Agricultural Crop Recommendations Based on Productivity and Season:

A. V. Senthil Kumar Hindusthan College of Arts & Science, India https://orcid.org/0000-0002-8587-7017 Aparna M. Hindusthan College of Arts & Science, India Amit Dutta All India Council for Technical Education, India Samrat Ray IIMS, India Hakikur Rahman Presidency University, Bangladesh https://orcid.org/0000-0002-2132-1298 Shadi R. Masadeh Isra University, Jordan Ismail Bin Musirin Universiti Teknologi Mara, Malaysia Manjunatha Rao L. National Assessment and Accreditation Council, India Suganya R. V. VISTAS, India Ravisankar Malladi Koneru Lakshmaiah Education Foundation, India https://orcid.org/0000-0002-8250-6595 Uma N. Dulhare Muffakham Jah College of Engineering and Technology, India https://orcid.org/0000-0002-4736-4472 Agricultural Crop Recommendations Based on Productivity and Season

This chapter aims to develop an agricultural crop recommendation system leveraging the power of machine learning algorithms. The proposed system takes into account crop productivity and prevailing season as crucial factors in making appropriate crop suggestions. The authors proposed the SVM algorithm, which was trained and evaluated on a comprehensive dataset comprising historical agricultural data with diverse features such as climate variables, soil properties, and geographical factors. The data was further segmented based on seasonal patterns to provide crop recommendations tailored to specific timeframes. The models' performance was evaluated using standard metrics, and an ensemble approach was considered to enhance the system's robustness. Ultimately, the developed system offers farmers and agricultural experts a valuable tool for making informed decisions, optimizing crop selection, and increasing overall agricultural productivity
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