Luan Pereira de Oliveira

Cover crops provide well-documented environmental benefits, including enhancing soil properties, promoting biodiversity, providing wildlife habitat, and reducing soil erosion (Adetunji et al. 2020;...

The transition to sustainable agricultural systems increasingly depends on the effective adoption of precision agriculture (PA) technologies. In the United States, Extension agents serve as key facilitators of agricultural innovation and play a critical role in supporting farmers through this transition. However, gaps in professional competencies can hinder their ability to promote and implement PA practices. This study conducted a needs assessment among agricultural Extension agents to evaluate perceived importance and organizational support for 13 core competencies essential to PA adoption. Findings revealed that the most significant needs for support from the Cooperative Extension Service included equipment operational skill, strategy execution, and problem-solving. These gaps highlighted that Extension agents perceived persistent gaps in their capacity to promote PA-related competencies. The Cooperative Extension Services must incorporate a balance of technical, operational, and soft skills to enhance Extension agents’ capacity to facilitate the adoption of precision agriculture. The study recommended that Extension systems inventory existing training resources, reallocate efforts, and invest in addressing the identified competency gaps presented in this study. Investing in the competencies needed for Extension agents will prepare them to effectively communicate precision agriculture practices and facilitate the adoption of the rapidly evolving precision agriculture technologies toward sustainable agriculture.

Unmanned aerial vehicles (UAVs) are transforming agriculture through enhanced data acquisition, improved monitoring efficiency, and support for data-driven decision-making. Complementing this, AI-driven platforms provide intuitive and reliable tools for advanced UAV analytics. However, their integration remains underexplored, particularly in specialty crops. Therefore, in this study, we evaluated the performance of an AI-driven web platform (Solvi) for automated plant counting and biometric trait estimation in two contrasting systems: pecan, a perennial nut crop, and onion, an annual vegetable. Ground-truth measurements included pecan tree number, tree height, and canopy area, as well as onion bulb number and diameter, the latter used for market class classification. Counting performance was assessed using precision, recall, and F1 score, while trait estimation was evaluated with linear regression analysis. UAV-based counts showed strong agreement with ground-truth data, achieving precision, recall, and F1 scores above 97% for both crops. For pecans, UAV-derived estimates of tree height (R² = 0.98, error = 11.48%) and canopy area (R² = 0.99, error = 23.16%) demonstrated high accuracy, while errors were larger in young trees compared with mature trees. For onions, UAV-derived bulb diameters achieved an R² of 0.78 with a 6.29% error, and market class classification (medium, jumbo, colossal) was predicted with <10% error. These findings demonstrate that UAV imagery integrated with a user-friendly AI platform can deliver accurate, scalable solutions for biometric monitoring in both perennial and annual specialty crops, supporting applications in harvest planning, orchard management, and market supply forecasting.

Robots are increasingly emerging as effective platforms to overcome a wide range of challenges in agriculture. Beyond functioning as standalone systems, agricultural robots are proving valuable as collaborative platforms, capable of supporting and integrating with humans and other technologies and agricultural activities. In this study, we designed and implemented an automated system embedded in a ground-based robotic platform to support spraying drone operations. The system consists of a robotic platform that carries the spraying drone along with all necessary support devices, including a water tank, chemical reservoirs, a mixer, generators for drone battery charging, and a top landing pad. The system is controlled with a mobile app that calculates the total amount of water and chemicals required and sends commands to the platform to prepare the application mixture. The input information in the app includes the field area, application rate, and up to three chemical dosages simultaneously. Additionally, the platform allows the drone to take off from and land on it, enhancing both safety and operability. A set of pumps was used to deliver water and chemicals as specified in the mobile app. To automate pump control, we used Arduino technology, including both the microcontroller and a programming environment for coding and designing the mobile app. To validate the system’s effectiveness, we individually measured the amount of water and chemical delivered to the mixer tank and compared it with conventional manual methods for calculating chemical quantities and preparation time. The system demonstrated consistent results, achieving high precision and accuracy in delivering the correct amount. This study advances the field of agricultural robotics by highlighting the role of collaborative platforms. Particularly, the system presents a valuable and low-cost solution for small farms and experimental research.

