Development of an optimally designed real-time automatic citrus fruit grading–sorting​ machine leveraging computer vision-based adaptive deep learning model. (April 2023)

• Record Type: Journal Article
• Authors: Chakraborty, Subir Kumar; A., Subeesh; Dubey, Kumkum; Jat, Dilip; Chandel, Narendra Singh; Potdar, Rahul; Rao, N.R.N.V. Gowripathi; Kumar, Deepak
• Is Part Of: Engineering applications of artificial intelligence. Volume 120(2023)
• Publication Date: 2023-04
• Subjects: Single board computer -- Embedded system -- Ergonomics -- Kinematic synthesis -- Vision-based sorting -- Weight grading; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2023.105826
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Conventional automation approaches for postharvest operations are plagued by time and data inefficiency seldom leading to suboptimal solutions. Automatic machines often require highly skilled software professionals for calibration and reconfiguration thus making the technology prone to high costs. Contemporary sensors and smart devices capable of handling deep learning image analytics have been employed in the present study for the development of an automatic machine that performs postharvest operations, like—washing, vision-based sorting and weight-based grading of citrus fruits with much reduced human effort while achieving excellent performance for the designated tasks. Accuracy of performance was ensured by the optimal design of mechanical components carried out by kinematic synthesis and dimensional analysis. The machine was equipped with an effective custom lightweight CNN model "SortNet" that was designed and tuned to carry out vision-based classification of citrus fruits into "accept" and "reject" based on surface characteristics. SortNet was less complex and took less computational time while exhibiting comparable accuracy with respect to existing state-of-the-art pre-trained deep learning models. An embedded system operated by a single-board computer was used in the weight grading section for segregating fruits based on three weight categories. Evaluation, realization and transferability of the above said strategy was demonstrated by the real hardware with physical actuators working in real-time to serve as proof-of-concept for a sustainable solution to postharvest automation of citrus fruits. Highlights: Vision-based sorting and weight grading of fruits can be automated. Real-time classification of fruits can be realized by DL driven computer vision model. Feature extraction with single board computer was used for machine development. Machine with AI based work environment improved operator comfort.