Performance Assessment of Crop Line Detection in Corn Field from Unmanned Aerial Vehicle Video

Rafi, M., Senyurek, V., & Gurbuz, A. (2024). Performance Assessment of Crop Line Detection in Corn Field from Unmanned Aerial Vehicle Video. SPIE 13053, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IX. National Harbor, Maryland, United States: SPIE. 13053, 89-98. DOI:doi.org/10.1117/12.3013501.

In recent times, precision agriculture, an approach that utilizes scientific and technological advancements and techniques for the enhancement of agricultural production, usually starts with the crop line detection procedure. Crop line detection helps precision agriculture with the mapping of the crop fields, which is useful for agricultural resources (water, fertilizer, pesticides, etc.) management, crop yield estimation, autonomous harvesting and irrigation management, disease and pest control, weed detection, controlled monitoring by autonomous machines and so forth. Although the aim of crop line detection in this inquiry is weed detection, which can aid the farmers regarding the optimum usage of herbicides in the field, it can be extended to any precision agriculture study. In this study, two different methods are employed for crop line detection: Hough transformation and Pixel/Frequency counting. The study was conducted on a 1.2-ha corn field through 2020 - 2023 that covers the crop period of corn (April ∼ August). More than 7000 high-spatial-resolution RGB images are collected using a GoPro camera attached to a custom-made unmanned aerial vehicle. Around 10% of these images are randomly selected for this analysis. RGB image frames were extracted from the video files and organized according to their weekly growth timeline. Normalized Excess Green Vegetation Index is calculated to convert them into two-level binary images. 2D Fourier transform is used to find the average crop line angle. Comparing the crop lines detected from both procedures with the actual crop lines present in the respective image frame, confusion matrix information is constructed for the performance evaluation. The average accuracy of crop line detection found for Hough transformation is 87.79%, and for Pixel counting, it is 95.71%, which can be promising choices to be employed for crop line detection.