Citation :

Chamorro-Quijije Adrián, Chávez-Jácome Félix, De la Torre-Guzmán Javier, Salazar-Jácome Elizabeth, "Design of an Automated System with Raspberry Pi, Machine Vision and Graphical Interface Control for Intelligent Sorting of Fruits and Vegetables in Real Time," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 1, pp. 236-247, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I1P118

Abstract

This research presents the design and implementation of an automated system for the intelligent classification of fruits and vegetables native to Ecuador, based on low-cost, low-energy consumption, and easy replicability technologies. The solution is composed of a Raspberry Pi 4 as the central processing unit, an official 5-megapixel camera for image capture, presence sensors, high-torque servomotors, and a DC geared motor that drives the conveyor belt. Through a combination of machine vision (OpenCV), machine learning (TensorFlow Lite), and physical control (GPIO Zero), the system allows agricultural products to be identified and sorted in real time, automatically diverting them to different trays according to their type. The artificial intelligence model was trained with images of native fruits and vegetables, considering aspects of shape, color, and texture. A graphical interface developed in Python allows the control and monitoring of the system in an intuitive way, making it accessible to operators without technical knowledge. Energy-efficient elements such as switching power supplies, voltage regulators, and transistors were incorporated for load control. The system was evaluated under real operating conditions, achieving an accuracy of over 92% and a processing rate of up to 180 fruits per hour. This project not only represents an advance in agroindustry automation but also responds to the criteria of technological sustainability, reduction of post-harvest waste, and strengthening of the circular economy. Its modular, educational, and open-source approach positions it as an innovative and sustainable tool for rural contexts and smallholder agricultural producers.

Keywords

GPIO Zero, Machine Vision, Raspberry Pi, Sustainability, TensorFlow.

References

[1] Emilio Guerreroe et al., “Precision Agriculture: Enhancing Crops and Weeds Classification of Unbanlance Databases via Weighted-Loss Functions,” 2024 IEEE Eighth Ecuador Technical Chapters Meeting (ETCM), Cuenca, Ecuador, pp. 1-6, 2024.
[CrossRef] [Google Scholar] [Publisher Link] 

[2] Abdelhakim Amazirh et al., “Assimilation of Smap based Disaggregated Soil Moisture for Improving Soil Evaporation Estimates by FAO-2Kc Model,” 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, pp. 6568-6571, 2021.
[CrossRef] [Google Scholar] [Publisher Link] 

[3] Shubham Awachat, and Deepak Sharma, “A Review on Impact of Agricultural Mechanization on Farm Productivity and Factors Facilitating the Growth of Mechanization,” 2024 5^th International Conference on Data Intelligence and Cognitive Informatics (ICDICI), Tirunelveli, India, pp. 1163-1166, 2024.
[CrossRef] [Google Scholar] [Publisher Link] 

[4] Bhavika Thakkar et al., “Leveraging Artificial Intelligence, Internet of Things, and Digital Twins in Sustainable Agriculture,” 2024 International Conference on Signal Processing and Advance Research in Computing (SPARC), LUCKNOW, India, pp. 1-6, 2024.
[CrossRef] [Google Scholar] [Publisher Link] 

[5] Rupanshi Agarwal et al., “Innovations in Agri-Tech: A Review of Artificial Intelligence Applications and Challenges in Modern Agriculture,” 2024 Second International Conference on Advanced Computing and Communication Technologies (ICACCTech), Sonipat, India, pp. 599-604, 2024.
[CrossRef] [Google Scholar] [Publisher Link] 

[6] Oscar Jhon Vera Ramirez, José Emmanuel Cruz de la Cruz, and Wilson Antony Mamani Machaca, “Agroindustrial Plant for the Classification of Hass Avocados in Real-Time with ResNet-18 Architecture,” 2021 5^th International Conference on Robotics and Automation Sciences (ICRAS), Wuhan, China, pp. 206-210, 2021.
[CrossRef] [Google Scholar] [Publisher Link] 

[7] Lixin Hou et al., “Tomato Sorting System based on Machine Vision,” Electronics, vol. 13, no. 11, pp. 1-19, 2024.
[CrossRef] [Google Scholar] [Publisher Link] 

[8] Jason Brian L. Rivero et al., “Moisture Content Determination of Corn using Red-Green-Blue to Hue-Saturation-Value Color Space with Artificial Neural Network,” 2022 IEEE 14^th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), Boracay Island, Philippines, pp. 1-5, 2022.
[CrossRef] [Google Scholar] [Publisher Link] 

[9] Ryoji Sato et al., “Evaluation of the Effect of Light Source Spectrum on the Estimation Accuracy of High Dimensional Spectral Images using Neural Networks based on RGB Images,” 2021 60^th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), Tokyo, Japan, pp. 1460-1465, 2021.
[Google Scholar] [Publisher Link] 

[10] Rupa Lalam et al., “Automatic Sorting and Grading of Fruits based on Maturity and Size using Machine Vision and Artificial Intelligence,” Journal of Scientific Research and Reports, vol. 31, no. 1, pp. 153-163, 2025.
[CrossRef] [Google Scholar] [Publisher Link] 

