Crop Selection Method to maximize crop yield rate using machine learning technique

TLDR

Agriculture planning is crucial for economic growth and food security, and crop selection—affected by production rate, market price, and policies—poses a complex decision problem that has been addressed using statistical and machine learning techniques. This paper proposes the Crop Selection Method (CSM) to solve the crop selection problem and maximize net yield rate over a season, thereby promoting economic growth. CSM evaluates crop options to identify those that yield the highest net output. The proposed method may improve net yield rate of crops.

Abstract

Agriculture planning plays a significant role in economic growth and food security of agro-based country. Se- lection of crop(s) is an important issue for agriculture planning. It depends on various parameters such as production rate, market price and government policies. Many researchers studied prediction of yield rate of crop, prediction of weather, soil classification and crop classification for agriculture planning using statistics methods or machine learning techniques. If there is more than one option to plant a crop at a time using limited land resource, then selection of crop is a puzzle. This paper proposed a method named Crop Selection Method (CSM) to solve crop selection problem, and maximize net yield rate of crop over season and subsequently achieves maximum economic growth of the country. The proposed method may improve net yield rate of crops.