Plant Disease Detects Based on Machine Learning Algorithms

Registration ID: IJNRD_305967

Published ID: IJNRD2504447

: https://doi.org/10.56975/ijnrd.v10i4.305967

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Koteswararao yenni, V.kirankumar

Keywords: Plant Disease Detection, Machine Learning, Convolutional Neural Networks, Precision Agriculture, IoT, Edge Computing, Image Classification

Abstract: Rapid advances in agricultural technology created opportunities for machine learning (ML) applications to detect plant diseases. The proposed framework implements an efficient scalable approach to plant disease detection by using machine learning algorithms which focus on image-based analysis. Through a diverse leaf image plant dataset conduct evaluations of supervised learning systems that combine Support Vector Machines (SVMs), Random Forests and Convolutional Neural Networks (CNNs). A new system was developed to detect strong features and precisely diagnose the plant disease among different plant varieties using a CNN detection system with several convolutional layers. Contrast adjustment reductions and data augmentation preprocessing techniques were used to boost system performance, because they assisted in models generalizing on unobserved data. We show our study that the CNN model gives out excellent 96.8% accuracy superior to the conventional algorithms in all performance metrics such as precision, recall and F1-score. It is a system for an early accurate and economically feasible disease detection that would support precision farming by keeping the crops safe and the food supply steady. Field level disease surveillance is implemented with an investigation of real-time deployment strategies that use IoT and edge computing technologies. Results from this articulated framework are shown to have great potential for converting agricultural operations to sustainable farming practices.

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Koteswararao yenni & V.kirankumar (April-2025). Plant Disease Detects Based on Machine Learning Algorithms. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(4), e342-e348. https://doi.org/10.56975/ijnrd.v10i4.305967

Volume 10 Issue 4, April-2025

Pages : e342-e348

Paper Reg. ID: IJNRD_305967

Published Paper Id: IJNRD2504447

Research Area: Science and Technology

Author Type: Indian Author

Country: srikakulam dist, andhrapradesh, India

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Crossref DOI: https://doi.org/10.56975/ijnrd.v10i4.305967

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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