The pursuit of higher yields is one that continues to entertain farmers, industry specialists and scientists alike. In the mid 20th century, the Green Revolution allowed yields to explode. Genetic engineering built higher yielding varieties, an array of chemical agents were developed to reduce pests and promote crop growth, while new machinery made the whole process easier [1]. In the second half of the 20th century, yields worldwide tripled despite the area of cultivated land only increasing 30% [2]. Particularly in Australia, the green revolution led to a near 2.5X increase in production, from 1.84 billion tonnes in the early 1960s to 4.38 billion tonnes in 2007 [3]. Astonishingly, this was achieved with only 11% more cropped land.
With arable land already being maximized worldwide, and land degradation becoming an ever-increasing issue [4], future improvements in yield will have to come solely from increasing outputs from each hectare of land. Additionally, keeping soil health high will also need to be prioritized.
With all the more recent problems plaguing agriculture these days: water shortages[5], resistances of weeds and pests to pesticides [6] and land erosion [7], a different strategy will need to be considered to achieve the next level in yield optimization. Despite genetic and chemical advances in the agricultural science field are still being made every day, there’s a new player to consider and it’s becoming available to farmers in Australia: drones.
Instead of focusing on new treatments to improve plant or soil’s performance, drones will play a different role in yield optimization. They focus on customization and precision. Drones, like the ones used by Drone Commander Australia, allow farmers to customize the treatment of their field, and they do so in a highly precise way.
Here we’ll talk about two of the main uses of drones in agriculture today: precise application and monitoring.
Precise Application
Today, conventional agricultural treatments such as fertilizers and various pesticides can be applied using drones, or in this context also called remotely piloted aerial application systems (RPAAS) [8]. This is one of the main services we provide at Drone Commander Australia. These systems are quickly becoming accepted as the most precise and effective way to disperse granular and liquid treatments of all sorts. Additionally, when considering liquid treatments, the turbulence created by a drone’s rotors help the droplets penetrate the crop’s canopy, thus providing a good coverage of leaves closer to the ground [8]. When it comes to the elimination of pests, be it weeds, insects, larvae, rodents or fungi, applying with precision can mean the difference between maintaining the crop and suffering a loss.
For example, an American study in 2020 found that RPAAS disperse more herbicide droplets onto weeds’ leafy regions than backpack sprayers [8]. In this study the drone was programed to release only 27% of the amount of herbicide used by backpack sprayer. However, the drone was able to target and cover 4 times the amount of weed leaves with the spray and thus was effective at eliminating more of them. Additionally, the drone completed the task approximately 3 times faster than the human.
Other treatments, such as baits for pests, can also be more effectively dispersed with the use of drones than by helicopter, and not surprisingly, significantly faster than application by hand. Two of the most destructive pests in Eastern Australia, the Fall armyworm and mice, are very effectively treated by drone – some of Drone Commander Australia’s biggest services.
Several species of armyworms are widespread in Australia, and when not dealt with quickly enough can result in over 20% yield losses in a short period of time [9]. Fall armyworm larvae are a new pest of concern in Australia and are known to feed on over 350 plant species, but prefer cereals and forage grasses [6]. Originally from the Americas, these larvae have caused significant losses in corn overseas in areas such as Florida (20%) and Argentina (up to 72%) [10]. The ability of the adult moths to travel far distances by flying several hundred meters off the grown cause them to be swept up in wind gusts, allowing them to travel several hundred kilometers at a time. In 2018, fall armyworms reached Africa and are reported to have caused 20-50% yield losses in corn in select regions [11]. Since early 2020, this species has been detected in the eastern mainland states and are a risk to major cereal crops [9]. Early detected infestations can be treated with insecticide to protect against young larvae [12], while setting pheromone or poison traps are common practise for extinguishing adult populations [13], [14]. Drone Commander Australia have collaborated with a number of agricultural bio-technology companies to develop capabilities in the extermination of the fall armyworm. In our collaboration with AgBiTech, Drone Commander have developed a system to distribute AgBiTech’s Magnet insect attractant using a drone which can carry payloads of 10 litres and operate at 25km an hour.
