Authors - Jayashri D.Palkar, Anuradha S. Deshpande Abstract - The crop protection plays vital roles in the food supply and depends on how healthy the crops are, which influences the agricultural production; any adverse condition on crops will be leading to economic loss. Grapes find much use, being important and widely cultivated crops primarily in the Mediterranean regions that control an outgoing market of over 189 billion United States dollars. They are grown for consumption as fresh fruits, as well as in various processed forms such as drinks and sweets. These would be grapes, which, unlike many other plants, thrive and develop despite sickness, thus their control mechanisms must also function well. At the same time, many instances of diagnosis of these infections being wrong can lead to inadequate treatments for the known diseases, inducing even more generalized losses amounting from 5-80% on the crop under inspection. Current computer-based solutions may not be precise enough, leading to high running costs, operational difficulties, and image quality issues due to distortions. The body of literature based on different algorithms for the detection and classification of grape crop diseases remains vast and continues to grow rapidly with the newly emerging algorithms. It presents the overview of different disease-detection algorithms for optimizing grape disease detection, thereby aiding farmers in choosing the appropriate algorithm based on particular diseases and weather condition. This study presents a systematic review of various methods implemented in literature and provides a framework for use of AI-ML for effective detection of disease.
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