Development of an IoT-Based Chili Ripeness Detection System Using Raspberry PI

As a result of the rapid development of the Internet of Things (IoT) and computer vision technologies, agricultural systems nowadays may be more developed in the aspects of automation and intelligent detection. Proper ripeness grade selection is critical to attain the optimal quality, market value, and agricultural output in chilli plantations. Farmers typically inspect the maturity stages visually, but it suffers from inconsistent, time-consuming, and weather-dependent result. Hence, this project will develop an intelligent system for chilli ripeness detection by integrating Web Camera module and Raspberry Pi controller to minimize the human intervention to monitor the ripeness stage. The procedure involves capturing real-time images of the chili fruits and classifying them into green stage, changing stage, and ripe stage by colour and visual characteristics. Real-time image data will be analyzed by Raspberry Pi and wirelessly transmitted to cloud storage and made available for remote monitoring through WiFi connectivity and signal processing. Additional sensors including TCS34725 colour sensor, DHT22 temperature and humidity sensor, and BH1750 light sensor are added into the system to optimize the detection accuracy and reliability especially in an agricultural field environment. This project would contribute to the agricultural efficiency and promote smart farm implementation in Malaysia by providing a low-cost, accurate ripeness detection tool to farmers.

Internet of Things (IoT), Computer Vision, Artificial Intelligence (AI), Chilli Ripeness Detection, Raspberry Pi, Smart Agriculture, Image Processing.

1. N. Y. D. Rahmawati and F. P. Rochim, "Chili Ripeness Level Detection with YOLOv8 and Fuzzy Logic for Harvest Decision Making," 2025. [Google Scholar] [Crossref]

2. N. S. B. Khalid, M. A. Zauhaili, I. I. Ibrahim, M. Z. Hasan, H. Mansor, N. D. N. Dalila, and S. A. Shukor, "Real-Time Classification of Chilli Ripeness using Convolutional Neural Network (CNN)," 2025. [Google Scholar] [Crossref]

3. S. M. Kulkarni, S. Umadi, S. S. Airani, and M. M. Raikar, "Grading and Classification of Tomatoes Using ESP32-CAM and Impulse Cloud-based IoT Platform for Sustainability," 2025. [Google Scholar] [Crossref]

4. M. Nahiduzzaman, R. Sarmun, A. Khandakar, M. A. A. Faisal, M. S. Islam, M. K. Alam, and M. E. Chowdhury, "Deep learning-based real-time detection and classification of tomato ripeness stages using YOLOv8 on Raspberry Pi," 2025. [Google Scholar] [Crossref]

5. J.M.Sultan, N.N.Kamaruzaman,R.Chaudhary, A.M.Yusop, (2024). Precision Indoor Positioning with Ultra-Wideband (UWB) Technology, Przeglad Elektrotechniczny, vol.5. [Google Scholar] [Crossref]

6. N. S. Salahuddin et al., "Development of a Robotic System for Agricultural Pest Detection: A Case Study on Chili Plants," 2025. [Google Scholar] [Crossref]

7. R. I. C. H. A. R. D. Salim and A. N. Fajar, "Object Detection of Chili Using Convolutional Neural Network YOLOv7," 2024. [Google Scholar] [Crossref]

8. M. Sajjan, L. Kulkarni, B. S. Anami, N. B. Gaddagimath, and L. S. Rodríguez Baca, "Chilli Dryness and Ripening Stages Assessment Using Machine Vision," 2023. [Google Scholar] [Crossref]

9. M. F. Ibrahim et al., "Detection and Classifying of Chili Fruits Variations using YOLOv5," 2023. [Google Scholar] [Crossref]

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