Atharva Gadgil Jay Admane Vyanktesh Dongre Dr. Dipli Shende

Agriculture plays a role in India’s economy serving as the backbone of the country. Our main objective is to address the standing challenges faced by agriculture, which often involve inefficient traditional methods, increased expenses, and suboptimal yields. Therefore, it is crucial to make progress in this field. To tackle this issue, we are developing a solution that can significantly advance agriculture with accuracy. By incorporating sensors for NPK (Nitrogen. Phosphorus, Potassium) analysis, we enable real-time data collection on soil levels. This eliminates guesswork and approximations when applying fertilizers. The data is collected at the cloud and analyzed using Naıve Bayes algorithm. Our project aims to provide farmers with crop predictions tailored to their specific soil conditions and regional climate. The method proposed here on analyzing the soil nutrients, soil temperature, humidity and rainfall using sensor nodes. By leveraging the potential of Machine Learning model such as Na ıve Bayes and Cloud computing, we aim to process data faster and make accurate predictions. The results of our research efforts have the potential to be transformative for farmers. They will gain the ability to make informed decisions leading to crop yields, reduced costs, and long-term sustainability.

https://doi.org/10.58357/jedcs.2024.v3.i2.11