Mapping is fundamental to the autonomous navigation of agricultural robots, as it provides a comprehensive spatial understanding of the farming environment. Accurate maps enable robots to plan efficient routes, avoid obstacles, and precisely execute tasks such as planting, spraying, and harvesting. Row crop navigation presents unique challenges, and mapping plays a crucial role in optimizing routes and avoiding obstacles in coverage path planning (CPP), which is essential for efficient agricultural operations. This study proposes a simple method for using Unmanned Aerial Vehicles (UAVs) to create maps and its application to row crop navigation. A case study is presented to demonstrate the method’s viability and illustrate how the resulting map can be applied in agricultural scenarios. This study focused on two major row crops, namely corn and soybean, but the results indicate that map creation is feasible when the inter-row spaces are not obscured by canopy cover from the adjacent rows. Although the study did not apply the map in a real-world scenario, it offers valuable insights for guiding future research.

Precision agriculture applies data-driven strategies to manage spatial and temporal variability within fields, aiming to increase productivity while minimizing pressure on natural resources. As interest in smart tillage systems expands, this study explores a central question: Can tillage tools be used to measure soil compaction during regular field operations? To investigate this, vibration data measurements were collected from a cultivator shank in the northeast of Kansas using the AVDAQ system. The test field soils were Reading silt loam and Eudora–Bismarck Grove silt loams. The relationship between shank vibrations, soil moisture (measured by a Hydrosense II soil–water sensor), and soil compaction (measured by a cone penetrometer) was evaluated using machine learning models. Both XGBoost and Random Forest demonstrated strong predictive performance, with Random Forest achieving a slightly higher correlation of 93.8% compared to 93.7% for XGBoost. Statistical analysis confirmed no significant difference between predicted and measured values, validating the accuracy and reliability of both models. Overall, the results demonstrate that combining vibration data with soil moisture data as model inputs enables accurate estimation of soil compaction, providing a foundation for future in situ soil sensing, reduced tillage intensity, and more sustainable cultivation practices.

Aphid infestations are one of the primary causes of extensive damage to wheat and sorghum fields and are one of the most common vectors for plant viruses, resulting in significant agricultural yield losses. To address this problem, farmers often employ the inefficient use of harmful chemical pesticides that have negative health and environmental impacts. As a result, a large amount of pesticide is wasted on areas without significant pest infestation. This brings to attention the urgent need for an intelligent autonomous system that can locate and spray sufficiently large infestations selectively within the complex crop canopies. We have developed a large multi-scale dataset for aphid cluster detection and segmentation, collected from actual sorghum fields and meticulously annotated to include clusters of aphids. Our dataset comprises a total of 54,742 image patches, showcasing a variety of viewpoints, diverse lighting conditions, and multiple scales, highlighting its effectiveness for real-world applications. In this study, we trained and evaluated four real-time semantic segmentation models and three object detection models specifically for aphid cluster segmentation and detection. Considering the balance between accuracy and efficiency, Fast-SCNN delivered the most effective segmentation results, achieving 80.46% mean precision, 81.21% mean recall, and 91.66 frames per second (FPS). For object detection, RT-DETR exhibited the best overall performance with a 61.63% mean average precision (mAP), 92.6% mean recall, and 72.55 on an NVIDIA V100 GPU. Our experiments further indicate that aphid cluster segmentation is more suitable for assessing aphid infestations than using detection models.

