Spatial Data Driven Concord Crop Estimation and Adjustment

Terry Bates

As part of the Specialty Crop Research Initiative project on vineyard spatial crop load management, we have been investigating ways to use sensors and spatial data to (a) improve the accuracy of mid-season crop estimates and to (b) test the ability to perform variable rate fruit thinning in NY Concord vineyards. Last week, the CLEREL team performed spatial crop estimates in seven different commercial vineyards. The following update summarizes a portion of the research done on crop estimation and variable rate thinning in cooperation with the Betts family in Westfield, NY.


Characterizing spatial vineyard variation:  The first step in improving crop estimation through directed vineyard sampling is to understand the spatial growth patterns within vineyard blocks.  This project uses mobile soil (DualEM) and canopy (CropCircle – NDVI) sensors to measure and map both soil and vine growth patterns (left).  The objective is to identify healthy regions of the vineyard with higher production potential as well as identify regions of weak vine growth that may need additional management for improvement.  For example, the research shows that early- and mid-season canopy sensor data relates to harvest yield potential (right).  Similar to pruning weight measurements, vines with a low NDVI sensor readings because of lower canopy growth and, therefore, lower sunlight interception have lower fruit production potential.  In contrast, big vines with large canopies and full light interception have higher yield potential.   


Stratified Sampling:  In contrast to picking random sample locations across a vineyard block, directed or stratified samples may be more accurate by taking into account known sources of variation identified by the sensor data.  The continuous spatial sensor maps are simplified into management classification maps with 2, 3, or 4 classifications that make sense to the vineyard manager.  In our crop estimation example, the vineyard was broken into classifications of low, medium, and high NDVI (and potentially low, medium, and high yield).  Crop estimation sample locations were generated so that the low, medium, and high vineyard regions were all sampled.  In each sample location, vines were cleaned picked with a harvester and the fruit was weighed.  In this case, the sample size was one percent of an acre identified with a rope on the ground (left).  The harvester is also equip with a field computer and grape yield monitor (right).  The field computer runs precision agriculture software (AgLeader, SMS) which shows the spatial management classification map (generated by Rhiann Jakubowski at CLEREL) and the location of the harvester in the field.  The yield monitor records the weight of fruit as it goes over the cross conveyor belt.


Recording Fruit Weight:  The stratified sample locations were clean picked with a grape harvester and the green fruit was discharged into a bucket on a platform scale and weighed by Dawn Betts (left).  At the same time, the grape yield monitor recorded the fruit weight as it was discharged over the cross conveyor belt.  The yield monitor data was then compared to the actual scale weights to test the performance of the yield monitor (inset).  For each management classification, sample fruit weights were multiplied by a berry weight factor to give the predicted harvest yield.  In this case, crop estimation was done at 30 days after bloom with the assumption that the berries were 50% of the final berry weight; therefore, sample weights were multiplied by 2 to give the predicted harvest yield.  For the whole field estimate, a weighted estimate was calculated by multiplying the management classification yield estimate by the size of each management classification.   


Variable Rate Crop Adjustment:  Once an accurate crop estimate is calculated, the fruit thinning or crop adjustment procedure starts with the vineyard manager making an educated decision on if the crop needs to be reduced in any particular management classification and how much the crop should be reduced within that management classification.  A manager may decide that no fruit thinning is needed, or only that the weak vines need to be thinned, or that all the zones need to be thinned at different rates.  In this case, Thom Betts set up his harvester so that the shaker rod RPMs could be adjusted through the harvester hydraulic system and controlled by the AgLeader software.  With a little testing in each management classification, shaker speeds and ground speeds were determined to reach the desired yield levels in each vineyard area.  These values were entered into the field computer software.  The harvester was then driven over the whole field while the shaker speed and thinning rate were adjusted on-the-fly by the field computer.  Check plots where no fruit thinning was done (seen as the small light blue boxes in the left image) were incorporated into the prescription map.  At harvest, the grape yield monitor data will be used to measure the effect of this variable rate thinning trial.  This work on crop estimation and variable rate fruit thinning is just a portion of the larger specialty crop research initiative project on spatial crop load management.  The project is national in scope with research in juice, wine, and table grape industries and multi-disciplinary with project leaders in engineering, viticulture, precision agriculture, economics, and extension.  The project is also made possible by the innovation and effort of industry cooperators, such as the Betts, in directing and integrating research into real-world practice.