SomiaImdad/Plant-Disease-Diagnostics-using-UAV-and-Android-APP

This project is a legacy implementation (2,800 days old) of a standard Convolutional Neural Network (CNN) for image-based plant disease classification. While it addresses a real-world problem in AgTech, it possesses no technical moat or defensibility in the current AI landscape. The code has zero velocity, indicating it is an abandoned academic or personal project rather than an evolving tool. The underlying models (likely based on VGG or ResNet transfer learning) have been significantly surpassed by modern architectures and transformer-based vision models. Furthermore, the problem space is now dominated by high-capital platforms like Plantix (which has millions of users and a proprietary database of millions of labeled images) and general-purpose visual search engines like Google Lens. Most critically, frontier multimodal models (GPT-4o, Gemini) now perform zero-shot or few-shot plant disease identification with high accuracy, making specialized, small-scale classification apps nearly obsolete unless they are part of a larger, integrated hardware/software farm management ecosystem.