NIRS-based On-tree Mango Fruit Maturity and Quality Estimation

Abstract

Estimation of on tree mango maturity is essential for the prediction of harvest time. Dry matter (DM) is a useful index in deciding mango maturity, and post-harvest quality. Existing NIR based maturity meters employ machine learning regressors to predict a particular maturity index value (such as DM, oBrix, or etc.) and then impose a hard threshold on predicted value to estimate maturity state of the fruit. In this paper, a new non-destructive handheld maturity meter is developed for on-tree harvest maturity estimation. The developed maturity meter directly estimates the maturity state (mature/immature) using a classifier trained on maturity labels assigned through standard DM thresholds for investigated mango varieties. To develop the hardware of the device, a commercial-off-the-shelf development kit of NIR micro-spectrometer in the spectral range of 400 - 1100 nm was employed with an intel compute stick, a micro-halogen lamp, a lithium battery, and a display. The application software (developed in C++) is designed to collect interactance spectra, noise removal, dimensionality reduction, and classification of maturity state. Performance of the developed device is evaluated by on tree test samples of mango fruit of different season. Comparison of both the literature reported indirect maturity estimation and proposed direct maturity classification is conducted. The test results show that the maximum accuracy achieved using indirect maturity estimation using hard thresholds is 55.9%. Whereas, direct maturity classification using KNN achieved 88.2% accuracy in predicting the maturity state (mature/immature) of the test mangoes. Overall results show that the developed DM mango maturity method has considerable potential to detect maturity state of mangoes in practical situations.