The increasing demand for high-quality agricultural products, driven by raised consumer awareness and tightened food safety regulations, necessitates robust and scalable quality control solutions. Manual fruit classification remains subjective, labor-intensive, and inefficient, leading to inconsistent outcomes and reduced competitiveness. Therefore, we developed an integrated lychee quality assessment system that combines deep learning with IoT technologies. A convolutional neural network was employed for the visual classification of fruit quality categories, whereas IoT sensors monitored environmental parameters including temperature, humidity, and ethylene concentration. The system showed an overall accuracy of 37%, with frequent misclassification between ripe and damaged fruits owing to overlapping RGB features. Despite this limitation, the system presented significant advantages over manual methods, achieving a throughput of 180 fruits per minute compared with 25–30 manually, and eliminating human subjectivity. The IoT component exhibited high reliability, with 99.2% uptime and stable sensor performance. The system can be further improved by expanding the dataset to at least 10000 samples per class, incorporating synthetic augmentation with generative adversarial networks, adopting advanced loss functions such as focal loss, and integrating multimodal fusion with environmental sensor data. Such improvements are essential to achieve commercially viable accuracy (>85%) and enable scalable, transparent supply chain monitoring for perishable fruits.