AgNet DEMO

Category: innovation

  • Smart Irrigation Tools for Blueberry Growers

    Figure 1. A: The University of Georgia Smart Sensor Array (UGA SSA) node is installed in blueberries. The electronics are housed in the white PVC container. The spring allows the antenna to bend when farm vehicles pass overhead. B: The UGA SSA sensor probe integrates three Watermark sensors and can be customized to any length.

    By Vasileios Liakos

    One of the goals of the University of Georgia College of Agricultural and Environmental Sciences (UGA CAES) is to develop new irrigation methods and tools for crops. Researchers, including myself, Erick Smith, George Vellidis and Wes Porter, have been developing smart irrigation scheduling tools for blueberry growers in Georgia since 2015. Smart irrigation is a new method of irrigation that uses technology and information to make more accurate and faster decisions.

    UGA has developed two smart irrigation tools for blueberries — the UGA Smart Sensor Array (SSA) and the Blueberry App.

    SYSTEM RECORDS SOIL MOISTURE

    The UGA SSA is a system that records soil moisture within fields. It consists of a monitoring system, a commercial server that receives soil moisture data wirelessly, and a website that presents soil moisture data and recommends irrigation rates. The monitoring system consists of smart sensor nodes and a gateway. Each node has a circuit board, a radio frequency transmitter, soil moisture sensors, thermocouple wires and an antenna (Figure 1a). Each node accommodates two thermocouples for measuring temperature and a probe that consists of up to three Watermark® soil moisture sensors (Figure 1b).

    “Soil moisture sensors record soil water tension, and we realized very soon that farmers could not make irrigation decisions based on the sensor readings. It was necessary to convert sensor readings into amount of irrigation,” said UGA precision agriculture specialist George Vellidis.

    To overcome this problem, we utilized soil properties and a model to convert soil water tension numbers into inches of irrigation that is needed to saturate the soil profile. Additionally, farmers can see in real time their soil moisture data to make irrigation decisions for each location in fields using a web-based interface that was developed by UGA.

    IRRIGATION SCHEDULING APP
    Figure 2. Left: The main screen of the Blueberry App tells growers how many hours they need to run their irrigation systems and how many gallons they are going to use. It also allows them to check accumulated rainfall from the past seven days and the expected crop evapotranspiration for the next seven days. Right: Blueberry growers do not have to check the app daily since it notifies users if there is rain at the field and how much irrigation they need to apply.

    Blueberry growers can also use the Blueberry App on their smartphones to schedule irrigation (Figure 2). The app runs a model that uses reference evapotranspiration (ETo) data and the Penman-Monteith equation to calculate the irrigation needs of blueberries.

    The innovation of the Blueberry App is that it is programmed to receive forecasted ETo data for the next seven days for every location in the United States from the Forecast Reference Evapotranspiration service of the National Oceanic and Atmospheric Administration. Precipitation data are received from the Georgia Automated Environmental Monitoring Network and the Florida Automated Weather Network (FAWN).

    UGA has developed a crop coefficient curve that shows the water needs of blueberries in Georgia every year. The goal is to include more coefficient curves from other states. This will be capable if more blueberry growers use the app.

    By knowing the total ETo for the next seven days and the crop coefficient values of the blueberries, the crop evapotranspiration of blueberries can be calculated, and irrigation events adjusted accordingly.

    EVALUATION OF SOIL MOISTURE SENSORS

    Another interesting project, involving soil moisture sensors and blueberries, began a few months ago. The objectives of the project are to 1) compare different commercially available soil moisture sensors in blueberry soil, 2) determine the accuracy of each type of soil moisture sensor in blueberries and 3) determine which soil moisture sensor type is best for use in blueberries.

    Figure 3. Field trials are testing four different soil sensor types in blueberry fields.

    The soil moisture sensors used in this project are Watermarks, Irrometer tensiometers, Aquachecks and Decagons (ECHO EC-5). The selection of these sensors was made based on their popularity in the United States. Table 1 shows advantages and disadvantages of different types of soil moisture sensors.

    This study takes place at a UGA blueberry farm in Alapaha and at two commercial blueberry farms in Alma and Manor. At each site, the four different soil moisture sensor types have been installed close to each other along the beds to collect data to meet the objectives of the project (Figure 3).

    Source: Practical use of soil moisture sensors and their data for irrigation scheduling by R. Troy Peters, Kefyalew G. Desta and Leigh Nelson, 2013, Washington State University.