For the distribution of baits, drones are a much better alternative to helicopters since they precisely drop their bait loads- targeting the invasive species of interest and reduce the risk of accidental poisoning of nearby non-targeted species. This practise is currently being used by governmental conservation agencies in the Galapagos Islands and New Zealand for large areas [15]. In addition to being more precise and effective than helicopters for pest eradication, the New Zealand Department of Conservation reports that they are also much cheaper, and produce 85% less carbon emissions than helicopters [16].
One of the newer uses of drones in agriculture is for seeding. Using conventional machinery can lead to uneven emergence and 5-10% yield losses in grain crops [17] and up to 21% in other crops like sugar beets, which are highly sensitive to inaccurate seed [18]. Various sources have identified that drones, like those used by Drone Commander, can achieve uptake rates of 75%, while other pod seeding methods (seed and nutrients) reducing planting costs 85% [19].
Monitoring
The most common types of drones used by agricultural scientists, but not yet widespread and accepted by farmers, are outfitted with remote sensing technologies that identify areas of poor plant or soil health within a field [20]. These drones are fitted with infrared, multispectral or hyperspectral sensors which allow them to accurately differentiate between good and poor performing areas of a field.
Despite rows of crops looking quite identical, plant health and soil fertility and moisture differ from plant to plant and soil region to region. Applying the same uniform treatment everywhere may not be enough to reach maximum yield in every section of the field. Using remote sensing technologies, drones can identify the least productive regions of a field and target those areas with the particular attention necessary; be it fertilizer, water or pesticides.
The Normalized Difference Vegetation Index (NDVI) is one of the most useful tools for determining the health and degree of development of a plant [21]. Captured with multispectral cameras, this index can be calculated by drones to measure differences in the density of green areas. In short, the green pigments in the leaves absorb visible light rays and reflect near-infrared rays. A healthy mature plant, that has more leaves, will therefore reflect more rays than a less healthy plant. Drone cameras pick up these differences and then converts these findings into a simple colour scheme which the drone operator can interpret. One study suggests NDVI measurements are highly correlated with the yields of various crops [22]. Another index, the Canopy Chlorophyl Content Index (CCCI), is used to measure canopy Nitrogen levels [23] thus identifies areas where more fertilizer should be applied to maximize yield.
Indexes like the Crop Water Stress Index (CWSI), measured with multispectral sensors, can be calculated by drones to identify field locations where water stress is higher. State of the art research coming out of Monash University, led by Professor Jeff Walker, aims to develop a drone which use microwave technology to assess soil moisture and thus can identify locations where irrigation should be done [24]. Their current project involves developing a prototype equipped with P-Band technology that is expected to be able to measure the moisture in the top 15cm of soil, unimpeded by vegetation.
To date, most of the work with drones outfitted with these remote sensing technologies is still preliminary and many are still only being used for research purposes by scientists. These types of sensors have been used to monitor crops and soils for over a decade and pictures were historically captured with satellites or planes [25]. These methods however lack resolution due to the distance the images are taken from. Since the images drones capture are taken from much closer to the ground, the spatial and spectral resolution is much better than images taken from other aerial sensor platforms. Drones can thus allow farmers to know the health of each individual plant. More work is still needed for these technologies to recognize less common crops and different planting patterns. As of now, few companies offer these services but over the next few years, as improvements are made and drones gain acceptance with farmers this will likely change.
Case study: Drones for crop monitoring
Although controlled studies by scientists examining yields improvements with drones are limited at this point, results from a select few farmers in various industries speak for themselves.
In Spain, a vineyard’s wine production increased nearly 17% with the use of a drone that could detect which grape vines were ripe and which required more growing time or treatment [22]. This involved the winemaker commissioning a local drone service provider to survey the land back in 2015 with a drone fitted with multispectral cameras. Upon landing, software was used to develop a series of maps of the property. One of these maps, depicting the Normalized Difference Vegetation Index (NDVI) of the grape vines, visually output the degree of development of the observed plants by measuring differences in the density of green areas. The drone finally converted the information collected from the entire field into a simple colour system of lowest (red) to highest (green) quality, which allowed the winemaker to know the problematic regions in his field and to prepare individual treatments for these areas. Two months later the drone provider returned to check on the status of the field to find all of the orange and red zones were now yellow and green, showing that the health of the plants had been improved thanks to individualized treatments. The winemaker calculated that this method increased his yearly wine production from 65000 to 76000 bottles.
