A team of researchers from North Carolina State University and Iowa State University have created a technology that automates the measurement of leaf angles on corn plants in the field. The AngleNet automatic robot helped researchers collect data on leaf angles faster and more efficiently than conventional methods, providing plant breeders with valuable data quickly.
The angle of a plant’s leaves relative to its stem is critical to measure because it impacts the plant’s ability to perform photosynthesis, says Lirong Xiang, an engineer at NC State and lead author of the study.
In corn, for example, vertical leaves at the top and flat leaves further down the stalk increase the plant’s capacity to harvest sunlight. Therefore, plant breeders focusing on plant architecture monitor leaf angles to inform their work.
The researchers compared the leaf angle measurements performed by the robot in a cornfield with those taken by hand to evaluate AngleNet’s accuracy. The angles assessed by AngleNet were found to be within five degrees of the angles measured manually, which is well within the permitted margin of error for plant breeding reasons, the researchers discovered.
“We are already collaborating with several crop experts to use this technology, and we are hopeful that other researchers will be interested in doing the same. Our ultimate objective is to hasten crop yield-improving plant breeding research,” said Xiang.
However, conventional techniques for measuring leaf angles are time-consuming and labour-intensive. Therefore, the researchers wanted to find a way to automate the process. The AngleNet technology comprises two primary components: hardware and software.
The equipment, which is a robotic device on wheels, can move between crop rows that are set 30 inches apart, which is the typical width employed by farmers. To record an additional level of leaves on the surrounding plants, it contains four levels of cameras, each of which is positioned at a different height. To give a stereoscopic view of the leaves and to make 3D modelling of plants easier, each layer has two cameras.
The U.S. National Science Foundation-funded equipment is guided down a row of plants while taking several stereoscopic photographs from various heights of each plant it encounters. A computer programme that calculates the leaf angle for each plant’s leaves at various heights is fed all the visual data.
According to Xiang, plant breeders must know the leaf angle and how far the leaves are from the ground. This information enables them to assess the leaf angle distribution for each row of plants, allowing them to identify genetic lines with desirable or undesirable traits.
The NSF’s Major Research Instrumentation program, according to Robert Fleischmann, a programme director in the division of biological infrastructure, made investments that resulted in developments in robotics and sensing that have a practical impact on farming, plant breeding, and crop production.
Apart from the U.S., some Southeast Asia countries have issued directives on using robotics, sensors, and AI to increase agricultural production. For instance, the government of Vietnam recently conducted a workshop to discuss how integrating artificial intelligence (AI) may significantly advance modern and sustainable agriculture. It can help with the automation and optimisation of farming activities, including but not limited to weather forecasting, keeping track of the health of plants and animals, and improving the quality of the result.
In Indonesia, the development of new, high-tech agricultural machinery incorporating artificial intelligence (AI) has been encouraged. To create better farms where everything can be automated with information technology (IT), electronics, the Internet of Things (IoT), and other fields. The use of precision farming techniques, such as the deployment of aerial drones to apply nutrients and water, represents the next wave of agricultural innovation.
The Thai government’s Office of Economic Promotion and Assistance (depa) trained a new generation of technologically savvy farmers to explore ways to increase farmers’ and enterprises’ use of digital technology.
