Machine Learning and IoT Driven Precision Agriculture for Indian Crop Selection

The optimal selection of efficient crops is an expeditiously emerging discipline that is imperative in augmenting agrarian practices. Appropriate crop assortment is pivotal in agriculture, empowering farmers to make well-informed choices approximately the most suitable plants for their land-living and weather conditions. Conventionally, this procedure is profoundly reliable on professional know-how, which proved time-consuming and hard work in depth. Furthermore, given the expected populace of eight billion by 2050, the dire want to deliver greater food sustainably becomes imperative. Machine Learning strategies and IoT features can play an essential role in efficiently automating crop guidelines and detecting pests to allow farmers to optimize their yield from the land while concurrently keeping soil fertility and replenishing vital nutrients. Soil parameters along with nitrogen, phosphorous, potassium (NPK), temperature, PH, humidity, and rainfall are taken into consideration for envisaging the productiveness of the soil and additionally to count on the correct crop to be grown and nutrients vital for it. The paper proposed machine-learning algorithms (ML) and IOT real-world testing that leverage numerous talents, including soil composition and climate statistics, to appropriately propose the most appropriate crop patterns. Through significant evaluation of an entire ancient data set, we've executed close to ideal accuracy with the resource of training and testing models of the tool gaining knowledge of algorithms with various configurations. The research paper established correct empirical consequences continually over ninety-five percent across all models, with the highest finished accuracy attaining ninety-nine percent.