  • Bioengineered Sentinel Plants Could Help Protect Future Crops

    Illustration by Snow Conrad

    By Jenelle Patterson

    As a plant molecular biologist, I often hear tales of gardening mishaps or plant-related tidbits from my friends and family.

    A few years ago, a friend excitedly relayed her experience at a Niagara wine tour, where the tour guide explained that they grow rose bushes at the end of each row not only for aesthetics, but as early warning systems for pests and diseases (such as powdery mildew). This piqued my curiosity, and I discovered that using plants as biosensors or sentinels is not a modern concept. Roses have been used this way for centuries.

    However, the use of roses as sentinels has disadvantages:

    1) Some pests or pathogens that target grapes do not affect roses.

    2) By the time a rose shows signs of a fungal infection, it may be too late to protect the grapes.

    Most modern vineyards employ more sophisticated integrated pest management strategies (e.g., forecasting disease outlooks using weather reports and tracking confirmed cases). But, this canary-in-a-coal-mine approach of using plants as warning systems still may prove useful, especially where other types of testing are unavailable or costly.

    ADDRESSING SENSITIVITY AND SPECIFICITY

    A diverse array of agricultural and human health hazards (pathogens, heavy metals, herbicide residues, radioactivity, even explosives, to name a few) could conceivably be detected using sentinel plants. But we first need to address the reasons why roses fall short: sensitivity and specificity.

    The first attempts to bioengineer sentinel plants began in the late 80s with the discovery and development of reporter genes (think of them as biological red flags). By the 90s, engineered plants were designed that could detect genotoxins (chemicals or UV-C light that cause DNA mutations). Prolonged exposure to genotoxins causes mutations in the plants, which were measured by staining the plant tissue with a chemical that turns cells blue if the reporter gene is mutated.

    These plants could detect heavy metals, herbicides and radioactivity just as effectively as conventional methods that use animals or chemical analysis. And these sentinel plants were cheaper, required less maintenance and avoided the ethical concerns of using animals. Despite being a big step in the right direction, prototypes had the same issues as their natural rose counterparts: The tests took weeks to months of exposure (sensitivity) and could not be used to identify the genotoxin, only to indicate that one was present (specificity).

    Bioengineering has made huge progress in the past decades as scientists develop new technologies and a better understanding of how plants naturally detect and react to changes in their environment. Plant researchers are beginning to rethink biology in terms of computer programming, adopting concepts like modular system design and logic gates.

    Simply put, biological components are being treated like modular parts used to build an input/output system. Plants and all living organisms use this kind of system already. A signal is detected (e.g., getting chewed on by a bug), that information is relayed in a game of telephone by enzymes and signal molecules, generally ending in the nucleus (the control center of the cell), which flips a genetic switch to bring about some response. The power of bioengineering is the ability to design a tunable system, one that is sensitive to a specific input.

    In 2011, researchers tested this principle by building a TNT-detector plant. They integrated genetic parts from bacteria into a plant’s natural stress-response pathway. Their creation could sniff out and signal the presence of soil- or airborne TNT molecules (input) by causing leaf de-greening (output), and at equivalent levels to bomb-sniffing dogs. This landmark study was just the beginning. It demonstrated the potential for bioengineered sentinel plants to address the sensitivity and specificity limitations of their natural predecessors.

    RESEARCH ADVANCEMENTS

    Researchers have since been identifying and redesigning biological parts to build complex logic gates. Early bioengineered systems were only capable of binary on/off states. Building more complex systems by adding modular components that work together gives the plant the ability to make more nuanced ‘decisions’ (like computer logic gates: react to this and/or this, but not to that). This can reduce false positives and allow for more sensitive detection. Different reporter outputs can also be used to reduce testing times and avoid destroying the sentinel plant, such as engineering plants to produce fluorescent proteins in their leaves, which is easily visible under a UV lamp.

    Researchers still have more work to do to make sentinel plants reliable enough for routine use in agriculture. But it’s not hard to foresee a future where genetically trained roses are a critical part of modern integrated pest management, and not just a nice story to tell guests on a wine tour.

  • Technology to Improve Vegetable Production

    Figure 1. Initial design of the low-cost robotic sprayer for precision weed control in vegetable production: main components of the smart sprayer (A) and self-reconfigured and self-adjustable design for easy field deployment in a variety of vegetable fields (B).

    By Yiannis Ampatzidis

    Vegetable growers face a variety of challenges, including pest and diseases, labor shortages and climate change. How can new advancements in technology help growers address these challenges? Can technology improve crops, reduce production costs and protect the environment? How can technological innovations be incorporated into traditional farming to improve production practices?

