Abstract:

Plant diseases pose a threat to consumers, producers, the ecosystem, and the global economy. In India, bugs and infections kill 35% of agricultural products, costing cultivators money. Due to the way they biomagnify and are toxic, unpredictable pesticide use also poses a significant health risk. These side effects may be lessened with tailored treatments, agricultural monitoring, and early disease detection. Utilizing external indicators, experts in farming can typically identify the majority of diseases. However, professional assistance is not always readily available to farmers. The first technology to combine automatic illness diagnosis, tracking, and prediction is ours. Ranchers can utilize a web-based application to quickly and precisely distinguish sicknesses and track down cures by catching affected plant areas. The most recent AI (artificial intelligence) forecasts for licensingadvancing security in cloud-based image handling The AI model continuously learns from clientsubmitted images and expert advice to improve accuracy. The website can also be used by farmers to communicate with experts in their field. To create disease thickness maps with spread meters for preventative measures, a cloud-based collection of geotagged photos and microclimatic limits is used. Through an online platform, specialists can use geological senses to direct disease research. For the purpose of training the artificial intelligence model (CNN) in our experiments, we made use of massive disease databases constructed from plant images gathered from various fields over a seven-month period. Plant experts affirmed the computerized CNN model's ID utilizing test photographs. The illness was found more than 95% of the time. Our response is a stand-out, versatile, and reasonable innovation for infection the board of a wide assortment of cultivating food plants for ranchers and specialists looking for environmentally practical yield creation

Description:

Artificial intelligence, cloud computing, Convolution neural network