Grading and Classification of Tomatoes Using ESP32-CAM and Impulse Cloud based IoT Platform for Sustainability

Accurate classification of tomatoes is crucial for efficient sorting, packaging, and overall agricultural management. Classifying tomatoes using an ESP32-CAM in an Internet of Things (IoT) platform can enhance sustainability by optimizing resources. By employing image recognition algorithms on the ESP32-CAM, the tomatoes can be classified based on various factors like size, ripeness, and quality. This data can be transmitted to the IoT platform, enabling real-time monitoring and management of tomato production. With this information, farmers can make informed decisions, reduce waste, and increase efficiency, contributing to sustainable agriculture practices. In the proposed system, the tomato classification system that integrates with the Edge Impulse platform is presented. In this paper, a case study on utilizing IoT for tomato classification using the ESP32-CAM microcontroller and the ResNet deep learning model is presented. We discuss the setup, implementation, challenges, and results of the project, highlighting the potential of IoT in improving agricultural processes. The aim is to create a sustainable solution that maximizes agricultural productivity while minimizing waste. The proposed system employs machine learning algorithms for real-time image processing and classification, offering a cost-effective and scalable approach to smart farming. Leveraging the Edge-Impulse cloud-based platform, ESP32-CAM, and servo motors, the system demonstrates an efficient workflow for automated sorting based on tomato ripeness and color. This approach features its potential for enhancing sustainability in agriculture.