SBIR-STTR Award

Specialty Crop production margins are eroded by input costs impacts of weather pests and diseases and market price fluctuations. Producers routinely overproduce to hedge against losses from environmental impacts and ensure sufficient supply to meet retail account demand further reducing their average margins. If they had greater certainty in advance of how much they will produce and when it will be harvest-ready they could consistently improve the match between supply and demand reducing overall losses and increasing margins. This project will develop precision crop maturity forecasting models for a number of key fresh vegetable crops based on computer vision analysis of imagery collected over fields during the crop growing cycle weather and solar irradiance data and knowledge of phenological development stages of each crop. During Phase I we will analyze historical planting harvest and weather data from one of the top- 10 producers in the industry for three representative crops combined with photosynthetically active radiation (PAR) data from satellites to determine the extents to which temperature and PAR history profiles can provide sufficiently accurate predictive models of harvest maturity dates. We will also collect high resolution aerial imagery weather and radiation data over fields during the Phase I period to determine the feasibility of using phenological development stages as determined from computer vision analysis to improve the forecasting capability. We will compare forecasted to actual harvest dates and yields for a set of representative crops grown in California and Arizona. The proposed capability will enable producers to know with high accuracy a field's different levels of maturity and when they can expect most of each field to be ready for harvest. This intelligence will significantly increase their ability to manage their supply in the face of environmental variabilities ultimately reducing waste and increasing their margins. We plan to sell the forecasting capability as a subscription service.

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-