One Canadian operator that manages 14k hectares of peas, lentils, canola, wheat, barley, and soy was also able to use NDVI maps to save 17% of his pea crop after the images detected a low health region which he later self-identified as an aphid issue [26].
Additional Benefit: Reducing Soil Compaction Saves Yields
As heavy machinery is not required, one additional simple benefit that drones provide is the reduction in soil compaction. Surface soil is easily compacted by machinery and, depending on soil characteristics, subsurface levels can also be affected. Subsurface level compactions compress soil aggregates together which reduces poor sizes, restricts root growth and can ultimately result in smaller crops and yields. Machinery is actually the most common culprit of subsoil compaction is various regions in Australia and is estimated to have cost regions like Western Australia $330 million in lost yields over the past few decades [27]. A British independent soil advisor, who has been studying the effects of soil compaction for a number of years, recently calculated that soil compaction in the wheeling strips generally leads to 15-25% lower yields in these areas and can result in greater than 5% reductions in total [28]. Drones, like the ones used by Drone Commander Australia, are resultantly less invasive and cause no added risk of subsoil compaction, as they do not make contact with the crops or soil.
Limitations and What’s Next?
One fascinating area of research to watch out for in the coming years will be the use of artificial intelligence (AI) and machine learning in drones to detect pests.
For example, as weed resistance to herbicides is predicted to increase, the agricultural scientists identify that the future of weed management will have to include precision. One researcher in the field explains this as “placing the right amount of inputs on the right target [weeds] at the right time”[29]. Drones will certainly be the primary way this AI will be used. New technologies are being developed to incorporate plant recognition AI into integrated weed management [29]. Instead of universal spraying technique, these technologies will allow drones to identify weed species and apply the most effective herbicide to each individually. It is not surprising that AI is also being used to identify fungal, viral and bacterial crop pathogens, insects and rodents [30].
There are still many concerns from farmers about the accuracy of drone monitoring data. Since these technologies are adapting rapidly and many are still in early stages of research, the number of available services to farmers are indeed still limited. Thankfully, in Australia, the barriers to the widespread adoption of drones are lower than in many countries where the use of drones in agriculture still face harsh legal restrictions, or are prohibited entirely [31]. Precision aerial application, like those offered by Drone Commander Australia, currently offer the best treatments on the market. However, the possibilities of drones in agriculture seem endless and are likely to play an ever-increasing role in yield maximization as the technology develops and as information becomes more accessible to farmers.
[1] A. Briney, “Green Revolution History and Overview.” Available: https://www.thoughtco.com/green-revolution-overview-1434948. [Accessed: 17-Feb-2021].
[2] M. Wik, P. Pingali, and S. Broca, “Background Paper for the World Development Report 2008: Global Agricultural Performance: Past Trends and Future Prospects,” Washington, DC, 2008.
[3] P. Langridge, “Agriculture in Australia: growing more than our farming future.” Available: https://theconversation.com/agriculture-in-australia-growing-more-than-our-farming-future-22843.
[4] “Global Assessment of Human-induced Soil Degradation (GLASOD) | Land & Water | Food and Agriculture Organization of the United Nations | Land & Water | Food and Agriculture Organization of the United Nations.” Available: http://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1036321/.
[5] S. Kelly, R. Cunningham, P. R., and M. K., “Water Scarcity Risk for Australian Farms and the Implications for the Financial Sector,” 2019.
[6] D. of A. Australian Government, “National Priority Plant Pests (2019) ,” 2019. Available: https://www.agriculture.gov.au/pests-diseases-weeds/plant/national-priority-plant-pests-2019.
[7] Australia State of the Environment, “Soil: Formation and erosion | Australia State of the Environment Report,” 2016. Available: https://soe.environment.gov.au/theme/land/topic/2016/soil-formation-and-erosion.
[8] D. Martin, V. Singh, M. A. Latheef, and M. Bagavathiannan, “Spray Deposition on Weeds (Palmer Amaranth and Morningglory) from a Remotely Piloted Aerial Application System and Backpack Sprayer,” Drones, vol. 4, no. 59, 2020.
