy or disease affected. The system aims to simplify plant disease diagnostics for farmers, providing essential information about leaf name, integrity, and life span. METHODS: The method aims to empower farmers by enabling easy identification of plant diseases, providing essential details like disease name, accuracy level, and life span. The CNN model accurately gauges the system's accuracy level. It further streamlines the process by offering a unified solution through a user-friendly web application, eliminating the need for separate interventions for affected leaves. the system saves farmers time by delivering crucial information directly. RESULTS: The Proposed web application proves to be a comprehensive solution, eliminating the need for farmers to search for separate interventions for affected leaves. The machine learning model exhibits a noteworthy accuracy of 96.67%, emphasizing its proficiency in making correct predictions for the given task. CONCLUSION: In conclusion, the paper successfully employed a CNN algorithm for precise plant disease prediction. With the proposed model deployment, farmers can easily access information about plant diseases, their life span, and preventive measures through the web application. By detecting illnesses early, farmers can promptly take remedial actions to mitigate sicknesses and minimize crop losses. The integrated approach holds promise for advancing agricultural practices and ensuring sustainable crop management.

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“…Farmers may find it challenging to correctly identify the symptoms of plant illness due to the minor features. Also, a lot of farmers having lack of the knowledge to identify the sickness, thus artificial intelligence(AI) can help them [1] [2].As AI fields have developed, convolutional neural networks have been applied for image processing. In this paper, a deep learning model is proposed to categorize various plant diseases.…”

Section: Introductionmentioning

confidence: 99%

Plant disease prediction system using advance computational Technique

Gulame,

Thite,

Patil

2023

J. Phys.: Conf. Ser.

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A vital sector of India’s economy is agriculture. Identification of plant infections is crucial to preventing crop damage and further disease. The majority of plants, such as apple, tomato, cherry, and grapes, have leaves that appear to have disease signs. The plant health can be monitored through images to precisely predict the disease and to take early preventative action. The traditional method is to manually inspect the plant leaf to identify the kind of disease, as done by farmers or plant pathologists. In this research, we presented a deep CNN model termed as Decompose, Transfer, and Compose (DTComp) for the classification of plant disease. The deep learning model makes predictions more quickly and precisely than manual plant leaf observation. Out of all the pretrained deep models, the ResNet50 model achieves the highest accuracy for classification. DTComp can handle any anomalies in the images using class decomposition approach to examine the class boundaries. The experimental findings demonstrated DTComp capacity for detecting plant disease instances on dataset gathered from multiple villages using the Kaggel Open Source platform. DTComp can successfully identify plant disease with a high accuracy of 98.30% from images. Additionally, this model can be deployable on real-time systems equipped with a Raspberry Pi and a camera module.

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Plant Leaf Diseases Detection Using Deep Learning Algorithms

Cited by 27 publications

References 16 publications

Plant Leaf Disease Detection Using Efficient Image Processing and Machine Learning Algorithms

Plant Leaf Disease Detection Using Efficient Image Processing and Machine Learning Algorithms

An Accurate Plant Disease Detection Technique Using Machine Learning

Plant disease prediction system using advance computational Technique

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