As is the case with many other types of new technology, agriculture is helping to lead the way when it comes to using drones and artificial intelligence in row crops, pasture and specialty crop systems.<\/p>\n
Farmers are quickly realizing the potential time and cost savings that drones and AI can offer over traditional scouting and ground equipment. From acquiring crop, pest and soil information to conducting field operations such as pesticide applications and cover crop seedings, technology is proving its mettle on the farm.<\/p>\n
\u201cAs drone sensor technology, artificial intelligence capabilities, and payload capabilities evolve, the use of these technologies in agriculture will increase,\u201d said Allen Torbert, research leader at the USDA-ARS National Soil Dynamics Laboratory on Auburn University\u2019s campus.<\/p>\n
In recent months, NSDL researchers have focused on utilizing drones in agricultural operations. This research is a collaborative effort with the Auburn University College of Agriculture\u2019s Crop, Soil and Environmental Sciences Department through a cooperative agreement with Steven Li, associate professor and Alabama Extension specialist, as the principal investigator for the research.<\/p>\n
The research involves several other departments at Auburn University, including the Biosystems Engineering Department.<\/p>\n
\u201cThis research focuses on efforts that will improve both liquid and dry material delivery, as there is a growing industry within Alabama utilizing drones to support agricultural practices in the state,\u201d Torbert said. \u201cThe research aims to develop improved methods to utilize drones and provide practical solutions to drone operators, as well as improve effectiveness and efficiency for farmers.\u201d<\/p>\n
In addition, the work focuses on methods to utilize sensors on drones to provide information about a producer\u2019s field, such as drought and vegetative indices.<\/p>\n
\u201cThe resulting analysis of the collected information can be used to help producers with management decisions,\u201d Torbert said. \u201cMost recently, a drone cover crop seeding field day was undertaken with Simer Virk\u2019s Precision Ag Team in Biosystems Engineering in December 2025. During the field day, various application rates, swaths and application heights were tested for three commercially available drone platforms.\u201d<\/p>\n
\u201cThe resulting data is currently being analyzed so that ideal operational parameters can be recommended to producers. All these efforts directly correlate to the research initiatives of helping understand drones\u2019 impact on agriculture in Alabama.\u201d<\/p>\n
Another part of the research effort is aimed at improving the drone technology itself, both in the physical design of the drone and the software that controls it. This also includes improving sensor technology for better interpretation and utilization of drone-sensor collected data.<\/p>\n
\u201cFor this portion of the research, we are cooperating closely with engineers from the University of Texas at Tyler\u2019s Department of Electrical and Computer Engineering, Center for Robotics and Intelligent Systems directed by Dr. Prabha Sundaravadivel,\u201d Torbert said. \u201cThis department focuses on the development of drone technology, and we are collaborating with them on projects to develop specific solutions for problems associated with using drones for agricultural purposes.\u201d<\/p>\n
Examples of some of this work include:<\/p>\n
Voice-controlled autonomous drone navigation.
\n3D estimation of cotton bolls from drone data.
\nImproved methods for autonomous analysis of drone-sensor data.
\nImproved real-time plant segmentation from drone imagery.<\/p>\n
Some of these current concepts are being processed for patent applications.\u00a0Other institutions involved in this effort include the USDA-ARS Laboratory in Poplarville, Mississippi, and Stoneville, Mississippi, the University of Texas at Dallas and Mississippi State University.\u00a0<\/p>\n
The advantages of using drones in agricultural practices are numerous, Torbert said.<\/p>\n
\u201cThe main advantage is that drone operations can access areas that would be difficult to access by traditional means,\u201d he said. \u201cOne example is excess moisture in farmland areas. Field conditions could be too wet to support a vehicle, or the crop could be damaged by the movement of the vehicle across the area.\u201d<\/p>\n
Another example, he added, is that forested property is often difficult to move across with vehicles and allows for potential environmental disruption. Drones are not easily impacted by conditions on the ground, and they can be flown so that no damage is done to the land or crops. They may be used to conduct timely applications for the delivery of seed, fertilizer or pesticides when field conditions are too adverse for land-based, ground-driven equipment.\u00a0<\/p>\n
\u201cDrones allow for an aerial or topographical examination of land to help monitor growing crops or to access forest areas that cannot be seen on the ground,\u201d he said.<\/p>\n
\u201cWith the advancement of sensor technologies that can be mounted on drones, landowners can make informed management decisions that had previously not been available to them. This will lead to land management practices that are not only more profitable but also more environmentally sustainable.\u201d<\/p>\n
And it wouldn\u2019t be a discussion of new technology without including the use of AI, Torbert said. The potential to advance drone technology to both reduce costs and improve utilization effectiveness in agriculture has grown tremendously in recent years due to the advancement of AI technology.<\/p>\n
\u201cThe use of AI with drones is one of the best examples of how AI can be used to beneficially advance technology,\u201d he said. \u201cAI programs can be integrated into drone software to utilize sensor information for real-time operational adjustments.<\/p>\n
\u201cExamples include weed identification for precision product applications and stress monitoring for plant health during a growing season. AI programs also can assist in the management of the large quantities of information gathered by drones during operations. In turn, this reduces labor input and operator fatigue during flights.\u201d<\/p>\n
Several field practices will be utilized throughout this research, Torbert said. These include commodity crop seeding; pesticide applications, including insecticides, herbicides and fungicides; fertilizer applications; harvest aid applications; and cover crop seedings.<\/p>\n
\u201cWe\u2019ll be looking at optimal times for making fertilizer, irrigation and pesticide applications,\u201d he said. \u201cGuidelines for Best Management Practices for utilizing drones for these operations also will be included in the research.\u201d<\/p>\n
In addition, monitoring land for pest control such as weed, insect and disease problems also will be a primary function of this research, Torbert said.<\/p>\n
\u201cFor example, a research effort is underway to have drone imagery detect weeds in a cotton field so that herbicides can be applied when weed pressure would be sufficient to warrant control measures.\u201d\u00a0<\/p>\n
Monitoring fields for optimal harvest times also will be an integral part of the work.<\/p>\n
\u201cScheduling harvesting times for flowers for commercial use can be very difficult and very costly for farmers if it is done too early or too late,\u201d he said. \u201cA research effort currently underway will develop technology to recognize flower blooms from drone images so that the whole farm operation can be monitored daily to allow the farmer to schedule the optimal harvest time for the whole orchard.\u201d\u00a0<\/p>\n
Outlying units of the Auburn University\u2019s Alabama Agricultural Experiment Station will be utilized for the research.<\/p>\n","protected":false},"excerpt":{"rendered":"As is the case with many other types of new technology, agriculture is helping to lead the way…\n","protected":false},"author":2,"featured_media":4252,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[24,25,3988,3987,3989],"class_list":{"0":"post-4251","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-ai","8":"tag-ai","9":"tag-artificial-intelligence","10":"tag-auburn","11":"tag-auburn-university","12":"tag-war-eagle"},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/4251","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=4251"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/4251\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/4252"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=4251"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=4251"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=4251"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}