Roberto Rodriguez

Achieving uniform agrochemical deposition in coffee is challenging because canopy structure, terrain, and wind conditions influence spray movement and retention. This study compared an unmanned aerial spray system (UASS), backpack sprayer, and tractor-mounted sprayers across three commercial coffee farms on Hawai‘i Island. Spray coverage, droplet density, droplet size metrics, and operational efficiency were evaluated using water-sensitive cards positioned throughout the canopy and analyzed using mixed-effects models. UASS produced significantly lower spray coverage and droplet density than the ground-based application systems, whereas backpack and tractor sprayers did not differ. Deposition patterns varied with canopy position, with application method effects depending on canopy height, depth, and aspect. Volume median diameter decreased in the upper canopy and with increasing wind speed, while relative span varied modestly among methods and was greater within the canopy interior. Canopy position and wind strongly shaped agrochemical deposition across spray platforms. Although UASS required less field labor and improved accessibility in terrain-limited systems, these operational advantages were accompanied by reduced deposition relative to ground-based sprayers. These findings demonstrate that canopy position and environmental conditions strongly influence agrochemical deposition and support UASS as a complementary application platform rather than a direct replacement for conventional sprayers under the conditions evaluated.

Rangeland grasshoppers are an endemic species that play an essential role in the rangeland ecosystem but can cause severe economic damage when populations reach outbreak levels. Remotely piloted aerial application systems (RPAASs) offer an alternative method to carry out aerial insecticide applications in relatively small areas. The objective of this study was to investigate the efficacy of a granular bait, 2% Sevin (with the active ingredient carbaryl), applied by an RPAAS. The bait was applied on four replicated 4.05-hectare (10-acre) plots at a rate of 2.27 kg/ha (5 lbs/acre) with an RPAAS on a private ranch in New Mexico. Applications resulted in a normalized population reduction of 70.32% ± 16.54% standard error of the mean (SEM) of bait-susceptible species. Although some of the observed reduction in population may be attributed to aging, the net effect was most likely due to the ingestion of bait based on field observations of rapid mortality after ingestion and other factors, like past experience with carbaryl bait treatments on grasshoppers. Plots required at least two flights due to the Federal Aviation Administration’s (FAA) maximum takeoff weight requirement for small RPAASs. Combined, these results indicate that RPAASs can provide treatment capabilities in relatively small areas, i.e., population hotspots, preferably before outbreak levels are reached.

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.

Unmanned aircraft systems (UAS), commonly referred to as drones, are an emerging technology that has changed the way many industries conduct business. Precision agriculture is one industry that has consistently been predicted to be a major locus of innovation for UAS. However, this has not been the case globally. The agricultural aircraft sector in the United States is used as a case study here to consider different metrics to evaluate UAS adoption, including a proposed metric, the normalized UAS adoption index. In aggregate, UAS operators only make up 5% of the number of agricultural aircraft operators. However, the annual number of new UAS operators exceeded that of manned aircraft operators in 2022. When used on a state-by-state basis, the normalized UAS adoption index shows that there are regional differences in UAS adoption with western and eastern states having higher UAS adoption rates while central states have significantly lower UAS adoption rates. This has implications for UAS operators, manufacturers, and regulators as this industry continues to develop at a rapid pace.

Grasshoppers are integral parts of rangeland ecosystems but also have the potential to reach population densities high enough (outbreaks) to cause serious economic damage from forage loss and affect adjacent crops. The objective of this study was to investigate the efficacy of treating grasshopper population hotspots with a liquid insecticide using a remotely piloted aerial application system (RPAAS), as opposed to fixed-wing aircraft, which is the most common method currently in use. A liquid insecticide, Sevin XLR PLUS (containing carbaryl), was applied on replicated 4.05-hectare (10-acre) plots with an RPAAS on a ranch in New Mexico. Our results demonstrated that Sevin XLR PLUS significantly suppressed grasshopper populations over a 14-day period (normalized population reduction was 79.11 ± 8.35% SEM) and quite rapidly (mostly by day 3) compared to untreated controls. These results are comparable to those achieved with fixed-wing aircraft. The RPAAS covered the whole test area in a single flight in approximately 5 min, making these population hotspot treatment applications relatively rapid, potentially more cost-effective, and more targeted in comparison to fixed-wing aircraft. Before adoption as an application method option, further research is recommended on using an RPAAS to cover larger areas in combination with using diflubenzuron-based insecticides, which are often preferred.