Despite having made much improvement in sensing, automation, and control, the current broadcast spraying system has several drawbacks, such as uneven coverage, excessive chemical use, and deviation from recommended dosage. Typically, farmers use large self-propelled sprayers to spray the entire field without knowledge of spatial pest severity, potentially resulting in an unintentional application. However, application errors and the extent of chemical use can be optimized by utilizing an intelligent site-specific decision-based sprayer to control pests more efficiently. Hence, the initial project goal was to design a robotic liquid application system for row crops (e.g., sorghum and corn) and validate sprayer system performance. The critical design considerations for the spray application system were modularity; the ability to be mounted on an autonomous platform to go within 76.2-cm spaced crops; spray on either side of the crop row using spray booms; onboard hardware and software for control and data acquisition; and record as-applied data. A system with desired design requirements was built and individual sub-systems were tested under simulated lab scenarios to quantify the response time and accuracy of the spray system. The results showed that the sprayer could maintain an average system pressure within ±5% of the target under different duty cycles for each of the six nozzles. At 40% duty cycle, the nozzle pressure settling time at an error margin of ±5% from the mean was 13 ms, 20 ms, and 19 ms, for one, three, and six nozzles, respectively. Also, no substantial pressure difference was observed between nozzles installed at different heights in two different booms. Therefore, this application system could be a viable solution for autonomous platforms to site-specifically apply pesticides only on critically infested plants, has the potential to decrease the overall input costs on chemicals and reduce the negative environmental impacts.

Since the invasion of the sorghum aphid Melanaphis sorghi (Theobald), farmers in the sorghum (Sorghum bicolor L. Moench) production region in the Great Plains of the U.S. have faced significant crop damage and reduced yields. One widely used practice to aid in managing sorghum aphids is pest monitoring, which often results in field-level insecticide applications when an economic threshold is reached. However, relying on this traditional management practice includes the application of insecticides to non-infested plants. To reduce insecticide usage in sorghum, we proposed spraying individual plants when aphids are present or absent compared to traditional spraying based on a standard economic threshold using field replicate plots over two summer seasons. The experimental results of this study indicated fewer aphids in plots managed with an economic threshold, followed by randomly sprayed and plant-specific treatments compared with the untreated control treatment. Therefore, compared with traditional management, those treatments can be alternative strategies for managing aphids on sorghum within our field plot study.

Aphid infestation poses a significant threat to crop production, rural communities, and global food security. While chemical pest control is crucial for maximizing yields, applying chemicals across entire fields is both environmentally unsustainable and costly. Hence, precise localization and management of aphids are essential for targeted pesticide application. The paper primarily focuses on using deep learning models for detecting aphid clusters. We propose a novel approach for estimating infection levels by detecting aphid clusters. To facilitate this research, we have captured a large-scale dataset from sorghum fields, manually selected 5447 images containing aphids, and annotated each individual aphid cluster within these images. To facilitate the use of machine learning models, we further process the images by cropping them into patches, resulting in a labeled dataset comprising 151,380 image patches. Then, we implemented and compared the performance of four state-of-the-art object detection models (VFNet, GFLV2, PAA, and ATSS) on the aphid dataset. Extensive experimental results show that all models yield stable similar performance in terms of average precision and recall. We then propose to merge close neighboring clusters and remove tiny clusters caused by cropping, and the performance is further boosted by around 17%. The study demonstrates the feasibility of automatically detecting and managing insects using machine learning models. The labeled dataset will be made openly available to the research community.

Uncontrolled spread of eastern red cedar invades the United States Great Plains prairie ecosystems and lowers biodiversity across native grasslands. The eastern red cedar (ERC) infestations cause significant challenges for ranchers and landowners, including the high costs of removing mature red cedars, reduced livestock forage feed, and reduced revenue from hunting leases. Therefore, a fleet of autonomous ground vehicles (AGV) is proposed to address the ERC infestation. However, detecting the target tree or trunk in a rangeland environment is critical in automating an ERC cutting operation. A tree trunk detection method was developed in this study for ERC trees trained in natural rangeland environments using a deep learning-based YOLOv5 model. An action camera acquired RGB images in a natural rangeland environment. A transfer learning method was adopted, and the YOLOv5 was trained to detect the varying size of the ERC tree trunk. A trained model precision, recall, and average precision were 87.8%, 84.3%, and 88.9%. The model accurately predicted the varying tree trunk sizes and differentiated between trunk and branches. This study demonstrated the potential for using pretrained deep learning models for tree trunk detection with RGB images. The developed machine vision system could be effectively integrated with a fleet of AGVs for ERC cutting. The proposed ERC tree trunk detection models would serve as a fundamental element for the AGV fleet, which would assist in effective rangeland management to maintain the ecological balance of grassland systems.

