I’m building a lightweight AI proof-of-concept that helps farmers monitor crop health and estimate likely yields. My immediate focus is on two functions: • Disease detection – flag visible leaf symptoms from the images I already have. • Yield prediction – provide a first-pass estimate based on those same images. Only images of crops are available right now, so the solution should rely on computer-vision techniques (Python with TensorFlow or PyTorch is fine). I need: 1. A well-commented notebook or script that trains and tests both models on my dataset. 2. Clear instructions for retraining with new images. 3. Basic performance metrics (accuracy / F1 or similar) on a held-out sample. 4. A short README outlining next-step recommendations for adding soil, weather, or sensor data as we expand toward irrigation management and market-linkage features. Keep the code modular and lightweight so it can eventually run on a modest cloud instance or edge device. Deliver everything in a shared repo or zip file within a week, and feel free to suggest any open-source libraries or pre-trained networks that speed things up while staying within a small footprint.