RotAlert: IoT Based Food Spoilage Detection

Fruit spoilage leads to significant economic losses and quality degradation in the agricultural supply chain. Manual inspection of sweet lime freshness is time-consuming, inconsistent, and prone to human error. To address this issue, an AI-based automated fruit spoilage detection system is proposed using the YOLOv8 deep learning model and Raspberry Pi for real-time edge deployment. The system captures images of sweet lime using a Raspberry Pi camera and processes them through a trained YOLOv8 object detection model to classify fruits as Good or Rotten. The model is trained on a labeled dataset with bounding box annotations and optimized for lightweight inference to run efficiently on Raspberry Pi. The system provides real-time detection with bounding boxes, class labels, and confidence scores, achieving an mAP of approximately 92% and a frame rate of 8–12 FPS on edge hardware. This low-cost and portable solution reduces manual effort, minimizes human misclassification, and enables automated fruit quality assessment for small vendors, warehouses, and smart agriculture applications. The proposed system demonstrates the feasibility of deploying deep learning–based computer vision models on edge devices for real-time food quality monitoring.