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ARS Home » Plains Area » College Station, Texas » Southern Plains Agricultural Research Center » Aerial Application Technology Research » Research » Publications at this Location » Publication #417981
Research Project: Improved Aerial Application Technologies for Precise and Effective Delivery of Crop Production Products

Location: Aerial Application Technology Research

Title: AI-driven computer vision detection of cotton in corn fields using UAS remote sensing data and spot-spray application

Author
item YADAV, PAPPU - Texas A&M University
item THOMASSON, J - Mississippi State University
item HARDIN, ROBERT - Texas A&M University
item SEARCY, STEPHEN - Texas A&M University
item BRAGA-NETO, ULISSES - Texas A&M University
item POPESCU, SORIN - Texas A&M University
item RODRIGUEZ, ROBERTO - Animal And Plant Health Inspection Service (APHIS)
item Martin, Daniel - Dan
item ENCISO, JUAN - Texas A&M University

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/24/2024
Publication Date: 7/27/2024
Citation: Yadav, P.K., Thomasson, J.A., Hardin, R., Searcy, S.W., Braga-Neto, U., Popescu, S.C., Rodriguez, R., Martin, D.E., Enciso, J. 2024. AI-driven computer vision detection of cotton in corn fields using UAS remote sensing data and spot-spray application. Remote Sensing. 16:2754. https://doi.org/10.3390/rs16152754.
DOI: https://doi.org/10.3390/rs16152754

Interpretive Summary: The cotton boll weevil is a serious pest to the U.S. cotton industry that has cost more than 16 billion USD in damages since it entered the United States from Mexico in the late 1800s. These studies used aerial photos at three different spatial resolutions to detect volunteer cotton plants in corn fields. Results showed that the images with the lowest resolution were just as good at detecting volunteer cotton plants as those with the highest resolution. This work has the potential to speed up eradication efforts for boll weevil in the U.S.

Technical Abstract: To effectively combat the re-infestation of boll weevils (Anthonomus grandis L.) in cotton fields, it is necessary to address the detection of volunteer cotton (VC) plants (Gossypium hirsutum L.) in rotation crops such as corn (Zea mays L.) and sorghum (Sorghum bicolor L.). The current practice involves manual field scouting at the field edges, which often leads to the oversight of VC plants growing in the middle of fields alongside corn and sorghum. As these VC plants reach the pinhead squaring stage (5–6 leaves), they can become hosts for boll weevil pests. Consequently, it becomes crucial to detect, locate, and accurately spot-spray these plants with appropriate chemicals. This paper focuses on the application of YOLOv5m to detect and locate VC plants during the tasseling (VT) growth stage of cornfields. Our results demonstrate that VC plants can be detected with a mean average precision (mAP) of 79% at an Intersection over Union (IoU) of 50% and a classification accuracy of 78% on images sized 1207 x 923 pixels. The average detection inference speed is 47 frames per second (FPS) on the NVIDIA Tesla P100 GPU-16 GB and 0.4 FPS on the NVIDIA Jetson TX2 GPU, which underscores the relevance and impact of detection speed on the feasibility of realtime applications. Additionally, we show the application of a customized unmanned aircraft system (UAS) for spot-spray applications through simulation based on the developed computer vision (CV) algorithm. This UAS-based approach enables the near-real-time detection and mitigation of VC plants in corn fields, with near-real-time defined as approximately 0.02 s per frame on the NVIDIA Tesla P100 GPU and 2.5 s per frame on the NVIDIA Jetson TX2 GPU, thereby offering an efficient management solution for controlling boll weevil pests.