Aphid infestations can cause extensive damage to wheat and sorghum fields and spread plant viruses, resulting in significant yield losses in agriculture. To address this issue, farmers often rely on chemical pesticides, which are inefficiently applied over large areas of fields. As a result, a considerable amount of pesticide is wasted on areas without pests, while inadequate amounts are applied to areas with severe infestations. The paper focuses on the urgent need for an intelligent autonomous system that can locate and spray infestations within complex crop canopies, reducing pesticide use and environmental impact. We have collected and labeled a large aphid image dataset in the field, and propose the use of real-time semantic segmentation models to segment clusters of aphids. A multiscale dataset is generated to allow for learning the clusters at different scales. We compare the segmentation speeds and accuracy of four state-of-the-art real-time semantic segmentation models on the aphid cluster dataset, benchmarking them against nonreal-time models. The study results show the effectiveness of a real-time solution, which can reduce inefficient pesticide use and increase crop yields, paving the way towards an autonomous pest detection system.

Rapid advancements in technology, particularly in soil tools and agricultural machinery, have led to the proliferation of mechanized agriculture. The interaction between such tools/machines and soil is a complex, dynamic process. The modeling of this interactive process is essential for reducing energy requirements, excessive soil pulverization, and soil compaction, thereby leading to sustainable crop production. Traditional methods that rely on simplistic physics-based models are not often the best approach. Computational intelligence-based approaches are an attractive alternative to traditional methods. These methods are highly versatile, can handle various forms of data, and are adaptive in nature. Recent years have witnessed a surge in adapting such methods in all domains of engineering, including agriculture. These applications leverage not only classical computational intelligence methods, but also emergent ones, such as deep learning. Although classical methods have routinely been applied to the soil–machine interaction studies, the field is yet to harness the more recent developments in computational intelligence. The purpose of this review article is twofold. Firstly, it provides an in-depth description of classical computational intelligence methods, including their underlying theoretical basis, along with a survey of their use in soil–machine interaction research. Hence, it serves as a concise and systematic reference for practicing engineers as well as researchers in this field. Next, this article provides an outline of various emergent methods in computational intelligence, with the aim of introducing state-of-the-art methods to the interested reader and motivating their application in soil–machine interaction research.

Agricultural robotics is a comparatively new field. Recently, it has gotten special attention because of agricultural labor shortages and rising labor costs. With the increasing operational use of agricultural robots, the need for a dedicated robotic simulator for such activity has been noted here. The need of a dedicated and specialty simulator for agricultural robotics research is the main focus area of this paper. Moreover, limitations have been pointed out when conventional robot simulators are used directly in agricultural settings. In a nutshell, it has been tried to emphasize that a dedicated simulator for agricultural robotics would be a timely advance and would accelerate the growth of agricultural robots.

