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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.
Manju et al. (Fri,) studied this question.