Integrated Framework for Yield Prediction and Seed Quality Assessment in Chickpea (Cicer arietinum L.) using Artificial Intelligence

Abstract

Background: Chickpea (Cicer arietinum L.) is one of the most important pulse crops in the world and it is greatly contributing to global protein security and sustainable agricultural systems. However, fluctuations in agro-climatic conditions and genotype-environment interactions considerably influence yield performance and seed quality traits. Accurate prediction and assessment mechanisms are therefore essential to support precision agriculture and breeding programs. Methods: This paper proposes an integrated biological-computational framework utilizing a novel hybrid attention-based random forest optimizer using artificial intelligence (HARFO-AI) for yield prediction and seed quality assessment in chickpea. Field experiments were conducted to determine the important morphological, physiological, and seed-related variables like plant height, chlorophyll index, number of pods per plant, weight of 100 seeds, germination percentage, vigor index and moisture content. These biological variables were used for training and testing the HARFO-AI model. Statistical parameters like coefficient of determination, root mean square error, mean absolute error, and classification accuracy were used to evaluate the performance of the HARFO-AI model. Result: The proposed framework yielded a prediction with R = 0.94, RMSE = 0.19 t ha, and MAE = 0.15 t ha, accuracy of 93.1%. The classification accuracy of seed quality was 95.2%. The comparative analysis showed that the performance was about 20% higher than that of the traditional regression models. The findings indicate that integrating biologically significant parameters with advanced AI optimization techniques enhances predictive reliability and provides an effective decision-support tool for precision breeding and sustainable chickpea production.