Conventional large agricultural machinery or implements are unsafe and unsuitable to operate on slopes > 6 ∘ $\gt {6}^{\circ }$ or 10%. Tractor rollovers are frequent on slopes, precluding farming on arable hills, uneven or highly sloped land. Therefore, a fleet of autonomous ground vehicles (AGV) is proposed to cultivate highly sloped land ( > 6 ∘ $\gt {6}^{\circ }$ ). The fleet aims to expand agricultural land to the slopes and to strengths the human‐robot collaboration in an unsafe sloped environment. However, the fleet's success largely depends on vehicle behavior models regarding traction, mobility, and energy consumption on varying slopes. The vehicle intelligent behavior models are essential and would solve multiple objectives ranging from simulations to path planning & navigation. Therefore, this study aimed to build a deep learning‐based vehicle behavior models on sloping terrain. A standard drawbar test was performed on a single AGV operating on an actual sloped field at varying speeds and load conditions. The drawbar test quantified the AGV's behavior on slopes in metrics related to traction (traction efficiency), mobility (travel reduction), and energy consumption (power number). Deep learning‐based models were developed from the experimental data to predict the AGV's behavior on slopes as a function of vehicle velocity, drawbar, and slope. A special model called the proposed model, which combined multiple deep neural networks with a mixture of Gaussians, was developed and trained with a hybrid training method. The proposed model consistently outperformed the other well‐known machine learning models. This study explored the capabilities of machine learning algorithms to simulate the behavior of small‐track vehicle or AGV on sloping terrain. The fleet aims to provide safer agriculture keeping human safety in focus, and the developed predictive vehicle behavior models would empower the fleet's operation on currently unsafe sloped terrain by assisting in vehicle path planning, route optimization, and decision making.

The encroachment of Eastern redcedar (ERC) (Juniperus virginiana L.) onto Great Plains prairies has become a serious threat to ecosystem functioning and grazing productivity. The uncontrolled spread of this invasive tree species has been called a »green glacier« converting grasslands into closed canopy woodlands. A pasture tree cutting robot was developed using a tracked Autonomous Ground Vehicle (AGV) equipped with a chainsaw bar to mitigate this green glacier dilemma. The prototype was fitted with amperage and voltage sensors to measure average power consumption and peak power requirements of tree cutting. It was evaluated on ERC and Honeylocust trees up to 20 cm in diameter. Cutting energy and time were determined to evaluate energy optimization and cutting time estimates. A pasture tree clearing energy consumption of the developed prototype was estimated for selected tree density/hectare. The prototype robot was successful in cutting down the intended size trees at a manageable power usage.

To obtain more consistent measurements through the course of a wheat growing season, we conceived and designed an autonomous robotic platform that performs collision avoidance while navigating in crop rows using spatial artificial intelligence (AI). The main constraint the agronomists have is to not run over the wheat while driving. Accordingly, we have trained a spatial deep learning model that helps navigate the robot autonomously in the field while avoiding collisions with the wheat. To train this model, we used publicly available databases of prelabeled images of wheat, along with the images of wheat that we have collected in the field. We used the MobileNet single shot detector (SSD) as our deep learning model to detect wheat in the field. To increase the frame rate for real-time robot response to field environments, we trained MobileNet SSD on the wheat images and used a new stereo camera, the Luxonis Depth AI Camera. Together, the newly trained model and camera could achieve a frame rate of 18–23 frames per second (fps)—fast enough for the robot to process its surroundings once every 2–3 inches of driving. Once we knew the robot accurately detects its surroundings, we addressed the autonomous navigation of the robot. The new stereo camera allows the robot to determine its distance from the trained objects. In this work, we also developed a navigation and collision avoidance algorithm that utilizes this distance information to help the robot see its surroundings and maneuver in the field, thereby precisely avoiding collisions with the wheat crop. Extensive experiments were conducted to evaluate the performance of our proposed method. We also compared the quantitative results obtained by our proposed MobileNet SSD model with those of other state-of-the-art object detection models, such as the YOLO V5 and Faster region-based convolutional neural network (R-CNN) models. The detailed comparative analysis reveals the effectiveness of our method in terms of both model precision and inference speed.

