Research Projects

India, the world's second-largest producer of fruits and vegetables, faces significant post-harvest and transport losses due to under-ripe or overripe harvests. Manual inspections for fruit harvesting time in India often fail to choose the optimal time, leading to pre-ripe or post-ripe fruits. Pre-ripe fruits can cause nutritional insufficiency and loss of taste/odour, while post-ripe fruits can result in early decay and economic loss for traders. To address these issues, a project aims to develop an inexpensive, portable ripening detector that estimates the optimal moment of harvest for each fruit based on subsequent economic activity. The project will develop a prototype of a hand-held meter that assesses the accurate state of ripeness and the day of optimum ripeness from a pre-developed data model based on the fruit's bio-impedance. The bio-impedance will be measured using a miniature impedance analyzer, which will be reduced in dimension by principal component analysis (PCA). The ripening characteristics of fruits depend on factors such as species, subspecies, weather, farming techniques, soil, and micro-climate. Decision tree-based machine learning classifiers or neural network techniques will be used to include these effects in pattern recognition, classification, and regression table formulation. A smart phone app will be developed to show the date of optimum ripeness to farmers and consumers. This project addresses the scopes of precision agriculture and Make-in-India drive, focusing on predicting optimal ripeness days for different species of mangoes and papaya.