Advanced Machine Learning Techniques for Early Detection and Classification of Diseases in Vegetable Crops to Enhance Agricultural Efficiency and Sustainability

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Krina Masharu; Tosal Bhalodia; Kinjal Raja

Advanced Machine Learning Techniques for Early Detection and Classification of Diseases in Vegetable Crops to Enhance Agricultural Efficiency and Sustainability

Authors: Krina Masharu, Tosal Bhalodia, Kinjal Raja

Unique Paper ID: 178931
Volume: 11
Issue: 12
PageNo: 6782-6784

Keywords: Machine Learning KNN Crop Disease Detection Agricultural Sustainability Early Disease Detection Confusion Matrix Precision Agriculture Smart Farming.

Abstract:
Early and accurate disease detection in vegetable crops is critical for enhancing agricultural efficiency and sustainability. This paper presents a machine learning approach utilizing the k-Nearest Neighbors (KNN) algorithm to detect and classify diseases in vegetable crops, achieving a high accuracy of 96%. The proposed approach leverages a balanced dataset, avoiding data duplication, and demonstrates effectiveness through a comprehensive evaluation, including confusion matrix analysis. The study integrates findings from recent advancements in lightweight convolutional neural networks (CNNs), transfer learning, and optimized deep learning techniques, as referenced in contemporary literature.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 12
PageNo: 6782-6784

Advanced Machine Learning Techniques for Early Detection and Classification of Diseases in Vegetable Crops to Enhance Agricultural Efficiency and Sustainability

Available:https://ijirt.org/article?manuscript=178931

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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