Smart Fields: Enhancing Agriculture with Machine Learning

A substantial portion of the Indian population makes their livelihoods mostly from agriculture. However, there is a critical need to support our farmers with the current- state-of-the-art techniques that help to tackle the challenges posed by traditional agriculture practices Thus, the farmers can enable an increased income through monitoring the crop health, and high yield. This paper introduces a novel approach that integrates advanced computer vision and machine learning techniques for identifying diseases in crops. In addition, It also includes a smart Decision Support System (DSS) for recommending suitable fertilizers to improve productivity and crop yield. To further enhance its capabilities, we plan to utilize Internet of Things (IoT) technology for monitoring conditions that impact crop growth in time. This addition will contribute to recommendations by providing up to date insights on light conditions affecting crops. In this innovative approach, integrated sensors are utilized to gather soil parameters such as nitrogen, phosphorous, and potassium (NPK), as well as environmental factors which enables the accurate evaluation of soil and crop health. This information is then employed to predict the right crop to be grown, along with the suggestion of the optimal number of fertilizers needed to provide the necessary nutrition for a healthy crop and soil. This DSS system is designed to be user-friendly, with a mobile or web application interface. Hence the current system will be more useful for the farmers with current technology expertise.

I. Introduction

Since agriculture provides a livelihood for a sizable portion of India’s population, it is essential to the nation’s economic development. Nonetheless, the agricultural industry confronts complex difficulties, as farmers struggle to choose which crops are best to grow in a particular situation. Complications stem from a variety of soil properties, fluctuating weather patterns, common plant illnesses, and the continuous requirement for crop observation. This research suggests a thorough and cutting-edge strategy to assist agricultural decision-making in order to address these issues. Our suggested method makes utilises a wide variety of sophisticated ML models, such as the, XGBoost, Decision Tree approaches, Random Forest model, Support Vector Classification (SVC) based on accuracy values. Our goal is to find the best model for accurate crop detection through thorough analysis and comparison. The main objective is to increase the precision and effectiveness of selecting the best crops to grow, providing farmers with tools for data-driven decision-making.