Spectral Image Analysis for Measuring Ripeness of Tomatoes

Abstract

In this study, spectral images of five ripeness stages of tomatoes have been recorded and analyzed. The electromagnetic spectrum between 396 and 736 nm was recorded in 257 bands (every 1.3 nm). Results show that spectral images offer more discriminating power than standard RGB images for measuring ripeness stages of tomatoes. The classification error of individual pixels was reduced from 51% to 19%. Using a gray reference, the reflectance can be made invariant to the light source and even object geometry, which makes it possible to have comparable classification results over a large range of illumination conditions. Experimental results show that, although the error rate increases from 19% to 35% when using different light sources, it is still considerably below the 51% for RGB under a single light source.

Image processing techniques for lemons and tomatoes classification

“…Figure 4 shows that during the ripening of tomato occurred an increase of the red color and a decrease of the green color, indicating chlorophyll degradation meanwhile lycopen started to be produced. VAN DER HEIJDEN et al (2000), POLDER et al (2000) and POLDER et al (2002) also compared images with standard RGB images for classifying tomatoes in different ripeness classes using individual pixels and obteined similars results.…”

A Disposable Sensor For Assessing Artocarpus heterophyllus L. (Jackfruit) Maturity

“…On the other hand, CDA is a classical statistical approach for classifying samples of unknown classes, based on training samples with known classes. In contrast to PCA, CDA needs to be trained with prior information to form a classification model [8].…”

Hyperspectral and Chlorophyll Fluorescence Imaging for Early Detection of Plant Diseases, with Special Reference to Fusarium spec. Infections on Wheat

“…Unquestionably, hyperspectral imaging includes a much higher information density than multispectral or RGB images. The involvement of several hundreds of wavelengths in image analysis may provide the prerequisite for high accuracy of detection, and, thus, makes a meaningful monitoring of maturation, quality or diseases possible at all [58,[89][90][91][92]. Nevertheless, with currently applied standard techniques such as conventional line scanners, recording of hyperspectral images is rather slow [45,58,74].…”