Robert Hardin

Early detection of nitrogen deficiency in cotton requires timely identification of stress symptoms like leaf chlorosis (yellowing) and canopy stunting. Chlorosis initially appears in older, lower-canopy leaves, which are often not visible in conventional nadir-looking imaging. This study investigates oblique ground-based multispectral imaging to estimate plant height and capture spectral details from the upper (UC) and lower (LC) cotton canopy layers. Images were collected from four camera pitch and height configurations: set 1 (30°, 2 m), set 2 (55°, 2 m), set 3 (68°, 3 m), and set 4 (70°, 1.5 m). A pre-trained monocular depth estimation model (MiDaS) was used to estimate plant height from aligned RGB images and an empirically derived tangential model corrected for perspective distortion. Further, the lower and upper vertical halves of the plants were categorized as LC and UC, with vegetation indices (CI_green, CI_rededge) calculated for each. The aligned images in set 1 had the best sharpness and quality. The plant height estimates from set 1 had the highest correlation (r = 0.64) and lowest root mean squared error (RMSE = 0.13 m). As the images became more oblique, alignment and monocular depth/height accuracy decreased. Also, the effects of perspective and object-scale ambiguity in monocular depth estimation were prominent in the high oblique and relatively low altitude images. The spectral vegetation indices (VIs) were affected by band misalignment and shadows. VIs from the different canopy layers demonstrated moderate correlation with leaf nitrogen concentration, and sets 2 and 3 specifically showed high and low differences in VIs from the UC and LC layers for the no and high-nitrogen treatments, respectively. However, improvements in the multispectral alignment process, extensive data collection, and ground-truthing are needed to conclude whether the LC spectra are useful for early nitrogen stress detection in field cotton.

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 × 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 real-time 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.

Plastic shopping bags are often discarded as litter and can be carried away from roadsides and become tangled on cotton plants in farm fields. This rubbish plastic can end up in the cotton at the gin if not removed before harvest. These bags may not only cause problems in the ginning process but might also become embedded in cotton fibers, reducing the quality and marketable value. Therefore, detecting, locating, and removing the bags before the cotton is harvested is required. Manually detecting and locating these bags in cotton fields is a tedious, time-consuming, and costly process. To solve this, this paper shows the application of YOLOv5 to detect white and brown colored plastic bags tangled at three different heights in cotton plants (bottom, middle, top) using Unmanned Aircraft Systems (UAS)-acquired Red, Green, Blue (RGB) images. It was found that an average white and brown bag could be detected at 92.35% and 77.87% accuracies and a mean average precision (mAP) of 87.68%. Similarly, the trained YOLOv5 model, on average, could detect 94.25% of the top, 49.58% of the middle, and only 5% of the bottom bags. It was also found that both the color of the bags (p < 0.001) and their height on cotton plants (p < 0.0001) had a significant effect on detection accuracy. The findings reported in this paper can help in the autonomous detection of plastic contaminants in cotton fields and potentially speed up the mitigation efforts, thereby reducing the amount of contaminants in cotton gins.