AgroVision: Bridging Laboratory and Field Data for Enhanced Plant Disease Recognition

Krutika S. Zantye, Siddhi S. Khamkar and Ravindra V. Kerkar

International Journal of Scientific Research in Science and Technology, 2026, vol. 13, issue 3, 745-752

Abstract: Worldwide, crop health still suffers despite constant watch – diseases linger, cutting harvests and weakening quality across regions. Early detection shifts outcomes once outbreaks begin; yet current approaches lean heavily on trained eyes examining symptoms up close – a resource often missing at critical moments. Enter AgroVision: an imaging tool powered by deep learning that scans leaf photos to catch signs of illness quickly. Instead of relying on rigid rules, it leans on Convolutional Neural Networks, uncovering subtle clues linked to specific ailments while learning on its own. What sets it apart? It learns patterns naturally, spotting threats without step-by-step instructions. Every now and then, working outside or under lab lights shows how tricky shifting conditions can be - this slip between environments is the core of what folks call the domain gap. Designed tight and with intent, the model moves fast yet expands smoothly if demands grow. Learning from earlier jobs helps it start faster, still hitting close even on fresh, unfamiliar inputs. A browser tab opens, farm photos go in, answers show up instantly, no lagging behind. Tests back its steady precision, all while staying light on computing load. Most older devices handle it without slowing down. Where connections drop often, that matters more than speed. Smart programming tackles messy farm decisions anywhere. Clarity comes when software respects tough conditions on the ground.

Keywords: Plant Disease Detection; Deep Learning; Convolutional Neural Networks (CNN); Transfer Learning; Precision Agriculture; Computer Vision; Domain Adaptation; MobileNetV2; Smart Farming; Real-Time Diagnosis (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v13:y2026:i3:id:1663

DOI: 10.32628/IJSRST26133200

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