This is like giving farmers a smart camera doctor for their crops: you point a phone or drone camera at leaves, and AI spots diseases and pests early from the pictures, just like a dermatologist checks skin photos.
Manual crop inspection is slow, labor-intensive, inconsistent, and often detects diseases too late. Image-based computer vision systems can automatically detect and classify plant diseases from leaf or field images, improving yield protection and reducing reliance on scarce expert agronomists.
Rich, labeled image datasets across crops, regions, and conditions; integration into existing agronomy workflows (mobile apps, field hardware, farm management systems); and on-device models that run reliably in low-connectivity, low-power farm environments.
Hybrid
Unknown
High (Custom Models/Infra)
Collecting and labeling high-quality, diverse plant disease images across geographies, growth stages, lighting conditions, and camera types; model robustness to real-world noise; and efficient deployment on edge/mobile devices in the field.
Early Majority
Positioned as a survey of both classical machine learning and modern deep learning approaches, this work synthesizes state-of-the-art techniques and open challenges for image-based plant disease detection, rather than being a single proprietary product. It can guide teams to choose between traditional feature-based classifiers and CNN-based/modern vision architectures, and to understand gaps like dataset bias, field conditions vs. lab conditions, and the need for robust, real-time, edge-deployable models.