Estimation of Mung Bean [Vigna radiata (L.) Wilczek] Pod Shell Rate Using Curve Fitting and Artificial Neural Network Techniques

Abstract

Mung beans are a nutrient-dense dietary option since they are low in fat and high in fiber, protein, and vitamins. Estimating the amount of pod shells is important because it gives information about the amount of seeds contained in the pods and indirectly about the yield. The study aimed to predict the pod shell rate of mung bean genotypes and cultivars in pod and seed sizes by using curving fitting and artificial neural networks. The produced equation for predicting of shell weight rate of the genotypes and varieties was formulized as SWR = (-1.349e -13 ) + (0.999 x TW) + (0.999 x SIW) + (1.416e -18 x TW²) -[1.908e -17 x (TW x SIW)] where SWR is shell weight rate, TW is total weight, and SIW is seed internal weight. On the other hand, this research discusses the use of an artificial neural network (ANN) model to predict the shell rate of legumes based on various input parameters such as pod length, pod width, pod thickness, seed length, seed width, and seed thickness. The R 2 values obtained from the ANN analysis indicate that the model predicts shell rate with 87% accuracy.