Congliang Zhou, Assistant Professor of Precision Agriculture and AI at the
Louisiana State University School of Plant, Environmental and Soil Sciences

Congliang Zhou

Assistant Professor, Precision Agriculture and AI

Louisiana State University
School of Plant, Environmental and Soil Sciences

I am an assistant professor in the School of Plant, Environmental, and Soil Sciences at Louisiana State University (LSU), specializing in precision agriculture, artificial intelligence, remote sensing, and robotics. I joined the LSU Agricultural Center on July 1, 2024. The long-term vision of my research and extension program is to develop precision agriculture technologies to help stakeholders and increase the adoption rates of various precision agriculture technologies in Louisiana.

I hold a Ph.D. in Agricultural and Biological Engineering from the University of Florida, where I completed a dissertation focusing on an Artificial Intelligence-based Decision Support Tool for Twospotted Spider Mite Management in Strawberry. In Florida, the twospotted spider mite (TSSM) is the major arthropod pest in the strawberry field. Growers usually hire pest control advisors to manually count TSSM eggs and motiles (including all growing stages except eggs). Due to the tiny size of the pest, we need to use a magnifying lens to identify and count them, which is very time consuming, and they are difficult to count accurately. During my Ph.D. studies, I developed a smartphone app and portable device that can improve TSSM detection accuracy and reduce the amount of time required for scouting.

As shown in Figure 1, the user puts a strawberry leaf in the portable device. Then, the smartphone app controls the portable device to turn on the LEDs and take high-resolution pictures of the leaf. We have trained the artificial intelligence model and integrated it into the portable device. The artificial intelligence model can detect the pests and send the pest count information to the smartphone app and save it in the database. Users can visualize the sampling location on the Google map, and the number of pests can also be visualized on the map. Then, a spatial analysis model is used to estimate the pest population in unsampled locations, and a spatial distribution map is generated. This map can guide growers to apply the right amounts of pesticide in the right locations, which can reduce the usage of the pesticide and increase profit.

Our strawberry pest detection technologies received several awards, including the Standing Innovation Award from University of Florida Innovate and the Luby Microgrant from University of Florida Entrepreneurship and Innovation Center.

Figure 1. Portable device and smartphone app for twospotted spider mite monitoring in strawberries. (a) Portable device and smartphone app, (b) Spatial distribution of sampling points. The red dots represent the sampling locations. (c) Spatial distribution of pests. The different colors represent the different numbers of the pest at each location.
Figure 1. Portable device and smartphone app for twospotted spider mite monitoring in strawberries. (a) Portable device and smartphone app, (b) Spatial distribution of sampling points. The red dots represent the sampling locations. (c) Spatial distribution of pests. The different colors represent the different numbers of the pest at each location.

Precision agriculture has great potential in small fruit crops. Various sensors can be used to monitor field conditions, including RGB cameras, multispectral cameras, hyperspectral cameras, lidar (light detection and ranging), etc. These sensors can be mounted on satellites, drones, ground vehicles, or robots for data collection. Then, different artificial intelligence methods can be developed to analyze the data to estimate fruit yield, predict yield, monitor pest populations and disease, estimate nutrient contents, detect water stress, etc. In addition, variable rate technologies can be used to apply the right resources (fertilizer, pesticides, herbicides, water, etc.) in the right locations. Precision agriculture technologies can provide more efficient ways of field monitoring and management, which will enhance small fruit crop production and profitability.

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