| Summary: | Heat and light stress causes sunburn to the maturing apple fruits and results in crop production and quality losses. Typically, when the fruit surface temperature (FST) rises above critical limits for a prolonged duration, the fruit may suffer several physiological disorders including sunburn. To manage apple sunburn, monitoring FST is critical and our group at Washington State University is developing a noncontact smart sensing system that integrates thermal infrared and visible imaging sensors for real time FST monitoring. Pertinent system needs to perform in-field imagery data analysis onboard a single board computer with processing unit that has limited computational resources. Therefore, key objective of this study was to develop a novel image processing algorithm optimized to use available resources of a single board computer. Algorithm logic flow includes color space transformation, k-means++ classification and morphological operators prior to fruit segmentation and FST estimation. The developed algorithm demonstrated the segmentation accuracy of 57.78% (missing error = 12.09% and segmentation error = 0.13%). This aided successful apple FST estimation that was 10–18 °C warmer than ambient air temperature. Moreover, algorithm reduced the imagery data processing time cost of the smart sensing system from 87 s to 44 s using image compression approach.
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