Crop Recommendation Using Supervised Learning Techniques

• Published in: 2023 4th International Conference on Smart Electronics and Communication (ICOSEC) pp. 1076 - 1084
• Main Authors: Ramya, J., Sankar, M.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 20.09.2023
• Subjects: Costs; Crop Recommendation; Crops; Data models; Decision Tree; Machine Learning; Productivity; Soil; Supervised learning; Supervised learning Random Forest; Support Vector Machine and K-Nearest Neighbor; Support vector machine classification
• DOI: 10.1109/ICOSEC58147.2023.10276199

The agricultural sector has a significant impact on the Indian economy. Researching crop production is crucial to enhance its contribution to the economy. Studies on crops, irrigation, and farm machinery are necessary to boost crop output. It is essential to select not only the most suitable crop for the area and environment but also high-yielding seeds. Farmers base their crop selection on consumer demand and previous season prices. Choosing an unsuitable crop for the soil type can be challenging and often leads to significant production setbacks, resulting in reduced output and lower returns for farmers. Crop recommendation plays a significant role in enhancing agricultural productivity and ensuring the optimal selection of crops based on specific land and environmental conditions. This study proposes a supervised learning approach utilizing the Random Forest algorithm to deliver precise and effective crop recommendations. The aim is to support farmers in making well-informed decisions regarding crop selection, seed varieties, and fertilizer quantities for cultivation. the importance of crop recommendation in enhancing agricultural productivity and selecting optimal crops based on specific land and environmental conditions. It introduces the proposed supervised learning approach using the Random Forest algorithm and highlights the objectives of supporting farmers in making informed decisions about crop selection.