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.
Project Team

Ana C. Arias
Professor of Electrical Engineering and Computer Sciences, UC Berkeley

Brian Bailey
Assistant Professor of Plant Sciences, UC Davis

Christine Diepenbrock
Assistant Professor of Plant Sciences, UC Davis

Mason Earles
Assistant Professor of Viticulture & Enology and Biological & Agricultural Engineering, UC Davis

Yufang Jin
Associate Professor of Land, Air, and Water Resources, UC Davis

Isaya Kisekka
Associate Professor of Land, Air, and Water Resources, UC Davis

Zhaodan Kong
Associate Professor of Mechanical and Aerospace Engineering, UC Davis

Xin Liu
Professor of Computer Science, UC Davis

Simo Mäkiharju
Assistant Professor of Mechanical Engineering, UC Berkeley

Khalid Mosalam
Professor of Civil Engineering, UC Berkeley

Mark Mueller
Assistant Professor of Mechanical Engineering, UC Berkeley

Alireza Pourreza
Assistant CE Specialist of Agricultural Mechanization, Biological and Agricultural Engineering, UC Davis

Aaron Smith
Professor of Agricultural Economics, UC Davis

Stavros Vougioukas
Associate Professor of Biological and Agricultural Engineering, UC Davis

Tarek Zohdi
Professor of Mechanical Engineering, UC Berkeley