Approximately 45% of annual food production is lost due to pest infestation. To alleviate this, pesticides are extensively used in modern agriculture. Soybean is a major crop in the USA, accounting for 22% of total pesticide consumption. The current U.S. agricultural practices apply pesticides uniformly to the entire field. The excessive use of pesticides puts an unsustainable chemical load on the environment. Reduction in pesticides, is a need of the hour to preserve human health and the environment while maintaining the necessary level of food production. The excessive use of pesticides can be potentially averted with integrated pest management (IPM). However, crop monitoring, insect pest identification, and severity assessment are critical components of IPM success. Therefore, identifying the pest type and locating pest outbreak sites in soybean fields is the first step in developing an effective pest control management system. This study uses an open-source dataset of insect-damaged soybean leaves collected under realistic weather conditions using a cell phone and UAV cameras. The dataset includes 6410 images classified into three classes i.e, healthy plants, plants infected by caterpillars, and plants infected by diabrotica speciosa. Pretrained deep learning models such as 1) VGG16, 2) ResNet50, 3) InceptionV3, and 4) DenseNet201 were used with a transfer learning approach to classify the pest-damaged images accurately. The success of designed models was evaluated with classification accuracy and confusion matrix. The overall classification accuracy was 88%, 86%, 84% and 74% respectively for DenseNet201, VGG16, InceptionV3 and ResNet50. The DenseNet201 outperformed the other models in the study and is recommended for the soybean image classification problem.

Pest infestation causes significant crop damage during crop production, which reduces the crop yield in terms of quality and quantity. Accurate, precise, and timely information on pest infestation is a crucial aspect of integrated pest management practices. The current manual scouting methods are time-consuming and laborious, particularly for large fields. Therefore, a fleet of scouting vehicles is proposed to monitor and collect crop information at the sub-canopy level. These vehicles would traverse large fields and collect real-time information on pest type, concentration, and infestation level. In addition to this, the developed vehicle platform would assist in collecting information on soil moisture, nutrient deficiency, and disease severity during crop growth stages. This study established a proof-of-concept of a crop scouting vehicle that can navigate through the row crops. A reconfigurable ground vehicle (RGV) was designed and fabricated. The developed prototype was tested in the laboratory and an actual field environment. Moreover, the concept of corn row detection was established by utilizing an array of low-cost ultrasonic sensors. The RGV was successful in navigating through the corn field. The RGV's reconfigurable characteristic provides the ability to move anywhere in the field without damaging the crops. This research shows the promise of using reconfigurable robots for row crop navigation for crop scouting and monitoring which could be modular and scalable, and can be mass-produced in quick time. A fleet of these RGVs would empower the farmers to make meaningful and timely decisions for their cropping system.

HighlightsLaying the groundwork for AGV mobility models for high slope terrain operations.AGV drawbar pull performance was evaluated on a level terrain, uphill, and downhill slopes up to 18° on a soil bin.AGV generates the optimum power efficiency with enough drawbar pull to perform a range of agricultural operations on uphill and downhill slopes up to 18°.Explored the suitability and established the boundary conditions of small size ground vehicles on the high slope farming.Generated sloped traction data would empower the multi-AGV system on sloped terrain.Abstract. Excessive steepness of grasslands, hills, or uneven terrain presents difficulties for farming with large conventional equipment. Therefore, a fleet of Autonomous Ground Vehicles (AGV) is proposed to perform primary agricultural operations on high sloped hills or terrain. However, it is imperative to understand how an individual AGV functions on sloping terrain under varying load and speed. Hence, this study aims to investigate the traction, mobility, and energy consumption characteristics of AGV on a sloped soil bin environment. A drawbar pull performance of the prototype AGV was evaluated on a level terrain and variable slope of 10° and 18°, both uphill and downhill, at varying drawbar pull (P) and AGV speed. The AGV’s performance metrics include power efficiency (PE), travel reduction (TR), and power number (PN) which relates to AGV’s traction, mobility, and energy usage, respectively. The AGV generated drawbar pull equivalent to its weight only on downhill run for reduced PE. On a level terrain (0°), the peak PE was 0.20 and was found to be 108.3% and 328.6% higher on 10° and 18° downhill run than uphill with 55.5% and 133% increase in drawbar pull, respectively. Both applied drawbar pull and uphill operation caused the AGV’s TR. The TR, corresponding to a peak PE, increased from 10% to 30%, respectively, on 0° and both 10° and 18° uphill. The optimum values of power number ranged from 2 to 4. The AGV delivers the optimum PE and generates enough drawbar pull with an optimum TR to perform a range of agricultural operations on a slope up to 18°. This study explored the suitability and established the boundary conditions of small size ground vehicles for high-sloped farming. Besides this, the study also aims to generate an AGV’s slope traction database to optimize its control variables, design optimization, and develop a mobility model for sloped terrain. Keywords: Drawbar pull, Ground vehicle, Multi-AGV fleet, Power efficiency, Slope, Travel reduction.

