Crop Yield Prediction

By Pallavi Shankarrao Mahore, Aashish A. Bardekar. International Journal of Scientific Research in Computer Science Engineering and Information Technology, 2021

63 citations recorded by the source corpus.

Cotton, popularly known as White Gold has been an important commercial crop of National significance due to the immense influence of its rural economy. Transfer of technology to identify the quality of fibre is gaining importance for crop yield is compared with Random forest, Support Vector Machine, Weather, K Nearest neighbor. , which shows better performance results for each selected weather parameters. Crop yield rate depends upon various parameters such as the geography of area, soil type, soil nutrients, soil alkaline, weather condition, etc. The combination of these parameters can be used for selection of suitable crops for a farm or land to gain maximum yield. In this manuscript, soil and weather parameters such as soil type, soil fertility, maximum temperature, minimum temperature, rainfall are used to identify suitable crops for specified farm or land.

Subfields: General Agricultural and Biological Sciences, Plant Science

View Paper: https://www.semanticscholar.org/paper/ca1bddba3da59f86bc1c353f6e42f91a2a1c714f

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In Plant Science

1. Dionysis D. Bochtis
2. Aristotelis C. Tagarakis

Citations in the corpus

• Machine Learning in Agriculture: A Comprehensive Updated Review (2021, background): “In turn, identifying such kinds of relationships mandates comprehensive datasets along with powerful algorithms such as ML techniques [53].”