[9] Agriculture Victoria, “Armyworms,” 2021. Available: https://agriculture.vic.gov.au/biosecurity/pest-insects-and-mites/priority-pest-insects-and-mites/armyworms.
[10] R. Early, P. González-Moreno, S. T. Murphy, and R. Day, “Forecasting the global extent of invasion of the cereal pest Spodoptera frugiperda, the fall armyworm,” NeoBiota, vol. 40, pp. 20–50, 2018.
[11] PreventionWeb, “South East Asia and Australia face fall armyworm threat.” Available: https://www.preventionweb.net/news/view/60106.
[12] R. Bessin, “Fall armyworm in corn,” 2003.
[13] R. Njeru, “Report on Stakeholders Consultation Meeting on: Fall Armyworm in Africa: Status and Strategy for Effective Management,” 2017.
[14] M. Lunagariya et al., “Efficacy of poison baits against fall armyworm, Spodoptera frugiperda (J.E. Smith) infesting maize,” J. Entomol. Zool. Stud., vol. 8, no. 4, pp. 2251–2256, 2020.
[15] Defenseworld.net, “No Title.” Available: https://www.defenseworld.net/news/24153/Drones_drop_Poison_to_Eradicate_Gal__pagos_Rats.
[16] The Guardian, “Poison-laden drones to patrol New Zealand wilderness on the hunt for invasive pests.” .
[17] OMFRA, “No Title.” Available: http://www.omafra.gov.on.ca/english/.
[18] V. V. Vasilenko, S. V. Vasilenko, and N. N. Achkasova, “Impact of Precision Seeding on Yield Of Sugar Beet,” 2018, pp. 776–778.
[19] “Six Ways Drones Are Revolutionizing Agriculture | MIT Technology Review.” Available: https://www.technologyreview.com/2016/07/20/158748/six-ways-drones-are-revolutionizing-agriculture/.
[20] D. van der Merwe, D. R. Burchfield, T. D. Witt, K. P. Price, and A. Sharda, “Drones in agriculture,” in Advances in Agronomy, vol. 162, Academic Press Inc., 2020, pp. 1–30.
[21] “Measuring Vegetation (NDVI & EVI).” Available: https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_2.php.
[22] J. Huang, X. Wang, X. Li, H. Tian, and Z. Pan, “Remotely sensed rice yield prediction using multi- temporal NDVI data derived from NOAA’s-AVHRR.,” PLoS One, vol. 8, no. 8, p. e70816, 2017.
[23] G. Fitzgerald, D. Rodriguez, and G. O’Leary, “Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index-The canopy chlorophyll content index (CCCI),” F. Crop. Res., vol. 116, no. 3, pp. 318–324, Apr. 2010.
[24] “Monash plants seeds to improve farming practices - Engineering.” Available: https://www.monash.edu/engineering/about-us/news-events/latest-news/articles/2019/monash-plants-seeds-to-improve-farming-practices.
[25] Future Farming, “No Title.” Available: https://www.futurefarming.com/Tools-data/Articles/2019/5/Autonomous-drone-improves-irrigation-432559E/.
[26] L. Burwood-Taylor, “The Next Generation of Drone Technologies For Agriculture - AgFunderNews.” Available: https://agfundernews.com/the-next-generation-of-drone-technologies-for-agriculture.html.
[27] Department of Primary Industries and Regional Development: Agriculture and Food, “Soil compaction overview ,” 2018. Available: https://www.agric.wa.gov.au/soil-compaction/soil-compaction-overview.
[28] “Counting the cost of soil compaction - FutureFarming.”. Available: https://www.futurefarming.com/Smart-farmers/Articles/2020/9/Counting-the-cost-of-soil-compaction-639059E/.
[29] S. L. Young, G. Meyer, and W. Woldt, “Future Directions for Automated Weed Management in Precision Agriculture,” 2014.
[30] D. T. Allen, “Farmers are using AI to spot pests and catch diseases — and many believe it’s the future of agriculture.”
[31] Food and Agriculture Organization of the United Nations, “E-AGRICULTURE IN ACTION: DRONES FOR AGRICULTURE,” Bangkok, Thailand, 2018.
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