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Jason Ward

Associate Professor and Extension Specialist

Professional Engineer

D. S. Weaver Labs NA

Bio

Selected Current Projects

 

Quantifying Crop Lodging Damage with UAS Imagery

In North Carolina, harvest season is hurricane season.  The possibility for weather-related crop damage is a real danger for producers.  After a severe weather event, crop health must be quickly assessed for the amount and severity of damage. Producers, insurance providers, and emergency managers need timely and accurate information to understand the scope of crop damage.  The objectives of this study are to compare specific measurement technologies to find the best suite of tools to quantify the severity of damage and area of impact.  Recently, multiple different deep learning object detectors were compared for their ability to automatically detect simulated lodged corn.  Traditional vegetative indices and machine learning techniques are being explored to identify the most efficient tool.  The measured impacts will be compared to the physiological impacts on the crop.  Finally, a best method of federal or commercial reporting and securing that data to current standards will be developed.

 

Cotton Quality Mapping

Modern cotton harvesting equipment has the capability to weigh, estimate moisture content of, and discretely identify round modules when completed.  These data along with bale location should allow tracking of gin fiber quality data all the way back to a field area via the permanent bale indicator (PBI) assigned at the gin and the radio-frequency Harvest ID (HID) tag assigned at harvest.  The first year of this project confirmed the proof-of-concept and the industry was receptive to the ability to translate fiber quality to a field basis. So far, this project has resulted the development of one of the first whole-field cotton fiber map in the US.

 

Automating the Cotton Replant Decision with UAS Imagery

Cotton replant decisions are often made based on a quick, ground-level visual inspection of fields. UAV plant counts are currently available but an expert decision tool is needed to help quickly process the resulting data into a replant recommendation.

 

Robot Systems to Support Pasture Animal Performance

Pasture animals are less comfortable and perform poorly when they are heat stressed. Heat stress can be mitigated with fixed or portable shade. Fixed shade causes animals to congregate which leads to negative environmental impacts and poor animal performance caused by muddy conditions and accumulated waste. Mobile shades are prone to damage, especially from wind, and can be difficult to move or operate. An autonomous robotic animal support platform is being developed which will autonomously move in a pasture which reduces localized environmental impacts, distributes waste, and protects forage. Environmental sensors will monitor the environment and respond to support the animal. Sensors will be used to detect how animals use the structure and if they are less stressed.

 

Improving Harvest Efficiency with Machine Data

Peanut harvesting is a complex series of field activities requiring multiple passes across the field with multiple implements.  There is limited time to get the job done before harvest will suffer.  Optimizing harvest efficiency, machine sizing, and machine settings are essential to getting in and out of the field as quickly as possible.  Modern farm equipment can generate substantial data during day-to-day operations.  The tools now exist to tap into this data and leverage that information to ensure that harvest operations are running as smooth as possible.  The objectives of this project are to collect that machine data and use it to measure harvest field operation efficiency and to understand how to best manage the equipment to save time and money.  The same data can be used to understand how to mitigate harvest risk within the allowable working days by properly sizing machines to the farm.

Education

B.S. Biosystems and Agricultural Engineering University of Kentucky 2003

M.S. Biosystems and Agricultural Engineering University of Kentucky 2004

Ph.D. Biological Engineering Mississippi State University 2012

Area(s) of Expertise

The Advanced Ag Lab works at the intersection of technology and farming. We leverage an understanding of agricultural methods and practices combined with technology, sensors, equipment, robotics, and data management to drive real-world decision making. We conduct applied research to identify existing or create new data in the modern precision agriculture environment and work closely with commodity experts to move from data collection to actionable insight. We develop and deliver innovative Extension programming that make the technology already on the farm more accessible and valuable. We defines precision agriculture as a methodology of data-driven decision making to improve output, manage impact, or reduce waste. This approach allows us to work across commodities and market sectors to create useful tools which allow best management at higher resolutions no matter the crop, animal, or equipment involved.

Publications

View all publications 

Grants

Date: 02/01/23 - 1/31/26
Amount: $93,240.00
Funding Agencies: NC Soybean Producers Association, Inc.