A fleet of autonomous ground vehicles (AGV) is envisioned to expand farming to arable land suitable for production except for being too steep for conventional equipment. The success of proposed multi-AGV system largely depends on the traction performance of the individual AGVs on unevenly sloped terrain and optimization of the AGVs control variables. Therefore, the drawbar pull performance of a prototype AGV was evaluated in a soil bin at varying slopes, speeds, and drawbar pull (DP). The AGV’s traction performance was expressed in three metrics: tractive efficiency (TE), travel reduction ratio (TRR), and power number (PN). Optimizing the control variables is intricate and ill-defined, which requires an accurate model to predict the performance of the proposed multi-AGV system. Hence, this study aims to design an artificial neural network (ANN) to estimate the traction behavior of the AGV on a sloped testbed as a function of AGV’s speed, applied DP, and slope. A multi-layer perceptron feed-forward ANN architecture with a single hidden layer trained with a back-propagation algorithm was adopted. A series of ANN models with increasing complexity and different hidden layer activation functions were developed for each response variable, i.e., ANN-TE, ANN-TRR, and ANN-PN. A re-sampling-based method, K-fold cross-validation, was employed to estimate the model generalization error. The model success was evaluated via Mean Squared Error (MSE) and the Coefficient of Determination (R2) against a test set. The final predictive model was trained on the entire data set, and the observed R2 was 0.933, 0.882 and 0.858, respectively, for ANN-TE, ANN-TRR, and ANN-PN. Subsequently, a Monte-Carlo Simulation based uncertainty analysis was carried out to demonstrate the model strength and the degree of uncertainty by constructing a 95% prediction interval. This study shows ANN as a promising, robust, and reliable method to predict traction performance in agricultural tillage-traction studies and developed models can empower the multi-AGV system on steep-uneven slope terrain.

Highlights An auger-type feed mechanism was designed for a robotic wheat drill, and a laboratory investigation was carried out. To avoid seed blockage, the recommended screw auger pitch must be at least 150% of the maximum seed dimension. The performance of the feed mechanism was influenced by auger speed, vibration, and slope. This study delivered a bulk feed mechanism for wheat drilling, which can be easily scaled and adopted by small autonomous vehicles or mobile robots. Abstract. Cultivating the arable, highly sloped hills and uneven terrain is challenging and unsafe with large agricultural machines. Therefore, a fleet of small autonomous ground vehicles (AGVs) was proposed to farm sloped or uneven terrain. The fleets need a robotic grain drill to operate on varying slopes, and the success of the fleet depends on the performance of the robotic seeder or grain drill. The feed mechanism is the heart of the seeder, and its design and performance influence the plant population and crop yield. In this study, we designed and fabricated an auger-type feed mechanism for robotic wheat drilling. Feed mechanisms with augers having three different pitches were developed as per the ASABE standards. The developed feed mechanism was investigated in a laboratory setup for flow rate and flow uniformity in accordance with ISO standards. The predictor variables were auger type (pitch), auger rotational speed, vibration, and slope. The auger flow rate for flat slopes was a linear function of auger speed and varied from 30 g/min to 170 g/min. The coefficient of variation (CV) for the flow rate ranged from 2% to 10%. The CV was within acceptable limits, which was an excellent indicator of the bulk feed mechanism's flow uniformity. The performance of the feed mechanism was influenced by vibration and slope. However, the auger flow rate remained constant for vibration frequencies of 0, 6, and 14 Hz, suggesting that the feed mechanism was vibration-proof and could tolerate the vibration frequency up to 14 Hz. The flat, downhill (descending), and uphill (ascending) slope levels did not affect the feed mechanism performance. However, the side slopes (right and left slope) significantly affected the feed mechanism flow rate but did not affect the flow uniformity. The study also developed a feed mechanism for a sloped-ground prototype seeder, which can be easily scaled and adopted by small autonomous vehicles or mobile robots. Keywords: Flow rate, Flow uniformity, Multi-robot, Robotic seed drill, Screw auger, Seed rate.

