Precision Harvest Forecasting of Fresh Vegetable Crops

Specialty crop producers routinely overproduce to hedge against losses from cultivation andenvironmental impacts and market price fluctuations and to ensure sufficient supply to meet retailand food service account demand. If they had greater certainty in advance of how much they willproduce and when it will be harvest-ready they could consistently improve the match betweensupply and demand reducing overall costs any resulting losses and increasing margins. Thisproject will develop precision crop maturity forecasting models for several key fresh vegetablecrops. Current approaches forecast harvest dates based on historical planting and harvestinformation and do not capture the significant variabilities from local weather soil conditions andfarmer practices. Our approach is to apply forecasted weather data with a unique modelingtechnique and supplement with innovative computer vision analysis of aerial imagery collectedover fields during the crop growing cycle. During Phase II we will refine the methods that wereproven for iceberg lettuce during Phase I acquiring more data and validating the approach fordifferent varieties and geographic locations. In addition we will extend the techniques from lettuceto other leafy green crops that have been identified by our grower partners as priorities forimproved forecasting.The proposed innovation will help growers and grower-processors produce more efficientlyreducing crop waste and saving on their production costs in a number of direct ways: first bydetermining earlier if fields will not produce sufficient yield or within the needed time windowto justify additional expenditures; then by helping them allocate worker resources more cost- effectively by directing harvest crews where they are most needed and when; and finally byhelping them plan and manage their supply against fluctuations related to weather pests weeds ordisease outbreaks. Our commercialization strategy is to demonstrate the improved forecasting toour current customers by comparing our forecasted harvest dates and yields to their actualschedules and yields. We will integrate results into our web and mobile applications which ourcustomers currently use for production management and with less precise forecasting based onplant population counts we derive for them from aerial imagery. Interested customers can thenpurchase the enhanced service as an upgrade to their current subscriptions.