AI‐enabled farm‐friendly automatic machine for washing, image‐based sorting, and weight grading of citrus fruits: Design optimization, performance evaluation, and ergonomic assessment

Abstract

The modernization of postharvest operations and penetration of emerging technologies in horticultural processing have provided intelligent solutions for reducing postharvest losses. Work environmental and occupational health issues require immediate attention as the awkward posture and continuous drudgeryprone on-farm sorting and grading activities may lead to musculoskeletal disorders.The main objective of this study was to develop an automatic farm-friendly machine for real-time citrus fruit washing, image-based sorting, and weight grading; designed optimally and equipped with an embedded system comprising a lightweight convolutional neural network (CNN) model. Also included in this study was a thorough ergonomic assessment of the developed machine in a real work environment. The parametric choice of the fruit washing and singulation system was performed by employing computational fluid dynamics modeling and response surface methodology designed optimization. It was observed that under steady-state conditions, the water jet would arrive at a velocity of 11.36 m/s which would eventually suit a singulation conveyor with a slope of 25°. A noninvasive grading and sorting approach for citrus fruits is presented in this paper that leverages deep learning to classify the fruits into "accept" and "reject" classes. The custom lightweight CNN model "SortNet" has shown excellent classification results with an overall accuracy of 97.6%. The ergonomic evaluation shows that the average body part discomfort score in case of operating an automatic fruit grading machine was much lower (12.3 ± 2.0) than the traditional method (30.9 ± 3.3). Further, in the case of machine operation, the percentage load on the muscles ranged from 28.67 to 34.31 reflecting that subjects can work for longer duration on the machine without fatigue as compared with the traditional manual operation.

Tecnologías innovadoras para el manejo de frutales, énfasis en caducifolios, aguacate y frutas tropicales

“…Del mismo modo, se han utilizados algoritmos de visión de máquina e IA para la detección en línea del estado de maduración de las frutas en (Chakraborty et al, 2023), en esta investigación se presenta un sistema de visión artificial que utiliza una cámara para capturar imágenes de la fruta en una cinta transportadora y luego aplica técnicas de procesamiento de imágenes para determinar la madurez de la fruta. El sistema se probó en manzanas y peras, donde se logró determinar con precisión la madurez de la fruta con un alto grado de precisión a través de un clasificador de máquina de vectores de soporte (SVM).…”