Imaging Techniques for Evaluation of Ripening and Maturity of Fruits and Vegetables

Optimal harvesting time of fruits and vegetables is an important factor, which is directly associated with the postharvest quality of the produce and shelf life. Depending on the variety of horticultural products, maturity can be assessed using internal properties like moisture, sugar, starch, oil content, soluble solid content (SSC), titratable acidity (TA), SSC/TA, pH, and firmness, or using external properties like surface or peel color (chlorophyll, carotenoids, lycopene, etc.), size, volume, shape, and peel/flesh ratio that are taken into consideration. The level of maturity for these products is determined by the limits based on the internal and external properties of that specific product. Conventional maturity evaluation methods generally employ destructive analysis; however, an increasing number of studies in the last decade have shown that nondestructive methods have been successfully applied to determine the maturity of produce. Nondestructive methods allow analyzing the raw data extracted from the original image and reconstructing a 3D model of dissected sample for visualization of internal structure. Surface color or the structure of samples is also analyzed with several imaging and image processing techniques in order to determine the maturity levels. Whether the internal or external structure is scrutinized, the compliance of extracted data with destructive maturity or ripening parameters must be clearly verified. Statistical models like artificial neural network, principal component analysis, or machine learning approaches are applied because of reducing the amount of extracted data from imaging analysis and its complexity. In this chapter, the imaging techniques used for determining the maturity or ripening levels of fruits and vegetables are discussed.