    In the last few decades, several “smart” technologies have been developed for vegetable production and processing. However, growers are confronted with a variety of challenges when considering adopting new technology or adjusting existing technology. Growers are being offered solutions that might not work in their specific production system or might not be economically feasible. This article presents examples of state-of-the-art technologies that may be used in vegetable production today or in the near future!

    SIMPLIFY SURVEYING

    Field surveys for disease/pest scouting and to assess plant stress are expensive, labor intensive and time consuming. Since labor shortage is a major issue in vegetable production, small unmanned aerial vehicles (UAVs) equipped with various sensors (remote sensing) can simplify surveying procedures, reduce the labor cost, decrease data collection time and produce critical and practical information.

    For example, recently UAVs and remote sensing have allowed growers to constantly monitor crop health status, estimate plant water needs and even detect diseases. The precision agriculture team (@PrecAgSWFREC) at the University of Florida Institute of Food and Agricultural Sciences (UF/IFAS) Southwest Florida Research and Education Center (SWFREC) developed a cloud-based application called Agroview (http://agroview.farm/login.php).

    Agroview can process, analyze and visualize data collected from UAVs and other aerial platforms (e.g., small planes and satellites). This technology utilizes machine learning (an application of artificial intelligence) to detect single plants and assess plant size and stress. Agroview and UAVs were initially used to create plant inventories in citrus (see a video demonstration at https://twitter.com/i/status/1202671242647490560) and to detect specific diseases in vegetables with high accuracy. Early detection and eradication of infected plants is crucial to controlling disease and pest spread throughout the field.

    SMART SPRAYERS

    Most conventional sprayers apply agrochemicals uniformly, even though distribution of pests and diseases is typically patchy, resulting in waste of valuable compounds, increased costs, crop damage risk, pest resistance to chemicals, environmental pollution and contamination of products. Contamination can be related to run-off after application, discharge from drainage and off-target deposition of spray due to wind (spray drift). This contamination can be significantly reduced through optimization of spraying technology.

    Spray drift of agrochemicals occurs during every application and accounts for a loss of up to 50 percent of the agrochemical used. Minimizing the negative impacts of agrochemicals (and spraying technologies) is a major global challenge.

    More than 90 percent of the acreage of crops in the United States are being sprayed with herbicides. It is estimated that $26 billion is spent on herbicides (more than 3 billion pounds) each year. This overuse of chemicals creates herbicide-tolerant weeds and approximately 250 known species of resistant weeds.

    In recent decades, several smart technologies have been developed for pest detection and for optimizing spraying applications. These new spraying technologies have shown an important improvement in efficiency and safety by adopting the latest advances in electronics, artificial intelligence (AI) and automation.

    One example is the See & Spray machine developed by Blue River Technology (www.bluerivertechnology.com) for weed control in arable crops. See & Spray utilizes computer vision and AI to detect and identify individual plants (such as cotton) and weeds and then applies herbicide only to the weeds. See how this technology works at https://youtu.be/gszOT6NQbF8. This machine can reduce the required quantity of herbicide by more than 90 percent compared to traditional broadcast sprayers. However, this technology was designed for arable crops and might not be a cost-effective solution for specific vegetable production systems.

    Another low-cost smart sprayer has been designed and developed by the UF/IFAS team for precision weed management in vegetables. In the initial evaluation experiments, smart technology was able to accurately detect and distinguish weeds from crops and apply chemicals only on specific weed(s), thus avoiding crops and areas without weeds. See a video demonstration of this technology at https://twitter.com/i/status/1045013127593644032.

    Recently, the precision ag team, in collaboration with Abhisesh Silwal (Carnegie Mellon University) and Panos Pardalos (UF), received funding from the U.S. Department of Agriculture and the National Research Foundation (award #2020-67021-30761) to improve and fully automate this smart sprayer. This novel robotic sprayer (or fleet of sprayers) was designed to be self-reconfigured and self-adjustable for easy field deployment (Figure 1). With this design, the robot can reconfigure itself (Figure 1b) to manage weeds in a variety of vegetable fields (e.g., with different row spacing and raised bed sizes).

    ROBOTIC HARVESTING
    Figure 2. Harvest Croo Robotics harvester for strawberries

    Fresh-market vegetables are quickly perishable and virtually 100 percent are hand-harvested. Vegetable growers face increasing shortages of laborers, which in turn, drive up harvest costs. Mechanical and robotic harvesting systems for vegetable growers could simultaneously decrease their dependence on manual labor, reduce harvesting costs and improve overall competitiveness in the market.

