Volunteer cotton plant detection in corn field with deep learning

Pappu Kumar Yadav; J. Alex Thomasson; Robert G. Hardin; Stephen W. Searcy; Ulisses Braga-Neto; Sorin C. Popescu; Daniel E. Martin; Roberto Rodriguez III; Karem Meza; Juan Enciso; Jorge Solorzano; Tianyi Wang

doi:10.1117/12.2623032

3 June 2022 Volunteer cotton plant detection in corn field with deep learning

Pappu Kumar Yadav, J. Alex Thomasson, Robert G. Hardin, Stephen W. Searcy, Ulisses Braga-Neto, Sorin C. Popescu, Daniel E. Martin, Roberto Rodriguez III, Karem Meza, Juan Enciso, Jorge Solorzano, Tianyi Wang

Author Affiliations +

Proceedings Volume 12114, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VII; 1211403 (2022) https://doi.org/10.1117/12.2623032
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Volunteer cotton (VC) plants growing in fields of inter-seasonal crop like corn can act as host for the boll weevil pests; therefore, they need to be detected, located, and sprayed to prevent reinfestation of the pest in the following season. However, detecting the VC plants in corn fields has always been challenging as they remain hidden under the canopy and appear spectrally similar during the early growth phase. In this paper, we show that deep learning based YOLOv3 model can be used to detect VC plants in early growth corn field on RGB aerial images collected remotely by unmanned aircraft system (UAS) at a mean average precision (mAP) of 90.60% and F1-score of 86.35%. The approach of using deep learning to detect VC plants demonstrates its ability to be used for near real-time detection thereby expediting the management aspects of Texas Boll Weevil Eradication Program.

Conference Presentation

Citation Download Citation

Pappu Kumar Yadav, J. Alex Thomasson, Robert G. Hardin, Stephen W. Searcy, Ulisses Braga-Neto, Sorin C. Popescu, Daniel E. Martin, Roberto Rodriguez III, Karem Meza, Juan Enciso, Jorge Solorzano, and Tianyi Wang "Volunteer cotton plant detection in corn field with deep learning", Proc. SPIE 12114, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VII, 1211403 (3 June 2022); https://doi.org/10.1117/12.2623032

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