Biological control technology and application based on agricultural unmanned aerial vehicle (<scp>UAV</scp>) intelligent delivery of insect natural enemies (<i>Trichogramma</i>) carrier

Zhan, Yilong; Chen, Shengde; Wang, Guobin; Fu, Jing; Lan, Yubin

doi:10.1002/ps.6371

Cited by 26 publications

(17 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The development of robotic applications for crop management differs with respect to the crop type and cultivation system. One example is the usage of UAVs in the field which are releasing Trichogramma brassicae , a natural enemy against Ostrinia nubilalis (European corn borer), as a biological control in corn plants (Zhan et al 2021 ). In contrast to the manual application of “Trichogramma bags”, UAVs enable a fast and practical application in open land.…”

Section: Digitalization In Agricultural Practice: Are Robots the Bett...mentioning

confidence: 99%

Digital plant pathology: a foundation and guide to modern agriculture

Kuśka¹,

Heim²,

Geedicke³

et al. 2022

J Plant Dis Prot

View full text Add to dashboard Cite

Over the last 20 years, researchers in the field of digital plant pathology have chased the goal to implement sensors, machine learning and new technologies into knowledge-based methods for plant phenotyping and plant protection. However, the application of swiftly developing technologies has posed many challenges. Greenhouse and field applications are complex and differ in their study design requirements. Selecting a sensor type (e.g., thermography or hyperspectral imaging), sensor platform (e.g., rovers, unmanned aerial vehicles, or satellites), and the problem-specific spatial and temporal scale adds to the challenge as all pathosystems are unique and differ in their interactions and symptoms, or lack thereof. Adding host–pathogen–environment interactions across time and space increases the complexity even further. Large data sets are necessary to enable a deeper understanding of these interactions. Therefore, modern machine learning methods are developed to realize the fast data analysis of such complex data sets. This reduces not only human effort but also enables an objective data perusal. Especially deep learning approaches show a high potential to identify probable cohesive parameters during plant-pathogen-environment interactions. Unfortunately, the performance and reliability of developed methods are often doubted by the potential user. Gaining their trust is thus needed for real field applications. Linking biological causes to machine learning features and a clear communication, even for non-experts of such results, is a crucial task that will bridge the gap between theory and praxis of a newly developed application. Therefore, we suggest a global connection of experts and data as the basis for defining a common and goal-oriented research roadmap. Such high interconnectivity will likely increase the chances of swift, successful progress in research and practice. A coordination within international excellence clusters will be useful to reduce redundancy of research while supporting the creation and progress of complementary research. With this review, we would like to discuss past research, achievements, as well as recurring and new challenges. Having such a retrospect available, we will attempt to reveal future challenges and provide a possible direction elevating the next decade of research in digital plant pathology.

show abstract

Section: Digitalization In Agricultural Practice: Are Robots the Bett...mentioning

confidence: 99%

Digital plant pathology: a foundation and guide to modern agriculture

Kuśka¹,

Heim²,

Geedicke³

et al. 2022

J Plant Dis Prot

View full text Add to dashboard Cite

show abstract

“…. , l n(chromosome(i,:)) ) (16) where l i(chromosome(i,:)) denotes the length of the global dispatch route path adjusted according to chromosome (i,:) for the ith row of the path l i in the operation order set obtained by GAACO.…”

Section: The Second Layer Of Gaaco-gamentioning

confidence: 99%

“…Current research on route planning for aerial applications has mainly focused on unmanned aerial vehicles (UAVs) [15]. Zhan et al [16] developed an intelligent delivery system for biological control agents using electric multi-rotor UAVs and achieved 36.75% coverage redundancy. Cabreira et al [17] reviewed studies of UAV-based coverage path planning for differently shaped areas (e.g., rectangles, concave polygons, and convex polygons).…”

Section: Introductionmentioning

confidence: 99%

Route Planning of Helicopters Spraying Operations in Multiple Forest Areas

Fang

Liu

et al. 2021

Forests

View full text Add to dashboard Cite

It is of great value to research the problem of forest pest and disease control. Currently, helicopters play an important role in dealing with this problem. However, the spraying route planning still depends on the pilot’s driving experience, which leads to low efficiency and less accurate coverage. For this reason, this paper attempts to use intelligent algorithms to plan the pesticide spraying route for helicopters. When the helicopter is conducting spraying operations in multiple forest areas, the routes are divided into two parts: pesticide spraying routes for individual forest areas and dispatch routes between multiple forest areas. First, the shorter spraying route with fewer turnarounds for individual forest areas was determined. Then a two-layer intelligent algorithm, a combination of a genetic algorithm (GA) and ant colony optimization algorithm (ACO), was designed to determine the dispatch route between multiple forest areas, which is referred to as GAACO-GA. The performance was evaluated in self-created multiple forest areas and compared with other two-layer intelligent algorithms. The results show that the GAACO-GA algorithm found the shortest dispatch route (5032.75 m), which was 5.60%, 5.45%, 6.54%, and 4.07% shorter than that of GA-GA algorithm, simulated annealing-GA (SA-GA) algorithm, ACO-GA algorithm, and particle swarm optimization-GA (PSO-GA) algorithm, respectively. A spraying experiment with a helicopter was conducted near Pigzui Mountain, Huai’an City, Jiangsu Province, China. It was found that the flight path obtained from the proposed algorithm was 5.43% shorter than that derived from a manual planning method. The dispatch route length was reduced by 16.93%, the number of turnarounds was reduced by 11 times, and the redundant coverage was reduced by 17.87%. Moreover, helicopter fuel consumption and pesticide consumption decreased by 10.56% and 5.43%, respectively. The proposed algorithm can shorten the application route, reduce the number of turnarounds and the cost of spraying operations, and has the potential for use in spraying operations in smart forestry and agriculture.

show abstract

“…For plant protection applications, the UAVs typically take-off and land vertically in the tea garden. The high-speed downward swirling airflow, and water and fertilizer spraying devices are able to turn the tea leaves, allowing for the even spraying of the front and back of the leaves (Lan et al, 2010;Kose and Oktay, 2020;Qin et al, 2021;Zhan et al, 2021;Zhu et al, 2021;Şahin et al, 2022). UAV multi-tea field scheduling route planning is an important component of the precision planting and protection of tea fields.…”

Section: Introductionmentioning

confidence: 99%