DEVELOPING A PREDICTIVE MODEL BY EMPLOYING AUGMENTED IMAGE PROCESSING, PATTERN RECOGNITION WITH A LINKED UNSUPERVISED CLASSIFICATION TYPE ALGORITHM IN EARLY DETECTION AND MITIGATION OF LEAF DISEASES TO SAFEGUARD CROPS
Lavaneesh Sharma
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Abstract
Plants are basis for sustaining the food chain and are responsible for bolstering almost all forms of life including humans on earth by providing oxygen and other important resources. This imperatively calls for their protection at every stage on our behalf. Agriculture crops, a subset of plant universe, are quintessential form for feeding majority, of the population of the world. In account for significant increase apart from variety and numbers, manual human plant by plant or leaf by leaf detection renders this technique worthless and call for an automated ‘Machine Learning’ technique. Here the goal is to deploy a preemptive and autonomous disease detection technique in plants via image processing coalesced with the implementation of an unsupervised classification algorithm to perpetrate the disease and accordingly take necessary corrective action on farmer’s behalf. Further, we are reviewing the existing work done by a researcher coupled by enumerating common disease symptoms caused by various pathogens