Machine Learning-Based Measurement Forecasting Approach for Smart Agriculture

This work explores whether a low-resolution thermal camera can estimate three discrete sensor measurements on a resource-constrained IoT node. Correlation analysis showed that individual thermal pixels correlate strongly with air temperature, negatively with relative humidity and positively with light intensity. Three lightweight regressors VGG CNN, ViT-Tiny and CvT-Tiny were trained from 1 053 single channel 120 x 160-pixel thermal frames to estimate sensor measurements. Experimental tests confirmed the CNN superiority as it achieved RMSE of 2.29 °C and R² of 0.978 (estimating air temperature), RMSE of 0.075 %RH and R² 0.897 (estimating relative air humidity) and RMSE of 0.059 lux and R² of 0.924 (estimating light intensity), outperforming ViT-Tiny and CvT-Tiny on humidity and light intensity estimation. The findings demonstrate that convolutional models remain critical for lightweight and accurate environmental measurement estimation in edge deployments.