Low-Cost Cameras Could Be Sensors to Remotely Monitor Crop Stress

Being able to identify crop problems early can make the difference between saving a crop and losing it, but high-tech solutions can be costly. An interdisciplinary team of researchers thinks a new approach leveraging existing technology may be part of the solution.

Specifically, NC State researchers in the Department of Crop and Soil Sciences and the Department of Electrical and Computer Engineering are launching an inexpensive camera system that can monitor crop stress remotely.

Corn and soybeans are important commodities for North Carolina and the world. Both are eaten fresh, processed into a variety of foodstuffs and turned into animal feed. A lack of water at certain stages stress the plants, and can make a significant dent on yields.

Paula Ramos-Giraldo, a computer vision and machine learning expert in the Department of Crop and Soil Sciences, has spent the past year working on a camera system that costs less than an average smartwatch to track drought stress in corn and soybean fields.

“Our goal, specifically, was to construct a low-cost sensor to track the soil moisture level in the field through plant behavior,” Ramos-Giraldo said.

These low-cost sensors can help researchers studying ways to make agricultural systems more resilient; plant breeders breeding more drought-tolerant varieties; and some day may be able to alert farmers when their fields need to be irrigated.

Constructing a StressCam

The StressCam system — constructed from parts that cost about $150 — is based around a Raspberry Pi. A Raspberry Pi is a tiny, inexpensive and easily-programed computer originally designed for teaching computer science.

The tiny WiFi-enabled computer includes a camera for taking pictures of a field and is hooked up to a timer that turns the system on in the morning and off in the evening, Ramos-Giraldo said. For corn, the camera is mounted at a 90-degree angle over the field and takes photos every 30 minutes to watch for curling leaves. For soybeans, the camera is mounted at a 45-degree angle over the field and takes photos every 15 minutes to watch for wilting. The system is solar powered, with a back-up battery for cloudy days.

The tiny computer runs a machine learning algorithm on the photos to analyze them for indications of drought stress. Then it sends this information to a web platform for researchers, breeders or farmers, she said.

Both the machine learning algorithm and the web platform were constructed with help from students in the Department of Electrical and Computer Engineering.

Teaching a Machine to Score Stress

During the fall 2019 semester, Ramos-Giraldo worked with Edgar Lobaton, an associate professor in the Department of Electrical and Computer Engineering, to enlist the students in his Neural Networks class to…