AI-Enabled Water and Nitrogen Stress Sensing and Forecasting for Sustainable Agricultural Production

AI-ENABLED WATER AND NITROGEN STRESS SENSING AND FORECASTING FOR SUSTAINABLE AGRICULTURAL PRODUCTION

Background

This project aims to develop a data-efficient, AI-enabled framework for water (W) and nitrogen (N) stress sensing and prediction. To do this, we will pursue the following sub-objectives below. We will pursue these objectives for almond, which is the most widely irrigated and economically valuable crop in California. Rapid detection and prediction of W and N stress is vital for optimizing production. Moreover, monitoring and predicting stresses at high spatio-temporal resolution enables precision management activities, mechanization, and automation that can increase grower output and quality, while minimizing negative environmental impacts. Despite its economic and environmental significance, W and N stress prediction are very challenging for specialty crops, due to their complex structure and physiology, diverse crop traits, and a wide range of environmental conditions and management strategies.

Goals

To dramatically accelerate the rate of ground-truth W and N data collection, we will design, build, and deploy W and N stress sensors. Specifically, we will instrument existing commercial farms with in-situ, proximal, and remote sensing systems for measuring W and N status in soil and plants at multiple scales.
To substantially increase data-efficiency and model generalizability, we will develop a synthetic data generation pipeline that couples biophysical modeling and deep learning for W and N stress prediction. A 3D crop model called Helios will generate millions of 2D synthetic sensor imagery of almond cultivars across combinations of environmental and management scenarios, with trees of estimated W and N stress values. We will then use this synthetic data to pre-train models to estimate W and N stress in actual production conditions.
To overcome current models’ inability to predict W and N stress with high accuracy at individual tree scale, we will create a deep learning framework that fuses multiple ground-based and aerial data streams to directly predict W and N. Novel deep learning architectures will be developed to fuse multiple data.

Impact

By developing an AI-enabled framework for near real-time monitoring and a prediction that are generalizable across many specialty crops, we expect to transform US food systems by innovating AI technology for a more sustainable food production system.