SBIR-STTR Award

Project Summary: The Company proposes to research the feasibility of reducing chemical insecticide and labor required while increasing the data collected using advanced imaging proprietary artificial intelligence and attractants in monitoring detecting and managing agricultural pests over the 8-month project period. This research specifically targets Amyeloistransitella aka Navel Orangeworm (NOW) the primary pest in California almonds which causes more than $900M of annual losses. The SolaRid™ product applies algorithms to determine crop phenology and timing of insects biofix events while monitoring abiotic conditions with sensors. It utilizes five interchangeable insect attractant modules to lure specific targeted pests. Finally a proprietary artificial intelligence (AI) technology developed by an awarded National Science Foundation (NSF) grant is used to detect target species infestations completing a comprehensive analytic decision support system. More efficient insect control will result from the quality of data analyzed by SolaRid allowing for reduced chemical insecticide use by increasing the specificity and timeliness of the applied input. More finely attuned applications in time and space will reduce residuals on produce and the amount of insecticides in our environment. EPA licensed agricultural Pest Control Advisors (PCA) are becoming more dependent on sensors and remotely generated data. SolaRid's use is consistent with this trend improving the quality/ accessibility of data saving up to 6 hours/day of in-field monitoring data is available 24 hours/7 days a week remotely. Project Objectives and a Description of the Effort. SolaRid will conduct the field trials with UC ANR. The objective is to test the AI technology to develop empirical data of the combined technologies' efficacy. The initial steps include setting up communication networks calibrating sensors and confirming protocols. Then while the device is being tested the Company will elicit product design and procedural recommendations from PCA's and producers. An example of how this process would work for adjusting the algorithm is broadly outlined at the end of the Technical Objectives section. There are five research questions concerning validity benefits and performance of the sensor outputs and procedures. Results are intended to create actionable intelligence for data driven management decisions developed during this phase I research. In phase II the Company will continue to collaborate with its California partners to complete the insect control device (ICD) creating a complete and sustainable turn-key solution. At the end of phase II we will have multiple fully functional units to competently identify and count local insects and identify key agricultural pests of high economic importance in nut orchard industry.