Abstract
Abstract: Disease has always been one of the main reasons for the decline of apple quality and yield, which directly harms the development of agricultural economy. Therefore, precise diagnosis of apple diseases and correct decision making are important measures to reduce agricultural losses and promote economic growth. The forecasting of crop disease is a significant and innovative topic; the debasement in both quality and quantity in the yield of crops is due to various conditions, which in turn alters the economy of countries like India, where a majority of the population hinge on crop and cultivation. It is challenging to keep an eye on disease in plants through conventional approaches, which involve a lot of effort, time, and experience. Automated disease identification of plants is a critical research field. In this paper, focus is laid on how machine learning helps in apple disease detection using various prediction algorithms using weather and disease datasets.
Published Version
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