    In one example, Harvest Croo Robotics, a Florida company, is developing a robotic harvester for strawberries that does not require growers to radically change the way they currently grow crops. This technology successfully harvested berries during the 2019–20 season. It could address the labor shortage problem and increase grower profit. 

  • Meeting meat demand with plant proteins

    By Jaya Joshi

    As the world population keeps growing, so does the pressure to feed everyone without increasing carbon footprints. By 2050, the world population is predicted to increase to 9 billion people, and the demand for meat is expected to rise by 73 percent. Meeting this demand would require an additional 160 million tons of meat per year. 

    Are we ready to keep up with the demand? No! For more meat, we need more land, water and processing plants. This would result in less land for crop production, more methane production from livestock, more demand for animal feed and more health concerns. Augmenting western diets with sustainable protein sources from plants is a good option to reduce meat demand and mitigate climate change.

    There is rising interest in plant-based protein among consumers who want to eat less meat and dairy and more vegetables and fruits without compromising their protein intake. The global plant-based protein market is growing rapidly. With a projected compound annual growth rate of 15 percent for 2020–2024, this industry is expected to be worth $24 billion by 2024.

    Most plant proteins come from fat- and protein-rich seeds of legumes such as soybeans, peas and many other bean varieties.

    SOY, PEA AND POTATO

    Soy protein has captured a large share of the global plant-based protein market in a very short time. Soy protein is on a par with animal protein in terms of quality and may have health benefits that include reducing cholesterol levels and improving bone mineral density. Examples of successful soy protein meat-substitute products are the burgers and meatballs from ImpossibleTM Foods, now widely available.

    Although soy is the best-known meat alternative, peas do everything that soy, wheat and corn don’t. Pea is considered the most sustainable source of plant protein, and its texturing properties and high digestibility make it a popular additive in the “mock-meat” industry. Beyond Meat® is an example of pea-based protein.

    Pea protein has many other applications but is perhaps gaining greatest acceptance as a dairy replacement. One such example is Ripple® milk, which is a good source of essential amino acids and calcium. In 2018, DuPont Nutrition & Health released TRUPRO TM 2000 pea protein for beverages, made from North American yellow peas.

    Potato is another vegetable that has potential in the plant-based protein market. The contents of essential amino acids such as methionine and lysine are lower in most plant proteins than in animal proteins, but potato protein is an exception. The essential amino acid content of potato protein is comparable to both the milk protein casein and egg protein.

    OTHER OPTIONS

    Soy, pea and potato proteins are just the beginning. There are many other animal-protein alternatives breaking into the market, including peanuts, lupin beans, jackfruit and oilseeds, as well as non-plant sources like mushrooms and algae.

    As it ranks second in the United States for vegetable and fruit production, Florida has an edge in growing for the plant-based protein market. Florida farmers already grow potatoes, snap beans and peanuts. Given the fact that Florida is one of the only states that can grow produce year-around, plant-based protein farming could have a bright future in Florida.

    DEMAND AND INNOVATION

    Despite the recent surge in demand, plant-based proteins are still a niche market rather than a staple in American diets due to their perceived lack of variety, quality, taste and texture. But this is changing fast. A burst of innovation is emerging, bolstered by collaboration between agriculture and the exciting new field of synthetic biology.

    Synthetic biology seeks to build upon and reimagine nature’s designs. Scientists are beginning to understand the factors behind meat products’ textures and flavors, and synthetic biologists can incorporate these qualities when designing new protein products. Plant-protein products are now available that are a match for their animal protein counterparts. For example, the famous plant-based ImpossibleTM burger contains engineered heme, a protein originally derived from soy plant roots that gives the burger its meat-like flavor, color and texture.

    There is even more to learn from plants: Recently, the U.S. Department of Agriculture launched an initiative called MP3 (“more proteins, more peas, more profits”) to understand the genetics of pea protein’s digestibility and desirable texture. In the future, combining convent­ional breeding with genome editing and synthetic biology could further strengthen the ability to produce high-quality, appealing plant proteins economically and sustainably.

    The coming years are likely to see a substantial market shift toward plant protein-based meat and dairy substitutes. The plant-based protein initiative has gained the attention of major players such as the World Wide Fund for Nature, the Global Alliance for Improved Nutrition and the confectionery manufacturer Hershey, who have started the Protein Challenge 2040 project. The goals of the project are to promote plant-based protein consumption, scale up sustainable feed and reduce protein waste. Future partnerships between growers, scientists, govern­ments, and the private sector have the potential to ramp up plant-protein production to address the surging demand of proteins in an eco-friendly way.