The cost of soybean production is increasing. Nutrient inputs are at historically high costs at a time where there is increased variability in growing season conditions. Crops can experience multiple kinds of stresses and better information is needed on strategies that allow the crop to perform as well as possible while using a minimum of inputs. In North Carolina three of the most substantial production costs are seed, nutrient inputs, and water management infrastructure. This project further develops relationships between water stresses and nutrient uptake so that the response to stress conditions will protect the variety???s yield potential, optimize nutrients, and suggest water management approaches ??? addressing all the major cost centers. The larger outcome of this research and extension effort is in-season response to water challenges such that nutrient input may be reduced, applications timed to environmental stress and profitability improved. The ability to utilize UAV data to assess plant water and nutrient stress in-season as it occurs and ultimately use the data to prescribe an appropriate course of action is necessary to protect soybean crop profits. This process will provide a framework to quickly assess areas in the field and allow for corrective measures to protect the crop from climatic conditions that ultimately would cause severe yield reductions.

Date: 02/01/22 - 1/31/25
Amount: $148,977.00
Funding Agencies: Corn Growers Association of NC, Inc.

The cost of corn production is increasing. Nutrient inputs are at historically high costs at a time where there is increased variability in growing season conditions. Crops can experience multiple kinds of stresses and better information is needed on strategies that allow the crop to perform as well as possible while using a minimum of inputs. In North Carolina three of the most substantial production costs are seed, nutrient inputs, and water management infrastructure. This project further develops relationships between water stresses and nutrient uptake so that the response to stress conditions will protect the variety????????s yield potential, optimize nutrients, and suggest manage water approaches ???????? addressing all the major cost centers. The larger outcome of this research and extension effort is in-season response to water challenges such that nutrient input may be reduced and profitability improved.

Date: 01/01/18 - 12/31/24
Amount: $325,150.00
Funding Agencies: Cotton, Inc.

Modern cotton harvesting equipment has the capability to weigh, estimate moisture content of, and discretely identify round modules when completed. These data along with bale location should allow tracking of gin fiber quality data all the way back to a field area via the permanent bale indicator (PBI) assigned at the gin and the radio-frequency Harvest ID (HID) tag assigned at harvest. The ultimate purpose of this project is a proof-of-concept linking fiber quality data to specific field locations within a cotton field. The objectives of this research are: 1. To identify data workflows and best management practices to connect USDA fiber quality data to specific round modules, 2. to map fiber quality to spatial locations within production fields, and 3. to better understand logistics management in modern cotton harvest such as in-field transport and prioritizing high-moisture round modules.

Date: 01/01/12 - 12/31/24
Amount: $228,370.00
Funding Agencies: Cotton, Inc.

Precision agriculture technology is being adopted in many crop production enterprises to optimize crop yields, minimize their associated costs and reduce environmental impacts of excessive crop inputs. Precision agriculture technology enhances management through variable-rate application of lime, fertilizers, pesticides, seeds and tillage. The management protocol is based on determining soil variability by georeferenced soil sampling/mapping, determining yield potential by georeferenced yield monitoring, and using agronomic recommendations for variable-rate application of inputs. Technologies available include georeferenced soil sampling, on the go crop sensors, variable rate control systems, and yield monitors.

Date: 02/01/21 - 1/31/24
Amount: $103,411.00
Funding Agencies: NC Soybean Producers Association, Inc.

Unmanned Aircraft (drones) are marketed in the agricultural sector as a ???????????????revolutionary?????????????????? technology. Although the technology and corresponding data are truly unique, the application of data outputs for agricultural management decisions (e.g., re-plant, pest management) remain unclear. This interdisciplinary project will investigate the use of drones in five key production areas 1) re-plant decisions, 2) incidence of fungal disease, 3) severity of insect-related defoliation, 4) weed identification and management, and 5) nutrient deficiencies. The project will evaluate common commercial drone technology to document baseline potential for decision support in soybean. The information generated by this project will be used to provide robust training to County Extension Agents and farmers across North Carolina on the use of these technologies to enhance profitability.

Date: 10/01/21 - 9/30/23
Amount: $86,024.00
Funding Agencies: United Soybean Board

We propose to develop a pilot Data Science Extension program at North Carolina State University, with a primary focus on data-driven soybean production. In the near-term, we will leverage our networks to interview stakeholder groups and identify grower interests and needs in data services and training, and use this information to prepare a vision and series of recommendations for Extension programs across the country regarding data science Extension programming. Additionally, we will develop a series of data science Extension products -- including tutorials, workshop materials, social media releases -- tailored for soybean growers. These products will be advertised through the Soybean Research Information Network to ensure national visibility and access. In the long-term, we will pursue additional funds to establish additional data-driven soybean research and Extension projects that serve the priorities identified through interviews held during this project, and advance the field and practice of data science Extension. USB funding is specifically sought to support personnel who will interview growers, summarize findings in a white paper, and prepare data-focused soybean Extension products.

