This paper introduces a novel hybrid approach, combining machine learning algorithms with feature selection, for efficient modelling and forecasting of complex phenomenon governed by multifactorial and nonlinear behaviours, such as crop yield. We have attempted to harness the benefits of the soft computing algorithm multivariate adaptive regression spline (MARS) for feature selection coupled with support vector regression (SVR) and artificial neural network (ANN) for efficiently mapping the relationship between the predictors and predictand variables using the MARS-ANN and MARS-SVR hybrid frameworks. The performances of the algorithms are com-pared on different fit statistics such as RMSE, MAD, MAPE, etc., using numeric agronomic traits of 518 lentil genotypes to predict grain yield. The proposed MARS-based hybrid models outperformed individual models such as MARS, SVR and ANN. This is largely due to the enhanced feature ex-traction capability of the MARS model coupled with the nonlinear adaptive learning ability of ANN and SVR. The superiority of the proposed hybrid models MARS-ANN and MARS-SVM in terms of model building and generalisation ability was demonstrated.

The yield of the crop is a complex function of a number of dependent traits, which makes yield prediction a statistically difficult task. A number of work on yield prediction using morphological characters already exists in the literature. Most of the work used statistical techniques such as linear regression and crop yield models, which assume a linear relationship between yield and the morphological traits; in actual practice, such a linear relationship is seldom achieved. With the advancement in the field of machine learning techniques, these methods can provide a viable alternative for dealing with nonlinear relationships for yield prediction. Globally, apples are the most consumed fruit. In this paper, attempts have been made to predict the yield of the apple crop using morphological traits. PCA was used for selection of the significant variables. These variables were later used as input variables in the ANN model with different hidden layers for predicting crop yield. The predictive performance of the model was evaluated using standard statistical tests. Sensitivity analysis was performed to find out the individual effects of each character on the apple yield. The study contributes to a better understanding of the complex relationships between crop yield and morphological traits.

Millets, recognized for their nutritional richness and adaptability to diverse environmental conditions, have regained global attention due to their sustainable agricultural practices and nutritional benefits. This paper explores the historical significance, global and Indian production trends, cost dynamics, export patterns, and governmental initiatives driving millet cultivation in India. By analyzing statistical data and research findings, the study underscores millets' potential to enhance food security, mitigate environmental impact, and foster socio-economic development.

Time series (TS) data is ubiquitous in various fields such as finance, economics, meteorology, and engineering. The analysis of TS data aims to understand the underlying patterns, make predictions, and inform decision-making. One of the fundamental techniques in TS analysis is decomposition, which breaks down a TS into its constituent components: trend, seasonality, and residuals. This chapter provides a comprehensive overview of TS decomposition, breaking down data into trend, seasonality, and residuals. It covers classical methods, such as additive and multiplicative models, advanced techniques like X-12-ARIMA and Seasonal-Trend decomposition using LOESS (STL), and recent approaches, including machine learning (ML) based decompositions. Practical applications in agriculture, meteorology, and economics, along with challenges like non-stationarity and nonlinear behavior, are discussed. The chapter offers guidelines for selecting appropriate methods and includes case studies for real-world insights. It is a valuable resource for researchers, data scientists, and professionals analyzing complex TS data.