A case study on prediction of sensitivity of tomato sepals to fungal infection using hyperspectral imaging

Tomato quality is dependent on growing conditions and chain conditions like humidity and temperature, as well as crop handling during harvest and post-harvest processes (transport, packaging, storage etc.). Like many other perishable fruits and vegetables, it is highly prone to postharvest losses, reaching up to 30% in some developing countries. Tomato is known to be highly susceptible to pathogenic fungi, such as Penicillium, Aspergillus and Mucor, which tend to attack crops with high moisture and nutrient content. Tomato tissue cell damage can occur due to changes in environmental conditions as well as damage during product handling. Such damage creates potential entrance for fungal spores which, given appropriate germination conditions, may infect stem, calyx,
sepals etc.
This work focuses on the sensitivity of sepals to fungal infection. In addition to the physical damage to the calyx, the calyx can also be physiological strong or weak, which is likely influenced by various growing conditions like radiation during fruit set and fruit growth, relative humidity during cultivation, more vegetative or generative growing crops, plant density and nutritional level of the crop. In case of presence of fungal spores and favorable fungal growing conditions, it is hypothesized that there is a correlation between weakness of the calyx (prior to fungal infection) and eventual fungal infection and/or the severity of infection.

Early sepal cell damage or weakness of calyx, is not visible to the naked eye, and, to our knowledge, no method exists for detecting this automatically prior to the infection. Hyperspectral imaging (HSI), especially in the Near-Infrared (NIR) range, has been shown to be sensitive to certain types of cell damage, such as bruises, but has not been demonstrated for the cell damage on the sepal tips and for early detection of weak sepals. As one of the novelty of this work, we investigate HSI to capture the sepal cell damage and weakness. To investigate the hypothesis, an experimental procedure was designed wherein hyperspectral images were acquired from several batches of tomatoes (from multiple origins, 1 cultivar) prior to visible evidence of fungal infection, potentially capturing the cell damage. The tomatoes were then introduced to conditions stimulating for fungal germination for multiple days. On the final day of the experiment, tomatoes are imaged (normal colour images) for gathering visual evidence of fungal severity for each sepal. Finally the first results are reported where a machine learning based approach Random forest regression was used to find a correlation between the spectral information from the first day of the experiment, and the fungal severity on the last day. Each sepal is described by the mean and standard deviation of its hyperspectral pixels values. 10-fold and group fold cross validation methods were used to evaluate the model performance. In reported experiments, groups correspond to different tomato origins. Predicted fungal severity correlated well with ground truth estimates with Pearson correlation of 0.73 and 0.66, and a high proportion of the variance explained with R2 score of 0.52 and 0.43 for 10-fold cross validation and group cross validation, respectively.