Miconia is a highly invasive plant species with incipient plants occupying remote areas of Hawaiian watersheds. Management of these incipient plants is integral to current containment strategies. Herbicide Ballistic Technology (HBT) has been used for 8 years from helicopters as a precision approach to target individual plants. We have developed a prototype HBT applicator integrated onto an unmanned aircraft system, HBT-UAS, which offers the same precision approach with a semi-automated flight plan. Inclusion of the HBT payload resulted in statistically significant deviations from programmed flight plans compared to the unencumbered UAS, but the effect size was lower than that observed for different stages of flight. The additional payload of the HBT-UAS resulted in a large reduction in available flight time resulting a limited range of 22 m. The projectile spread of the HBT-UAS, within a 2–10 m range, had a maximum CEP of 1.87–5.58 cm. The most substantial limitation of the current prototype HBT-UAS is the available flight time. The use of larger capacity UAS and potential for beyond visual line of sight operations would result in a substantial improvement in the serviceable area and utility of the HBT-UAS for containment of invasive plants.

Timely, accurate maps of invasive plant species are critical for making appropriate management decisions to eliminate emerging target populations or contain infestations. High-resolution aerial imagery is routinely used to map, monitor, and detect invasive plant populations. While conventional image interpretation involving human analysts is straightforward, it can require high demands for time and resources to produce useful intelligence. We compared the performance of human analysts with a custom Retinanet-based deep convolutional neural network (DNN) for detecting individual miconia (Miconia calvescens DC) plants, using high-resolution unmanned aerial system (UAS) imagery collected over lowland tropical forests in Hawai’i. Human analysts (n = 38) examined imagery at three linear scrolling speeds (100, 200 and 300 px/s), achieving miconia detection recalls of 74 ± 3%, 60 ± 3%, and 50 ± 3%, respectively. The DNN achieved 83 ± 3% recall and completed the image analysis in 1% of the time of the fastest scrolling speed tested. Human analysts could discriminate large miconia leaf clusters better than isolated individual leaves, while the DNN detection efficacy was independent of leaf cluster size. Optically, the contrast in the red and green color channels and all three (i.e., red, green, and blue) signal to clutter ratios (SCR) were significant factors for human detection, while only the red channel contrast, and the red and green SCRs were significant factors for the DNN. A linear cost analysis estimated the operational use of a DNN to be more cost effective than human photo interpretation when the cumulative search area exceeds a minimum area. For invasive species like miconia, which can stochastically spread propagules across thousands of ha, the DNN provides a more efficient option for detecting incipient, immature miconia across large expanses of forested canopy. Increasing operational capacity for large-scale surveillance with a DNN-based image analysis workflow can provide more rapid comprehension of invasive plant abundance and distribution in forested watersheds and may become strategically vital to containing these invasions.

Global Navigation Satellite System (GNSS) receivers make use of the United States' Global Positioning System (GPS), Russia's Global Navigation Satellite System (GLONASS), Europe's Galileo, China's Beidou, and Japan's Quasi-Zenith Satellite System constellations to determine the geolocation of the receiver based on the location of satellites and measurements of the distance and travel time of the GNSS signals. GNSS signals are dependent on line of sight to satellites with reflections or obstructions resulting in errors in the estimated position [1]. GNSS systems, while having their origin in military applications [2], have been adopted for many civilian uses from recreational navigation [3] to automated vehicle location systems [4]. While the GPS Standard Positioning Service without any correction sources establishes a 30 m range error limit for civilian use, GPS receivers typically perform better than this standard with errors primarily from atmospheric interference [5]. This is further improved in mobile devices by assisted GPS, WiFi, and cellular positioning when available [6].