Better Almond Water Stress Monitoring Using Fractional-order Moments of Non-normalized Difference Vegetation Index

Stem water potential (SWP) has become a very popular tool for farmers to monitor the water status of almond trees. However, it is labor intensive and time consuming to scale up the measurements in the large field. With the development of unmanned aerial vehicles (UAVs) and sensing payload, it becomes possible to monitor the water status much more efficiently with UAV-based multispectral images of higher spatial resolution and more flexible temporal resolution. Driven by this possibility, studies have been started in a commercial almond orchard since 2014 to research almond water stress monitoring using the small unmanned aerial vehicle and the modified multispectral camera. More specifically, we are researching how to predict almond SWP by extracting information from these multispectral images. Recent experiments showed that traditional vegetation indices such as normalized difference vegetation index (NDVI) do not work very well with high resolution aerial images to predict SWP. Meanwhile, we found non-normalized difference vegetation index (NNDVI) between near-infrared (NIR) band, blue band, and its higher order moments have a better correlation with SWP. In this paper, we proposed the fractional-order moments of NNDVI and discussed its correlation with SWP. It is shown that the correlation between the proposed fractional-order moments of NNDVI and SWP is more significant than that between the traditional NDVI and SWP.

[1]  Janine Hasey,et al.  Plant water status as an index of irrigation need in deciduous fruit trees , 1997 .

[2]  E. Milton,et al.  The use of the empirical line method to calibrate remotely sensed data to reflectance , 1999 .

[3]  Yangquan Chen,et al.  Challenges in Water Stress Quantification Using Small Unmanned Aerial System (sUAS): Lessons from a Growing Season of Almond , 2016, Journal of Intelligent & Robotic Systems.

[4]  A. Hoekstra,et al.  Four billion people facing severe water scarcity , 2016, Science Advances.

[5]  H. Nilsson.  Remote sensing and image analysis in plant pathology. , 1995, Annual review of phytopathology.

[6]  Joan Girona,et al.  Sensitivity of Continuous and Discrete Plant and Soil Water Status Monitoring in Peach Trees Subjected to Deficit Irrigation , 1999 .

[7]  L. S. Pereira,et al.  Crop evapotranspiration : guidelines for computing crop water requirements , 1998 .

[8]  Brandon Stark,et al.  A detailed field study of direct correlations between ground truth crop water stress and normalized difference vegetation index (NDVI) from small unmanned aerial system (sUAS) , 2015, 2015 International Conference on Unmanned Aircraft Systems (ICUAS).

[9]  Raoul R. Nigmatullin,et al.  The statistics of the fractional moments: Is there any chance to "read quantitatively" any randomness? , 2006, Signal Process..

[10]  YangQuan Chen,et al.  Melon yield prediction using small unmanned aerial vehicles , 2017, Commercial + Scientific Sensing and Imaging.

[11]  YangQuan Chen,et al.  An analysis of the effect of the bidirectional reflectance distribution function on remote sensing imagery accuracy from Small Unmanned Aircraft Systems , 2016, 2016 International Conference on Unmanned Aircraft Systems (ICUAS).

[12]  Allan Fulton,et al.  Using the Pressure Chamber for Irrigation Management in Walnut, Almond and Prune , 2014 .

[13]  Marco Dubbini,et al.  Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images , 2015, Remote. Sens..

[14]  R. O. Slayter,et al.  Plant-water relationships. , 1967 .

[15]  E. Fereres,et al.  Improving the precision of irrigation in a pistachio farm using an unmanned airborne thermal system , 2014, Irrigation Science.

[16]  John P. Fulton,et al.  An overview of current and potential applications of thermal remote sensing in precision agriculture , 2017, Comput. Electron. Agric..

[17]  D. Raes,et al.  Crop yield response to water , 2012 .

[18]  Piero Toscano,et al.  Intercomparison of UAV, Aircraft and Satellite Remote Sensing Platforms for Precision Viticulture , 2015, Remote. Sens..

[19]  M. Delwiche,et al.  A Continuous Leaf Monitoring System for Precision Irrigation Management in Orchard Crops , 2014 .

[20]  Thomas Appel,et al.  Plant Water Relationships , 2001 .

[21]  Xiang Zhou,et al.  Evaluation of a UAV-based hyperspectral frame camera for monitoring the leaf nitrogen concentration in rice , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[22]  Brandon Stark,et al.  More Reliable Crop Water Stress Quantification Using Small Unmanned Aerial Systems (sUAS) , 2016 .

[23]  E. Fereresa,et al.  Almond tree canopy temperature reveals intra-crown variability that is water stress-dependent , 2011 .

[24]  Uri Yermiyahu,et al.  Spatial distribution of water status in irrigated olive orchards by thermal imaging , 2013, Precision Agriculture.

[25]  Dong Wang,et al.  Infrared canopy temperature of early-ripening peach trees under postharvest deficit irrigation , 2010 .

[26]  L. Suárez,et al.  DETECTING WATER STRESS IN ORCHARD CROPS USING PRI FROM AIRBORNE IMAGERY , 2009 .

[27]  Yangquan Chen,et al.  Fractional calculus in image processing: a review , 2016, ArXiv.

[28]  Pablo J. Zarco-Tejada,et al.  Airborne Thermal Imagery to Detect the Seasonal Evolution of Crop Water Status in Peach, Nectarine and Saturn Peach Orchards , 2016, Remote. Sens..

[29]  K. Shackel,et al.  Drought management for California almonds , 2016 .

[30]  W. H. Olson,et al.  THE RELATION OF MIDDAY STEM WATER POTENTIAL TO THE GROWTH AND PHYSIOLOGY OF FRUIT TREES UNDER WATER LIMITED CONDITIONS , 2000 .