Bridging the Gap Between Low-Cost Cameras and High-Fidelity Monitoring: Deployment of Super-Resolution Models for Real-World Lettuce Farming

Currently, the application of visual monitoring in smart agriculture is one of the options in the implementation of precision agriculture. Smart agriculture visual monitoring has challenges in terms of the relatively high cost of high-resolution cameras and limited access to resources. One option that can be used is implementing embedded low-resolution cameras so that the cost is also low and improves the quality of image resolution by implementing a deep learning-based Super-Resolution (SR) method. This study applies image enhancement to low-resolution cameras directly using three deep learning-based SR models—EDSR, Real-ESRGAN, and ESPCN—for a 2× resolution increase from 800 × 600 (SVGA) to 1600 × 1200 (UXGA) to find out the SR model that is suitable for precision agriculture according to the conditions. Experiments were conducted using a real-world lettuce growth dataset taken by ESP32-CAM as input to the low-resolution camera and implemented on NVIDIA Jetson Orin Nano as edge computing. Performance was assessed in terms of reconstruction quality, computational load, processing latency, and power consumption under CPU and GPU execution. The results show that Real-ESRGAN achieves the highest visual quality at the expense of computational and energy requirements, EDSR offers a good balance, and ESPCN provides the highest efficiency with reduced image detail. These findings highlight the potential for low-cost visual growth monitoring of lettuce plants under limited resource constraints, leading to precision agriculture applications.