Leveraging Machine Learning Algorithms for Enhanced Crop Yield Predictions: Optimizing Rainfall, Fertilizer, and Pesticide Data - H P, Kousalya; I T, Nishanth; R, Anilkumar; Hema Prabha (INFOCOMP Journal of Computer Science, 2025)
Crop yield is essential for productivity and efficiency of crop production, and it is vital for food security and economic stability. Here we are predicting the crop yield by considering agronomic factors such as annual rainfall, fertilizers, and pesticides. The rainfall patterns specify the availability of water; fertilizers balance the impacts on the environment and nutrient availability; and pesticides reduce the losses in productivity and protect crops from pests and diseases. By considering these three factors, it has both a positive and negative outcome, because if annual rainfall is high and a farmer will apply necessary fertilizers and required pesticides, it reflects the quantity and quality of crop production in both positive and negative ways. If the annual rainfall is expected, a farmer can achieve expected crop production by using the required amount of fertilizers and pesticides. If the annual rainfall is low, it leads to drought conditions and negatively impacts crop growth, and it also positively impacts some categories of crop yield like jowar, ragi, etc. While considering these complex interrelationships among these three factors helps in understanding the crop yield and its increasing demands of food sustainably at a global scale. The data considered for this research consists of agricultural data for multiple crop yields across various states of India from the year 2016 to 2020. The dataset attributes are including crop, crop year, season, state, area, production, annual rainfall, fertilizers, pesticides, and crop yield. Using machine learning techniques such as random forest, linear regression, and K-Nearest Neighbors (KNN). By using random forest, we can analyze a large number of input data, such as agronomic factors, and we can predict the accuracy of crop yield. Linear regression is used to establish relationships between input variables and crop yield. KNN, also known as an algorithm that machines learn and use, applies to classification and regression. It finds a given number of the closest data points around a new point in the training dataset and then predicts according to the neighbors. By doing this, considering these methods, it tells which of the agronomic factorsârainfall, fertilizers, and pesticidesâhas the maximum effect on crop yield based on the models.