Maturity stages detection prototype device for classifying custard apple (Annona squamosa L) fruit using image processing approach

Abstract

Custard apple (Annona squamosa L.) is commercially grown for its sweetness, delicacy, and nutraceutical benefits across the world. Fruit must be harvested from trees before the climacteric stage begins for stabilizing the maturation process and perishability, which otherwise restricts longer shelf–life. Therefore, maturity–based grading or classifying custard apple fruit in accordance with their ripening is required for commercial marketing and industrial storage purposes. This is a crucial task that agriculturists and the food processing industries must have to perform manually, which is ineffective and susceptible to human errors. In this research, we have proposed an image processing–based maturity stages detection prototype device for fruit classification of custard apple (Cv. Balanagar) based on changes in their physical, chemical and imaging features (skin color of fruit areoles). The newly developed handheld device comprises of a Raspberry Pi board, camera module, LCD touch screen, and image processing algorithms to identify five essential maturity stages i.e. 0 %, 25 %, 50 %, 75 % and 100 % areoles opening of custard apple. The images were pre–processed and segmented, after which imaging aspects of custard apple fruit including relative R, G and B components were extracted and their relationship with chemical properties such as TSS was established. For classification of captured images, K–means clustering and SVM algorithms were performed on Raspberry Pi Platform with model training and classifier codes in Python (3.9.6) to get desired results. The statistical analysis revealed a substantial difference between the maturity stages in terms of R, G and TSS. Overall, 100 % accuracy was accomplished with respect to the grading of custard apple fruit at varied maturity. This could be a low–cost device useful for farm–level custard apple fruit classification, minimising economic losses caused by faulty supply entering the marketing chain. With slight modifications in algorithms, this device could also be used to assess the maturity of other fruit crops. It could be also an alternative to several expensive approaches being used for automating the maturity grading process that is not feasible, particularly for farmers working in field conditions.

Keywords

• Custard apple
• Image processing
• Maturity detection
• Machine learning algorithms
• Python
• Raspberry Pi