Thailand’s mango export industry faces significant challenges in meeting stringent international quality standards, particularly the costly phytosanitary X-ray irradiation process. Current fixed-dose irradiation methods result in substantial energy waste due to variations in fruit size. This research presents a low-cost, real-time system that integrates computer vision and artificial intelligence (AI) to optimize this process. By capturing a single top-view 2D image, the system accurately estimates the three-dimensional characteristics (width, height, and depth) of ‘Nam Dok Mai Si Thong’ mangoes. This dimensional data is crucial for dynamically adjusting the radiation dose for each fruit, leading to significant reductions in energy consumption and operational costs. Our novel approach utilizes a Linear Regression combined with Co-Kriging (LR + CoK) model to precisely estimate fruit depth from 2D data, a key limitation in previous studies. The system demonstrated high efficacy, achieving a dimensional estimation error (RMSE) of less than 0.46 cm and a size grading accuracy of up to 93.33 percent. This technology not only enhances sorting and grading efficiency but also offers a practical solution to lower the economic and energy burden of phytosanitary treatments, directly improving the sustainability of fruit export operations.

Thailand’s mango export industry faces significant challenges in meeting stringent international quality standards, particularly the costly phytosanitary X-ray irradiation process. Current fixed-dose irradiation methods result in substantial energy waste due to variations in fruit size. This research presents a low-cost, real-time system that integrates computer vision and artificial intelligence (AI) to optimize this process. By capturing a single top-view 2D image, the system accurately estimates the three-dimensional characteristics (width, height, and depth) of 'Nam Dok Mai Si Thong' mangoes. This dimensional data is crucial for dynamically adjusting the radiation dose for each fruit, leading to significant reductions in energy consumption and operational costs. Our novel approach utilizes a Linear Regression combined with Co-Kriging (LR+CoK) model to precisely estimate fruit depth from 2D data, a key limitation in previous studies. The system demonstrated high efficacy, achieving a dimensional estimation error (RMSE) of less than 0.46 cm and a size grading accuracy of up to 93.33 percent. This technology not only enhances sorting and grading efficiency but also offers a practical solution to lower the economic and energy burden of phytosanitary treatments, directly improving the sustainability of fruit export operations.

This study presents a PMV-based heuristic rule control strategy for thermal comfort regulation in an electric airport bus air condi- tioning system. A full-scale experimental cabin was developed to emulate the thermal environment of an electric airport bus under controlled laboratory conditions. Key Environmental parameters, including air temperature, relative humidity, air velocity, and mean radiant temperature, were measured in real time to estimate the dynamic Predicted Mean Vote (PMV) index. The proposed controller used PMV error and the rate of change of PMV to determine discrete operating commands for the compressor, evaporator fan, and condenser fan through CAN Bus communication. Experimental evaluations were conducted to investigate the effects of control sampling rate, PMV control boundary, and feedback variable selection. The results showed that a 4 s/sample control updateinterval provided faster response and smoother PMV regulation than an 8 s/sample counterpart. A narrower PMV control boundary of 0 < PMV < +0.3 with PMVrₑ f = +0.15 significantly enhanced system stability and mitigated overshoot compared with a wider boundary of +0.3 < PMV < +0.5. A comparative performance analysis between temperature-based and PMV-based control further showed that conventional temperature regulation alone fails to guarantee guarantee acceptable thermal comfort. Under slightly different environmental conditions, the PMV-based strategy showed a clear tendency to reduce estimated energy consumption from 4.8035 kWh to 2.833 kWh, corresponding to an energy reduction trend of approximately 41%. These findings demonstrate the potential of PMV-based heuristic control for energy aware thermal comfort management in electric public transit applications.