Predicting sensitivity of recently harvested tomatoes and tomato sepals to future fungal infections

Abstract

Tomato is an important commercial product which is perishable by nature and highly susceptible to fungal incidence once it is harvested. Not all tomatoes are equally vulnerable to pathogenic fungi, and an early detection of the vulnerable ones can help in taking timely preventive actions, ranging from isolating tomato batches to adjusting storage conditions, but also in making right business decisions like dynamic pricing based on quality or better shelf life estimate. More importantly, early detection of vulnerable produce can help in taking timely actions to minimize potential post-harvest losses. This paper investigates Near-infrared (NIR) hyperspectral imaging (1000–1700 nm) and machine learning to build models to automatically predict the susceptibility of sepals of recently harvested tomatoes to future fungal infections. Hyperspectral images of newly harvested tomatoes (cultivar Brioso) from 5 different growers were acquired before the onset of any visible fungal infection. After imaging, the tomatoes were placed under controlled conditions suited for fungal germination and growth for a 4-day period, and then imaged using normal color cameras. All sepals in the color images were ranked for fungal severity using crowdsourcing, and the final severity of each sepal was fused using principal component analysis. A novel hyperspectral data processing pipeline is presented which was used to automatically segment the tomato sepals from spectral images with multiple tomatoes connected via a truss. The key modelling question addressed in this research is whether there is a correlation between the hyperspectral data captured at harvest and the fungal infection observed 4 days later. Using 10-fold and group k-fold cross-validation, XG-Boost and Random Forest based regression models were trained on the features derived from the hyperspectral data corresponding to each sepal in the training set and tested on hold out test set. The best model found a Pearson correlation of 0.837, showing that there is strong linear correlation between the NIR spectra and the future fungal severity of the sepal. The sepal specific predictions were aggregated to predict the susceptibility of individual tomatoes, and a correlation of 0.92 was found. Besides modelling, focus is also on model interpretation, particularly to understand which spectral features are most relevant to model prediction. Two approaches to model interpretation were explored, feature importance and SHAP (SHapley Additive exPlanations), resulting in similar conclusions that the NIR range between 1390–1420 nm contributes most to the model’s final decision.

Chlorine Dioxide Treatment Modulates Ripening-Related Genes and Antioxidant System to Improve the Storability of Tomato

“…CI symptoms usually become pronounced under market shelf conditions following cold storage, thereby reducing consumer desirability [1,2]. Storage above 10 °C is recommended to avoid CI in tomatoes; however, high storage temperatures accelerate softening and promote microbial growth especially on calyces, resulting in quality deterioration [1,[3][4][5]. Te calyx is considered an indicator of the freshness and quality of the tomato fruit.…”

“…However, calyces are vulnerable to microbial spoilage (epiphytic bacteria and molds) during shipping and storage and are usually the frst part of tomatoes to show fungal growth. Te presence of mold on the calyx infuences the marketability and shelf-life of tomatoes, even if the fruit itself is not infected [5]. Tus, the maintenance of microbial safety and quality of tomatoes during storage and distribution requires investigation.…”