BACKGROUND

Yield forecasting is vital for agricultural management, logistics preparation, and order fulfillment. Moreover, forecasting yield at high spatio-temporal resolution enables precision management activities, mechanization, and automation that can increase grower output and quality, while reducing resource inputs and waste. For example, early yield forecasting can enable automation of variable-rate application of inputs like water, fertilizer, and pesticides, which reduces expenses and environmental impact for growers, all while increasing productivity. However, yield forecasting is very challenging for specialty crops, spanning diverse environmental conditions, crop traits, and management strategies.

GOALS

• Design, build, and deploy yield monitoring sensors to accelerate ground-truth yield data collection
• Develop a syntheticDevelop a synthetic data generation pipeline that couples a 3D crop model to a deep learning framework for yield prediction
• Create a deep learning framework that fuses multi-modal ground based and aerial data streams to directly predict yield from large-scale ground-truth data that incorporates human decisions
• We will pursue these objectives for the three most economically valuable crops in California: strawberry, grape, and almond.

IMPACT

Achieving the objectives will lay the groundwork for efforts in subsequent years on agricultural automation, mechanization, and robotics. By accelerating ground-truth data collection through new sensor development and synthetic data generation we will address AI challenges in data-efficiency. Further, we will develop novel multi-modal sensor fusion machine learning frameworks to improve yield forecasting in several economically and environmentally impactful crops.