Machine learning techniques for non-destructive estimation of plum fruit weight.

Plum fruit fresh weight (FW) estimation is crucial for various agricultural practices, including yield prediction, quality control, and market pricing. Traditional methods for estimating fruit weight are often destructive, time-consuming, and labor-intensive. In this study, we addressed the problem of predicting plum FW using artificial intelligence (AI) methods based on fruit dimensions.

Application of image processing and soft computing strategies for non-destructive estimation of plum leaf area.

Plant leaf area (LA) is a key metric in plant monitoring programs. Machine learning methods were used in this study to estimate the LA of four plum genotypes, including three greengage genotypes (Prunus domestica [subsp. italica var.