[11] M.W. Amna et al., “Machine Vision-based Automatic Fruit Quality Detection and Grading,” Frontiers of Agricultural Science and Engineering, vol. 12, no. 2, pp. 274-287, 2025.
[CrossRef] [Google Scholar] [Publisher Link] 

[12] K. Bavithra et al., “Precision Harvesting: Utilizing Inception Neural Networks in Robotic Arms for Fruit Sorting,” 2025 International Conference on Computational Innovations and Engineering Sustainability (ICCIES), Coimbatore, Tamilnadu, India, pp. 1-6, 2025.
[CrossRef] [Google Scholar] [Publisher Link] 

[13] Vijay Sai Kumar Sudabathula et al., “Sentiment Analysis of Machine Learning Algorithms: A Transformer-based Approach,” 2024 4^th International Conference on Advancement in Electronics and Communication Engineering (AECE), GHAZIABAD, India, pp. 909-914, 2024.
[CrossRef] [Google Scholar] [Publisher Link] 

[14] Nadimpalli Madana Kailash Varma et al., “Machine Learning-Enabled Smart Transit: Real-Time Bus Tracking System for Enhanced Urban Mobility,” 2024 Intelligent Systems and Machine Learning Conference (ISML), Hyderabad, India, pp. 520-524, 2024.
[CrossRef] [Google Scholar] [Publisher Link] 

[15] Harshali Hemant Vadher et al., “Unveiling the Potential of Machine Learning: Harnessing Machine Learning for Enhanced Coronary Heart Disease Detection and Intervention,” 2024 4^th International Conference on Advancement in Electronics and Communication Engineering (AECE), Ghaziabad, India, pp. 1073-1078, 2024.
[CrossRef] [Google Scholar] [Publisher Link] 

[16] Luo Peng, “Simulation Research and Development of Agricultural Products Processing Process based on Machine Learning Algorithm,” 2022 International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS), Bristol, United Kingdom, pp. 408-412, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[17] Mikhail Chernyakov et al., “Modeling of the Processes of using Agricultural Machinery Products for Automation of Personal Subsidiary Farms,” 2024 9^th International Conference on Energy Efficiency and Agricultural Engineering (EE&AE), Ruse, Bulgaria, pp. 1-5, 2024.
[CrossRef] [Google Scholar] [Publisher Link] 

[18] Vinayambika S. Bhat, and Yong Wang, “Revisiting the Control Systems of Autonomous Vehicles in the Agricultural Sector: A Systematic Literature Review,” IEEE Access, vol. 13, pp. 54686-54721, 2025.
[CrossRef] [Google Scholar] [Publisher Link] 

[19] Navin Kumar et al., “Impact of IoT based Autonomous Farming Equipment on Crop Culture and Management in the Agricultural Sector,” 2022 International Conference on Edge Computing and Applications (ICECAA), Tamilnadu, India, pp. 669-675, 2022.
[CrossRef] [Google Scholar] [Publisher Link] 

[20] Satyanarayana Nimmala et al., “A Recent Survey on AI Enabled Practices for Smart Agriculture,” 2024 International Conference on Intelligent Systems for Cybersecurity (ISCS), Gurugram, India, pp. 1-5, 2024.
[CrossRef] [Google Scholar] [Publisher Link] 

[21] S. Ashok Kumar et al., “Intelligence Pest Detection and Control in Agriculture using Computer Vision and Deep Learning,” 2025 International Conference on Intelligent Computing and Control Systems (ICICCS), Erode, India, pp. 1147-1150, 2025.
[CrossRef] [Google Scholar] [Publisher Link] 

[22] Junyi Yang, “Real Time Object Tracking using OpenCV,” 2023 IEEE 3^rd International Conference on Data Science and Computer Application (ICDSCA), Dalian, China, pp. 1472-1475, 2023.
[CrossRef] [Google Scholar] [Publisher Link] 

[23] Sai Sri Nandan Challapalli et al., “Comparing TensorFlow.js and TensorFlow in Python: An Accessibility and Usage Analysis,” 2023 6^th International Conference on Contemporary Computing and Informatics (IC3I), Gautam Buddha Nagar, India, pp. 250-254, 2023.
[CrossRef] [Google Scholar] [Publisher Link] 

[24] Eric Thian Aun Tan, Sabyasachi Mohapatra, and Kean Hong Boey, “Design and Implementation of GPIO Subsystem using External Foundry I/O Buffer,” 2021 IEEE Asia Pacific Conference on Circuit and Systems (APCCAS), Penang, Malaysia, pp. 109-112, 2021.
[CrossRef] [Google Scholar] [Publisher Link] 

[25] S. Vasavi, P.D.L. Nikhita Sri, and P.V. Sai Krishna, “GUI-Enabled Boundary Regularization System for Urban Buildings using the Tkinter,” 2024 2^nd International Conference on Device Intelligence, Computing and Communication Technologies (DICCT), Dehradun, India, pp. 424-429, 2024.
[CrossRef] [Google Scholar] [Publisher Link]