This article proposes a novel algorithm to determine the optimal coverage path of a mobile robot for uniform seed dispersion in any tract of agricultural land. The robot is required to operate under steep terrain conditions that are too risky for conventional, human operated equipment. Using data from an field experiment with the real robot, machine learning based function approximators are trained to estimate the minimum energy paths between adjacent points. Exemplar-based clustering is used to identify a suitable subset of way-points that ensure full coverage of a tract of land. An optimal cyclic tour is computed from the way points using a new asymmetric TSP algorithm proposed for this specific application. The clustering and the cyclic path planning algorithms can be implemented entirely through local message passing in a field-deployed sensor network. Simulations with synthetic as well as real topographic data establish the overall effectiveness of the proposed method.

HighlightsNozzle pressure drop varies between PWM systems at different application rates and application pressures.Change in flow rate with respect to the expected flow differs between PWM systems at different rates and pressures.There was a latency before the system reached the target application pressure.PWM systems operate for less time than the specified duty cycle, which may cause application errors.Abstract. Three PWM nozzle control systems, Capstan PinPoint II, John Deere ExactApply, and Raven Hawkeye, referred to as systems S1, S2, and S3, respectively, were used in this study. Data on nozzle pressure, boom pressure, flow rate, and response time were recorded with different duty cycles (25%, 50%, 75%, and 100%) and operating frequencies (10, 15, and 30 Hz) for two application rates (112.2 and 187.1 L ha-1) and two application pressures (275.8 and 448.2 kPa) at 1 kHz using a LabVIEW program and a cRIO data acquisition system. Results indicated that the PWM systems perform differently when operating at different application rates, pressures, duty cycles, and frequencies. Each PWM system provided a different pressure drop at the nozzle during operation. The increase in application rate and pressure increased the pressure drop. The percent change in flow rate with respect to the expected flow was also significantly different between the PWM systems, which could be due to the differences in pressure provided at the nozzle during operation. The PWM systems also showed latency before reaching the target application pressure during operation and operated for less time than the specified duty cycle at stable target pressure while also continuing to spray even after the solenoid valves had closed. The application pressure during peak and fall times and the time of stable application pressure within a cycle should be given careful consideration when selecting a PWM system, as they can contribute to application errors. Producers should also consider the pressure drop with the selected PWM system and target application rate to set up the system to apply at the desired pressure. Manufacturers mostly recommend operating PWM systems at 10 Hz. For the purpose of this study, the operating frequency of the PWM systems was set to 10 and 15 Hz for S1, to 15 and 30 Hz for S2, and to 10, 15, and 30 Hz for S3. Producers should expect differences in pressure drop, stabilized pressure application time, and flow rate if they choose to operate at a higher frequency. The results of this study are only applicable to the types of nozzle bodies and nozzle tips used. The data will differ based on the dual-orifice valve coefficient equation: the larger the second orifice, the greater the pressure drop. This will affect the final orifice pressure, as well as the flow rate. This study did not address the impact of flow resistance caused by differences in the design of nozzle bodies and nozzle types. Keywords: Nozzle flow rate, Pressure drop, Pulse width modulation control modules, Response time.