Crop Plantation and Yield in Machine Learning...

Crop Plantation and Yield in Machine Learning: A Systematic Literature Review

Author Name : Mrs. A. Chitradevi, Dr. N. Tajunisha

ABSTRACT

Agriculture is a significant component of economic growth. An infrastructure of the country is so dependent on farming. A nation has decreased poverty and accelerated economic growth when trade, income growth, and employment are strongly correlated. Focusing on excellent agriculture is one of the finest strategies to accelerate growth and raise a country's status in the world since it yields rewards pretty rapidly. The agriculture sector continues to be one of the largest employers and is now seeing growth in several regions. But farming is a very difficult profession.[1] Farmers have had to come up with innovative solutions to a range of ecological problems for centuries. As a result, they have evolved over time into very adaptive people. In addition, unexpected difficulties have emerged in recent years as a result of changes in the environment and the world economy. Obtaining crop plantation and yield that is sustainable is never easy for a farmer. The main causes of agricultural yield uncertainty are: land types, resource availability, and weather variability. The main aims of the study include: (a) Analysis of several machine learning methods for crop sowing and yield prediction; (b) Analysing the various crop yield prediction algorithms; (c) To study the various crop yield methodologies obtainable. The study also looked into the literature carried out by researchers to evaluate the impact of multiple components on crop yields, and it was revealed that temperature and rainfall have the biggest effect on the yields of various crops.

Keywords: Crop yield prediction, Machine learning algorithms, Systematic literature review, crop planation, weather forecast, soil moisture