Hybrid Mode of Crop Yield Prediction Using Various Machine Learning Algorithms

Over 50% of India's population depends on agriculture for existence, making it the foundation of the country's economy. Variations in weather, climate, and other environmental factors are now a significant threat to the continued success of agriculture. The decision support tool for Crop Yield Prediction (CYP), which includes assisting decisions on which crops to plant and what to do during the growth season of the crops, is where machine learning (ML) plays a vital role. The current study focuses on a systematic review that extracts and synthesizes the CYP traits. In addition, a number of approaches have been created to analyse agricultural production prediction utilizing Artificial Intelligence techniques. In this paper, the predictions provided by the Random Forest and Naive Bayes algorithms will assist the farmers in choosing which crop to cultivate to produce the greatest yield by taking into account variables such as water, wind, sunlight, temperature, rainfall, and photosynthetic activity. Pollinating agents, which analyses several ML strategies used in the field of agricultural yield estimation and offered a complete study in terms of accuracy employing the techniques, boost the fertility of the soil.