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Open AccessArticle

Smartphone Application-Enabled Apple Fruit Surface Temperature Monitoring Tool for In-Field and Real-Time Sunburn Susceptibility Prediction

by Bin Wang 1,2,†, Rakesh Ranjan 2,†, Lav R. Khot 2,* and R. Troy Peters 2
1
College of Information Science and Technology, Hebei Agricultural University, Baoding 071001, China
2
Department of Biological Systems Engineering, Center for Precision and Automated Agricultural Systems, Washington State University, Prosser, WA 99350, USA
*
Author to whom correspondence should be addressed.
Equal contribution author.
Sensors 2020, 20(3), 608; https://doi.org/10.3390/s20030608
Received: 2 November 2019 / Revised: 16 December 2019 / Accepted: 21 December 2019 / Published: 22 January 2020
(This article belongs to the Special Issue Advanced Sensor Technologies for Crop Phenotyping Application)
Heat stress and resulting sunburn is a major abiotic stress in perineal specialty crops. For example, such stress to the maturing fruits on apple tree canopies can cause several physiological disorders that result in considerable crop losses and reduced marketability of the produce. Thus, there is a critical technological need to effectively monitor the abiotic stress under field conditions for timely actuation of remedial measures. Fruit surface temperature (FST) is one of the stress indicators that can reliably be used to predict apple fruit sunburn susceptibility. This study was therefore focused on development and in-field testing of a mobile FST monitoring tool that can be used for real-time crop stress monitoring. The tool integrates a smartphone connected thermal-Red-Green-Blue (RGB) imaging sensor and a custom developed application (‘AppSense 1.0’) for apple fruit sunburn prediction. This tool is configured to acquire and analyze imagery data onboard the smartphone to estimate FST. The tool also utilizes geolocation-specific weather data to estimate weather-based FST using an energy balance modeling approach. The ‘AppSense 1.0’ application, developed to work in the Android operating system, allows visual display, annotation and real-time sharing of the imagery, weather data and pertinent FST estimates. The developed tool was evaluated in orchard conditions during the 2019 crop production season on the Gala, Fuji, Red delicious and Honeycrisp apple cultivars. Overall, results showed no significant difference (t110 = 0.51, p = 0.6) between the mobile FST monitoring tool outputs, and ground truth FST data collected using a thermal probe which had accuracy of ±0.4 °C. Upon further refinements, such tool could aid growers in real-time apple fruit sunburn susceptibility prediction and assist in more effective actuation of apple fruit sunburn preventative measures. This tool also has the potential to be customized for in-field monitoring of the heat stressors in some of the sun-exposed perennial and annual specialty crops at produce maturation. View Full-Text
Keywords: crop loss management; apple sunburn prediction; fruit surface temperature; thermal-RGB image; smartphone application; Android OS; ‘AppSense 1.0’ crop loss management; apple sunburn prediction; fruit surface temperature; thermal-RGB image; smartphone application; Android OS; ‘AppSense 1.0’
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Wang, B.; Ranjan, R.; Khot, L.R.; Peters, R.T. Smartphone Application-Enabled Apple Fruit Surface Temperature Monitoring Tool for In-Field and Real-Time Sunburn Susceptibility Prediction. Sensors 2020, 20, 608.

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