The use of unmanned aerial application systems (UAASs) for precision pesticide applications has increased alongside the demand for sustainable agricultural practices. However, limited studies have standardized the necessary flight parameters ensuring the optimal use of UAASs in specialty crops (e.g., fruits and vegetables). Thus, the objective of this study was to evaluate the effects of flight speed, droplet size, and application volume on the spray deposition of UAASs, creating guidelines to facilitate their use in specialty crops. Field experiments were conducted in a three-factorial experimental design of three flight speeds (i.e., 4, 7, and 10 m/s), three droplet sizes (i.e., 150, 250, and 350 µm), and two application volumes (i.e., 18.75 and 28.10 L/ha). Spraying droplet parameters (i.e., coverage, droplet density, and droplet spectra, and application uniformity), measured through the effective swath width, were recorded to assess spray deposition efficiency. Flight speed, droplet size, and application volume significantly influenced spray deposition. Treatments with slower flight speeds (4 m/s) and higher application volumes (28.10 L/ha) increased spray coverage, while droplet density was maximized at 4 m/s with the finest droplet size (150 µm), which are desirable characteristics for pesticide applications in specialty crops. Ultimately, the effective swath width and spray uniformity were maximized at a flight speed of 7.93 m/s with a droplet size of 350 µm. These results help optimize UAAS-based pesticide application, increasing efficiency and reducing environmental impact; however, understanding pesticide translocation dynamics (i.e., systemic or contact) on plants is key for growers to determine flight parameters.

Robots are increasingly emerging as effective platforms to overcome a wide range of challenges in agriculture. Beyond functioning as standalone systems, the agricultural robots are proving valuable as collaborative platforms, capable of supporting and integrating with other technologies and agricultural activities. In this study, we designed and implemented an automated system embedded in a robotic platform to support spraying drone operations. The system consists of a robotic platform that carries the spraying drone along with all necessary support devices, including a water tank, chemical reservoirs, a mixer, generators for drone battery charging, and a top landing pad. The system is controlled by a mobile app that calculates the total amount of water and chemicals required and sends commands to the platform to prepare the application mixture. The input information in the app includes field area, application rate, and up to three chemical dosages simultaneously. Additionally, the platform allows the drone to take off and land on it, enhancing both safety and operability. A set of pumps was used to deliver water and chemicals as specified in the mobile app. To automate pump control, we used Arduino technology, including both the microcontroller and a programming environment for coding and designing the mobile app. To validate the system’s effectiveness, we individually measured the amount of water and chemical delivered to the mixer tank and compared it with conventional manual methods for calculating chemical quantities and preparation time. The system demonstrated outstanding performance, achieving high precision and accuracy in delivering the correct amount. This study advances the field of agricultural robotics by highlighting the role of collaborative platforms. Particularly, the system presents a valuable and low-cost solution for small farms and experimental research.

The advancement of technology in agriculture has driven the use of drones for spraying, with their increasing adoption presenting challenges in calibration and volumetric distribution efficiency. This study aimed to evaluate the volumetric distribution of drone spraying by combining different operational parameters to determine spray swath and application uniformity. Experiments were conducted using a DJI T10 drone and a volumetric distribution table to assess the impact of different flight heights (2, 3, and 4 m), application volumes (8, 12, 16, and 20 L ha⁻¹), and nozzle types (FV 110 015, FL 110 010, and CO 080 010). Environmental conditions were monitored, and data were analyzed using histograms, analysis of variance (ANOVA) by F-test (p ≤ 0.05), and the Scott–Knott test (p ≤ 0.05) to group means. Results indicated that a lower application volume (8 L ha⁻¹) led to greater application uniformity and a narrower spray swath. Higher flight altitude (4 m) resulted in a wider spray swath and a normal distribution of spray deposition. Fine droplet nozzles (CO 080 010) enhanced uniformity, while very coarse droplets (FV 110 015) concentrated more volume in the center of the swath. Thus, using fine droplet nozzles (CO 080 010), lower application volume (8 and 12 L ha⁻¹), and higher flight altitude (4 m) as operational parameters maximizes drone spraying efficiency; however, this also increases drift potential.