An overview paper on automatic detection of numerous plant diseases that impact leaves

A growing field in India that can assist farmers in many ways is the identification of plant diseases. Plant disease reduces the mass production in agriculture. Every time due to heavy loss in agriculture, farmers face many challenges to raise up their needs. This study analyzes various diseases that affect various plants due to the following criteria like environmental condition, types of plant formed from Monocot and Dicot family, crop rotation and Ph value of the soil. The existing system analyzed the diseases based on one of the criteria. Although the strategy is to be followed to identify plant diseases at the early stage with various aspects so that the production in agriculture could be increased. The majority of machine learning algorithms already exist and may be improved upon to yield more accuracy. Xanthomonas wilt, Black and Yellow Sigatoka, Bunchy Top Virus, Black Sigatoka, Fusarium wilt, Cassava Bacterial Blight (CBB), Cassava Brown Steak Disease (CBSD), Cassava Green Mite (CGM), and Cassava Mosaic Disease (CMD), Yellow vein mosaic virus, and Powdery Mildew are a few of the diseases of the plants grown on crop rotation which are the subject of study of this paper. Plant illnesses are classified according to disease in the many open-source datasets that are accessible. For deeper understanding, we have provided a list of references. Here, we’ve examined the approaches that are now in use for automatically detecting various diseases of plant leaves. We present an approach for accurately identifying early signs of plant diseases from a variety of perspectives.