A Study on Various Methodologies for Plant Leaf Disease Detection and Classification

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Arpan Singh Rajput, Dr. Shailja Shukla, Dr. S. S. Thakur

Abstract

Disease detection is basically a principal aspect in ameliorating agricultural production. The presented research concentrates on devising Plant Leaf Disease (PLD) detection together with an identification process intended for larger fields of crop production. Here, an inclusive study on disease recognition together with the classification of plant leaves utilizing Image Processing (IP) methods is performed. Since this technique is unpredictable and inconsistent, the customary manual visual quality examination can’t be systematically stated. Furthermore, an extraordinary quantity of expertise is involved in plant disease diagnostics as well as the inconsistent processing times. Therefore, IP is implemented for plant disease recognition. Next, an imperative role is played by the Deep Learning (DL) together with Machine Learning (ML) classifiers in leaf disease classification. Centered upon an assessment of the formerly recommended top-notch techniques, a comprehensive discussion on disease detection together with classification performance is given. Lastly, the challenges and also some prospects for future ameliorations are discussed as well as classified.

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