Design and Implementation of Vision Based Autonomous Rover for Solar Panel Inspection

Sustained output from photovoltaic installations depends heavily on the condition of individual panel surfaces, yet routine inspection across large solar farms remains a predominantly manual and resource-intensive task. This paper presents the design, implementation, and field evaluation of an autonomous ground rover developed for systematic photovoltaic panel inspection. The rover traverses solar panel rows by tracking a physical guide line and executes a structured inspection sequence at each panel stop without operator intervention. A triggered OV3660 camera captures a surface image that is classified on-device by a Convolutional Neural Network trained through the Edge Impulse TinyML platform on a dataset spanning three surface conditions: clean, dust-covered, and contaminated by bird droppings. This inference runs entirely on an ESP32-S3 microcontroller, requiring no cloud connectivity. Complementary dual-LDR sensing quantifies surface reflectance loss, and integrated voltage-current monitoring records electrical output at each panel. Results from all three measurement channels are consolidated and transmitted to an inspection analytics dashboard, which presents panel-level health status in a format accessible to non-specialist operators. Across a controlled field evaluation, the system achieved strong classification accuracy, consistent stopping precision, and a throughput suitable for regular inspection scheduling. The system was able to classify them with a high accuracy of 92-95%, and this was done in just 2-3 seconds, indicating that it is efficient enough to be used in real-time checks. The rover was assembled from commercially available components at a modest hardware cost, demonstrating that capable automated inspection is achievable without expensive infrastructure.