Forecast of agri-residues generation from rice, wheat and oilseed crops in India using machine learning techniques: Exploring strategies for sustainable smart management

Abstract

Management of agri-residues generated in large quantities necessitates for its accurate estimation. Data analysis using machine learning methods can predict the agri-residues generation. The objective of the study was to forecast agri-residues generation from rice, wheat, and oilseed crops in India using ML methods and their sustainable uses. Prediction of agri-residues was done first by forecasting the crop production via the application of ML techniques for the period 2022 to 2030, and then the amount of crop residues generation calculated by multiplying the crop productions with the residues-to-product-ratio (RPR) values of the respective crops. RPR was estimated by using the gravimetric ratio of the residue to the actual crop production. The crop-specific RPR values were taken from various earlier studies in Indian context. The RPR values of 1.73 for the rice, 1.65 for wheat, and 2.6 for the oilseed crop were used as a conversion factor for residues calculation. Machine learning models linear regression, sequential minimal optimization regression (SMOreg), M5 Rule, and Gaussian process were used in the study. SMOreg performed better in models tested by coefficient of determination, root mean square error, and mean absolute error. The models predicted the generation of residues in 2030 as rice straw and husk 195.76 Mt to 277.68 Mt, wheat straw 188.62 Mt to 266.95 Mt, and oilseed stalk and oil cakes 55.61 Mt to 96.30 Mt in India. An overview of the management of agri-residues discussed. Estimation of agri-residues can provide an opportunity to utilize them with the best possible ways, lessen pollution and promote a zero-waste strategy.