**Background:** Growing demand for organic food has led to increased food fraud, as consumers cannot visually distinguish organic from conventional produce. Laboratory-based spectroscopic methods (NIR, FT-IR, NMR) are accurate but expensive, time-consuming, and not portable. This study aimed to design a low-cost, portable, nondestructive multispectral sensor system combined with machine learning for real-time discrimination of organic and conventional vegetables.
**Methods:** A Triad AS7265X multispectral sensor (410–940 nm) with 18 channels across UV, visible, and NIR bands was used. The system included an ESP8266 for data acquisition and a Raspberry Pi for processing. Organic and conventional tomato (red), brinjal (purple), and green chili samples were grown in separate 10×10 ft² red soil plots 15 m apart. Organic plots received cow dung and goat dung fertilizers; conventional plots received DAP (18:46) and SSP fertilizers. A total of 70 vegetable samples (25 tomato: 11 organic, 13 conventional; 22 brinjal: 12 organic, 10 conventional; 24 chili: 12 organic, 12 conventional) yielded 10,800 spectral data points (1,800 per vegetable type). A fuzzy logic engine (triangle membership function, centroid defuzzification) generated 18 new features from raw sensor data to handle variability from sensor distance, angle, ambient light, and sample size. Ant colony optimization (ACO) with two-stage pheromone updating selected 12 optimal features from the 18 fuzzy features. Classification was performed using random forest (ensemble of decision trees) and neural network (12 input neurons, 10 hidden layers, sigmoid activation, cross-entropy loss) models. Data were split 70% training, 15% validation, 15% testing.
**Key Results:** Raw (non-optimized) classification accuracy was 92% for random forest and 89% for neural network. After ACO-based feature selection and parameter tuning, both models achieved 100% accuracy. The ACO algorithm converged at the 40th iteration for RF and the 50th iteration for NN. Histogram analysis identified key discriminating channels: for tomato, Channels 13 (NIR, 730 nm) and 6 (IR, 535 nm); for brinjal, Channels 13 (NIR, 730 nm) and 7 (Vis, 560 nm); for green chili, Channels 4 (UV, 485 nm) and 2 (UV, 435 nm). The ROC curve showed 100% performance for the two-class classification. The system response time was a maximum of 400 ms. Repeatability testing over three months with samples from different markets and soil locations produced consistent results. The total system cost was approximately Rs. 9,700 (sensor Rs. 6,250, Raspberry Pi Rs. 1,600, ESP8266 Rs. 1,850). Results were displayed on a password-protected IoT web page.
**Clinical Implications:** While not a clinical study, this work has significant public health implications. It provides a rapid, portable, and affordable tool for authenticating organic produce at the point of sale or in the field, potentially reducing food fraud and protecting consumers from mislabeled or chemically treated products. The nondestructive nature allows repeated testing without sample destruction. The IoT integration enables remote monitoring and transparency in the food supply chain. Future work should extend testing to other vegetables, different soil conditions, pesticide-sprayed samples, and on-farm field conditions.