| Title |
Machine learning-based measurement forecasting approach for smart agriculture |
| Authors |
Kempelis, Arturs ; Romanovs, Andrejs ; Patlins, Antons ; Brūzgienė, Rasa |
| DOI |
10.37394/232015.2025.21.78 |
| Full Text |
|
| Is Part of |
WSEAS Transactions on environment and development.. Houston, TX : WSEAS. 2025, vol. 21, art. no. 78, p. 937-949.. ISSN 1790-5079. eISSN 2224-3496 |
| Keywords [eng] |
Convolutional Network Regression ; Deep learning ; Measurement Forecasting ; Plant Sensor Data Estimation ; Precision Agriculture ; Thermal imagery ; Vision Transformers |
| Abstract [eng] |
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. |
| Published |
Houston, TX : WSEAS |
| Type |
Journal article |
| Language |
English |
| Publication date |
2025 |
| CC license |
|
