Smartphone ApplicationEnabled Apple Fruit Surface Temperature Monitoring Tool for InField and RealTime Sunburn Susceptibility Prediction 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 infield testing of a mobile FST monitoring tool that can be used for realtime crop stress monitoring. The tool integrates a smartphone connected thermalRedGreenBlue (RGB) imaging sensor and a custom developed application (xe2x80x98AppSense 1.0xe2x80x99) 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 geolocationspecific weather data to estimate weatherbased FST using an energy balance modeling approach. The xe2x80x98AppSense 1.0xe2x80x99 application, developed to work in the Android operating system, allows visual display, annotation and realtime 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 xc2xb10.4 xc2xb0C. Upon further refinements, such tool could aid growers in realtime 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 infield monitoring of the heat stressors in some of the sunexposed perennial and annual specialty crops at produce maturation.

Forecasting the potential of apple fruitlet drop by insitu VisNIR spectroscopy Abstract   Apple trees (Malus domestica Borkh.) tend to exhibit a biennial cycle: a heavyflowering year with an excessive amount of lowquality fruits is followed by a year with scarce flowering and low fruit load. Chemical thinning is currently the only viable solution in large commercial operations to ensure adequate yield. However, most thinners are effective only in the first few weeks following bloom and thinning efficiency depends on numerous factors and is difficult to predict. Forecasting the expected fruitlet drop after an initial thinner application would help perform corrections with the subsequent application. In this study, we used insitu spectroscopy in the visible and nearinfrared (VisNIR) range to forecast fruitlets drop rate. The study was carried out on xe2x80x9cGolden Deliciousxe2x80x9d apple trees during two growing seasons xe2x80x93 in April 2017 and April 2018. As commonly done in commercial orchards, the fruitlet drop was amplified by the application of synthetic auxins 1naphthaleneacetic acid (NAA) and its amide (NAD). Fruitlets were tagged and monitored in situ every 2xe2x80x934xc2xa0days by measuring reflectance over the 400xe2x80x931000xc2xa0nm range. Special care was taken to assess sunlight interference during the measurement and correct it using a custom postprocessing procedure. Measurements at 4xe2x80x9312xc2xa0days after NAANAD treatment (days after treatment  DAT) were used to forecast fruitlet drop by 20xe2x80x9326 DAT (prediction dates). Principal component analysis (PCA(was carried out, followed by a classification algorithm (linear or quadratic discriminant analysis). Performing measurements on 4 DAT proved too early to predict fruitlet drop with satisfactory reliability (forecast accuracy 65%). Measurements at 6xe2x80x9312 DAT resulted in forecast accuracies of 80xe2x80x9397%, depending on the selected dates. The method offers a nondestructive prediction of apple fruitlet drop rate, which could lead to the development of a lowcost device that could help manage chemical thinning.