Soil Analyser - Revolutionizing Agriculture through Wireless Sensor Networks and Machine Learning

Abstract

Abstract: Crop yield analysis, traditionally dependent on the experiential knowledge of farmers, has undergone a transformative revolution with the advent of machine learning. Anticipating yields stands as a critical concern for farmers eager for insights into their upcoming harvests. In the past, such predictions relied on a farmer's intimate familiarity with their specific field and crop. However, the challenge has persisted in effectively leveraging available data. Enter machine learning-a rapidly advancing field with compelling solutions. This research introduces a system that utilizes historical agricultural data to forecast crop yields. By employing sophisticated machine learning algorithms such as Support Vector Machine and Random Forest, this system not only predicts yields but also recommends optimal fertilizers for each crop. At its core, the emphasis is on constructing a robust predictive model that reliably anticipates future crop production. The paper meticulously delves into the realm of crop yield prediction, exploring the subject through the lens of advanced machine learning techniques.