Takao Nishi

Features Extraction for Eggplant Fruit Grading System Using Machine Vision

Machine vision based grading for agricultural crops has been well developed and accepted as an attractive grading method. However, machine vision based grading for eggplant fruit is not available yet. This study reports on the attempt to develop an eggplant grading machine using six CCD cameras as the sensing device. Feature extraction algorithms were developed to extract eggplants features, i.e., length, diameter, volume, curvature, color homogeneity, calyx color, calyx area, and surface defect. The system could acquire six images per fruits covering the entire surface of the eggplant fruits. An agreement rate of 78.0% was achieved in the feasibility study where the machine vision based grading was compared with manual grading. The throughput of the developed system was 0.3 second per fruit. Details of the system, an outline of the algorithm, and performance results are reported in this article.

Surface Gloss Measurement on Eggplant Fruit

Surface gloss is one of the important quality factors affecting consumers opinion on purchasing eggplant fruits. The current manual gloss grading method is subject to human bias and the outcome is often very ambiguous. This study developed a sensing device consisting of two CCD monochrome cameras and three long tungsten lights and an associated quantitative method of measuring surface gloss of eggplant fruit using the device. Distinguishing various glossiness states of the fruit were made possible by analyzing the specular reflectance of the fruit surface. Specular reflection of the light on fruit surface was measured by digital image analysis method. Gradient filter was applied on the gray-level image for measuring magnitude of intensity changes. The algorithm consistency and validation test on the samples showed the algorithm was feasible of quantitatively assessing eggplant fruit surface gloss with an accuracy rate of 0.806. Details of the sensing devices, outline of the algorithm and preliminary results are presented in this article.

Application of NIR-color CCD camera to Eggplant Grading Machine

An on-line eggplant grading machine was developed to inspect and grade fresh market eggplant in an agriculture cooperative located at Okayama, Japan. Two machine vision systems, which made up from 6 color CCD cameras and 4 monochrome CCD cameras were used for acquiring digital image of the eggplant. Eggplant fruits are graded when the fruits were conveyed through these cameras on a special designed rotary tray. 180 ° vertical turn of the rotary tray in between these camera boxes enable the inspection of the eggplant’s entire surface. It was found that disorientated and disposition fruit on the rotary tray affect the grading process. Eggplant fruit is dark purple in color with extremely low spectral reflectance in the visible spectrum. Consequently, defect detection on the eggplant fruits and extraction of fruits feature from the low color contrast background are difficult. A new NIR-enhanced-color CCD camera (380nm-1400nm) was studied in overcome the problems mentioned above. Experimental result showed that this new camera was able to extract the fruit’s feature from dark background and detection of low-contrast-defects was found possible too.