CropGuard: An AI-Assisted Crop Health Detection and Smart Farming Rover Prototype
Year: 2026
Type: preprint
Abstract: Early detection of foliar crop diseases is critical to safeguarding agricultural yields and minimizing economic losses, particularly for smallholder farms that lack on-demand access to professional plant pathologists. CropGuard is an autonomous agricultural rover platform combining multi-angle optical sensing, host-based edge computing, and localized mist treatment. In this paper, we document and rigorously evaluate the computer-vision core of the prototype: a lightweight YOLO11n object detector fine-tuned from COCO-pretrained weights across 16 crop-health classes (healthy foliage plus 15 fungal, bacterial, viral, and pest-induced conditions). The experimental dataset comprises 2,573 multi-crop leaf images (8,931 annotated bounding boxes) partitioned into a 2,344-image training split (8,476 instances) and a 229-image validation split (455 instances). On the validation split, the best-performing checkpoint (epoch 21 of 22 completed) achieves a mean precision of 0.5427, mean recall of 0.5778, [email protected] of 0.6082, and [email protected]:0.95 of 0.4624 across the 15 evaluable classes. One class (TWO_SPOTTED_SPIDER_MITES) contains zero validation instances and cannot be evaluated. On an Intel Core i7-14700HX CPU, the standalone YOLO11n forward pass requires 32.72 ms per frame (30.56 FPS), while an end-to-end processing pipeline incorporating CLAHE contrast enhancement and bounding-box drawing requires 107.23 ms (9.33 FPS). Physical benchtop integration of the 1:8 four-wheel-drive rover chassis, quad ESP32-CAM optical array, dual-relay ultrasonic mist actuation, and bilingual operator dashboard demonstrates responsive closed-loop plant treatment.
Institution: Somaiya Vidyavihar University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W7225382975