Multispectral Imaging Enables High-Throughput Detection of Feijoa Fruit Defects

Feijoa fruits are known for their pronounced post-harvest ripening. Phytopathogen-infected specimens pose a significant risk to storage stability and overall fruit quality. Early detection and removal of defective fruits during the initial storage stages are critical for maintaining market value and preventing the spread of disease. In this study, we analyze how the multispectral reflectance properties of the feijoa surface change in response to various defects. ‘Superba’ cultivar fruits were selected, including healthy controls and samples exhibiting bruises, anthracnose, stink bug damage, tissue suberization, and gray mold. Biochemical analyses were conducted to measure the levels of organic acids, sugars, ascorbic acid, and total polyphenols. Multispectral imaging was performed with a 12-channel camera operating in the 400–1000 nm wavelength range. Results showed that the fruits affected by gray mold had the lowest concentrations of malic and citric acids but the highest levels of succinic acid. Fruits with anthracnose or insect damage exhibited the highest sugar content. Distinct differences in spectral reflectance were observed between healthy and affected areas of fruit. Based on these findings, an image processing algorithm for defective fruit detection was developed.