IOT Assisted Smart Farming using Data Science Techniques

Abstract

The rising global population demands a high yield of crop production. At present, farmers grow crops for all the people, but in case of contracting horticultural grounds and exhaustion of limited regular assets due to many reasons, and a massive increase in population, the need to improve ranch yield has turned out to be essential. Nowadays, there are various startups, technology innovators, and steps taken by the government that work to enhance total crop production. All those innovations taken for the farming framework are called smart Farming (SF). Smart Farming includes consolidating data and correspondence advances into apparatus, hardware, and sensors in the rural creation framework. The advancement of technologies must be reduced to convey meaningful information. The economy of nations like India is highly dependent on agricultural production. So disease detection in plants using an efficient algorithm is supposed to be a vital job in the farming field. This research paper is presented in three-fold:(1) Efficient way to detect disease and find cavity area; It presents Image Segmentation Algorithm (2) Using data analysis in different ways which will work for crops in a better way. The paper also presents two analysis methodologies, one is based on using an optical transducer for detecting the presence of Nitrogen (N), Phosphorous (P), and Potash (K) in soil, and the other analysis is based on the moisture content of the soil using sensors. (3)using machine learning algorithms to predict the number of fertilizers based on the collected features of soil samples, which will help farmers in amount prediction.