I need a production-ready solution that ingests raw Infrared Spectroscopy readings from a handheld or benchtop spectrophotometer, extracts the relevant spectral fingerprints, and predicts both the presence and concentration of common pesticide residues on fresh fruit and vegetable samples. My current lab setup streams CSV files over USB; if you have dealt with other device protocols, feel free to propose an efficient data-capture approach. Core requirements • A clean Python pipeline that parses the spectra, performs any necessary preprocessing (baseline correction, smoothing, normalization), and feeds the data into a TensorFlow model. • A well-documented training notebook + scripts so the model can be re-trained when new pesticides or produce types are added. • (Optional but welcome) a complementary computer-vision module. If you have experience with object detection, segmentation, or classic feature extraction, show me how you would fuse image cues with the spectral output to boost accuracy. • An API or simple GUI that lets a lab technician load a spectrum (and, if available, an image), press “Analyze,” and receive a residue concentration report with confidence scores. • Unit tests and a short validation study on my sample dataset to confirm prediction error stays within regulatory limits we will define together. Deliverables 1. Source code with clear, modular structure (Python 3.x, TensorFlow). 2. Trained model weights and instructions for future retraining. 3. Setup guide covering dependencies, spectrophotometer integration, and optional camera configuration. 4. Brief performance report summarizing accuracy, precision, recall, and inference time on benchmark samples. I’m happy to iterate quickly, so outline your plan, timeline, and any similar spectroscopy or ag-tech projects you’ve handled.