Hyperspectral imaging detection of decayed honey peaches based on their chlorophyll content

- Three optimal wavelengths were selected based on chlorophyll content via SPA.
- The band ratios were used to locate the position of disease pots and classify diseased peaches.
- The PLSDA model based on three band ratios show 98.75% accuracy to classify diseased peaches.
- CRC images were used to visualize the spatial distribution of diseased regions on the peaches.

Honey peach is a very common but highly perishable market fruit. When pathogens infect fruit, chlorophyll as one of the important components related to fruit quality, decreased significantly. Here, the feasibility of hyperspectral imaging to determine the chlorophyll content thus distinguishing diseased peaches was investigated. Three optimal wavelengths (617 nm, 675 nm, and 818 nm) were selected according to chlorophyll content via successive projections algorithm. Partial least square regression models were established to determine chlorophyll content. Three band ratios were obtained using these optimal wavelengths, which improved spatial details, but also integrates the information of chemical composition from spectral characteristics. The band ratio values were suitable to classify the diseased peaches with 98.75% accuracy and clearly show the spatial distribution of diseased parts. This study provides a new perspective for the selection of optimal wavelengths of hyperspectral imaging via chlorophyll content, thus enabling the detection of fungal diseases in peaches.