Authors - Vittal Badami, Suvarna Kanakaraddi, Priyadarshini Kalwad Abstract - This research investigates the approaches for identifying and classifying plant leaf diseases from digital images using deep neural networks. While diseases can affect any part of a plant and occasionally go undetected, there are some physical characteristics or symptoms that the plants exhibit that can be quickly detected using vision. These diseases can be identified and classified by machine learning algorithms using a wide range of techniques like deep neural networks, regression analysis, colour analysis and so on but not all techniques are appropriate for identifying all types of plant leaf diseases. It might be difficult to determine which approach is best for particular type of disease detection. This paper mainly focuses on image classification based on deep neural networks, object detection based on thresholding and image transformation and severity quantification with different methods like colour analysis which include Histogram of Oriented Gradients (HOG) and spot analysis and leaf area measurement using open source tool and segmentation-based calculation of affected area. This detailed analysis will be helpful to researchers working on algorithm optimization for image classification, object detection and severity quantification in identifying plant leaf diseases.
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