Date: 03/01/20 - 8/31/23
Amount: $22,034.00
Funding Agencies: NC Sweet Potato Commission

One of the most expensive labor costs associated with sweetpotato production occurs during transplanting. Current approaches require multiple (between 4 and 16) human operators to manually sort and singulate individual slips from a pile and feed them into a planter that buries the slip into the ground. The ability to automate the slip singulation process would allow growers to save on prohibitively expensive labor costs while increasing transplanting efficiency. This project will take an integrated computer vision, mechanization, and robotics approach to automatically separate, identify, and grasp a single slip for transplanting operations.

Date: 02/01/21 - 1/31/22
Amount: $10,229.00
Funding Agencies: Corn Growers Association of NC, Inc.

Severe weather, especially during Atlantic hurricane season, will continue to impact corn production in North Carolina. After the negative impacts have occurred, quickly assessing the extent and severity of the damage can aid in recovery and mitigation of losses. UAVs or manned aircraft provide opportunities to survey locations where crop lodging may have occurred, but often significant additional analysis was needed. Rapid assessment of the collected imagery can help to direct effort to where it is most needed.

Date: 10/01/20 - 9/30/21
Amount: $22,038.00
Funding Agencies: US Dept. of Agriculture (USDA)

Farmers across Eastern North Carolina are becoming increasingly dependent on precision agriculture (PA) technologies to sustain profit and compete nationally and globally. Schimmelpfennig (Farm Profits and Adoption of Precision Agriculture, USDA ERS 2016), Ward et. al (Precision Agriculture Technologies for Cotton Production in North Carolina, Cotton, Inc. 1/01/12 - 12/31/20) and others have documented that PA technologies including telematics on agricultural machinery, in-field sensor networks and other automated data centric applications provide farmers with increased efficiencies and reduced operating costs by helping detect and manage the impact of crop damage, invasive species, erratic weather patterns, irrigation levels and other key elements to improve yields and reduce risk. Auto-steered combines and tractors help reduce operator errors by determining precise field locations (that are often difficult to determine accurately by sight) and compensating for operator fatigue. Field operators using guidance systems have real-time positional and operational information in the equipment cab which saves money by reducing over- and under application of crop inputs and off-target seeding of field crop rows. Advanced guidance systems allow equipment to return to the exact same position within the field which improves all in-season field operations including harvest. PA also enables farmers to determine the ideal time to plant and manage resources such as water, soil, fertilizer, seeds and labor to increase profits. As food borne illnesses become more prevalent, PA also helps farmers track and trace the production and delivery of their crops from farm to markets to consumers to mitigate food safety issues, spoilage and waste across the value chain. Consumers are demanding sustainability and to know the origin and production system used to grow the food they eat and the fibers they wear. Only through traceability throughout the value chain, enabled by broadband data streams, can those requests be met in a meaningful way.

Date: 04/15/19 - 4/14/21
Amount: $24,867.00
Funding Agencies: Corn Growers Association of NC, Inc.

As North Carolina corn producers have been reminded in recent years, harvest season is hurricane season. After a severe weather event crops must be rapidly assessed for producer decision making, disaster declarations, and insurance claims. Producers need to know how to assess losses and plan remediation. Disaster response agencies need to quickly estimate agricultural losses. Insurance providers need to verify claims and prepare for financial outlays. Agricultural statisticians need to estimate crop damage to report to federal agencies. This project will identify underlying relationships to describe crop damage from UAV imagery, automate the analysis process, and engage stakeholders to better understand data. This project will build on a preliminary project to use unmanned aerial vehicles (UAVs) to build tools to detect crop damage from imagery and to then automate the process using machine learning tools. Imagery from unmanned aerial vehicles emphasize early to mid-season analyses. The late season environment has not received similar efforts in data collection or analytical tools. New data-driven tools are needed to skillfully identify the presence and severity of crop damage. Long term outcomes of this project will allow better response, recovery, and resiliency when crop damage occurs. Understand the underlying damage metrics and automating the damage detection processes can put UAV-based crop damage detection to work in a region already heavily impacted by severe weather.


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