Use of UAVs for an efficient capsule distribution and smart path planning for biological pest control

Freitas, Heitor; Faiçal, Bruno S.; Silva, Alef Vinicius Cardoso e; Ueyama, Jó

doi:10.1016/j.compag.2020.105387

Cited by 30 publications

(12 citation statements)

References 16 publications

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“…Furthermore, with a combination of K-agglomerative clustering, a Set-based Particle Swarm Optimization (S-PSO) and an A* algorithm, a path can be efficiently planned predicting and reducing the energy consumption, as well. In [27], three different path planning algorithms have been implemented for biological control. The implemented algorithms are Ant Colony Optimization (ACO), Guided Local Search (GLS) and Lin-Kernighan (LKH).…”

Section: Bio-inspired Algorithmsmentioning

confidence: 99%

Fixed-Wing UAV Energy Efficient 3D Path Planning in Cluttered Environments

Aiello

Valavanis

Rizzo

2022

J Intell Robot Syst

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UAV path planning in 3D cluttered and uncertain environments centers on finding an optimal / sub-optimal collision-free path, considering in parallel geometric, physical and temporal constraints, fox example, obstacles, infrastructure, physical or artificial landmarks, etc. This paper introduces a novel node-based algorithm, called Energy Efficient A* (EEA*), which is based on the A* search algorithm, but overcomes some of its key limitations. The EEA* deals with 3D environments, it is robust converging fast to the solution, it is energy efficient and it is real-time implementable and executable. In addition to the EEA*, a local path planner is also derived to cope with unknown dynamic threats within the working environment. The EEA* and the local path planner are first implemented and evaluated via simulated experiments using a fixed-wing UAV operating in mountain-like 3D environments, and in the presence of unknown dynamic obstacles. This is followed by evaluating a set up where three UAVs are commanded to follow their respective paths in a safe way. The energy efficiency of EEA* is also tested and compared with the conventional A* algorithm.

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Section: Bio-inspired Algorithmsmentioning

confidence: 99%

Fixed-Wing UAV Energy Efficient 3D Path Planning in Cluttered Environments

Aiello

Valavanis

Rizzo

2022

J Intell Robot Syst

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“…However, owing to environmental factors, there is a deviation between the actual landing point and the air drop point, so the actual coverage rate is not 100%. Freitas 28 et al mainly optimized the operation route of UAV according to the coverage of the natural enemy carrier, to achieve maximum coverage with the minimum number of It was assumed that the effective coverage area of parasitic eggs in the capsule after hatching is a uniform circular area with radius r. When the distance between the boundary route and the boundary is less than or equal to ffiffi ffi 2 p r=2 and the release interval distance and distance between routes are less than or equal to√2r, full coverage of the test area can be realized theoretically. 27…”

Section: Field Release Testmentioning

confidence: 99%

“…The coverage rate in this paper is the theoretical rate calculated using the dropping point without considering any deviation caused by airflow; however, the actual coverage effect should be calculated according to the position of Trichogramma carrier in the field. On this basis, to optimize the coverage effect, Freitas et al 28 optimized the route for the UAV to launch the Trichogramma operation, which can effectively improve the boundary coverage rate of the operational area. However, for small area operations, frequent changes in route will lead to a decrease in operational efficiency, and changing course will result in instability in the flight speed, which will affect the uniformity of delivery.…”

Section: Introductionmentioning

confidence: 99%

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

Zhan

Chen

Wang

et al. 2021

Pest Management Science

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BACKGROUND: Agricultural pests and diseases affect grain yield and quality. In addition to the use of chemical agents, the biological control of diseases and insect pests has attracted more and more attention due to an urgent need for environmental protection. However, traditional operational methods do not integrate well with biological control technology. With the advantages of simple operation, low operating cost, high operating efficiency and wide application range, use of multirotor unmanned aerial vehicles (UAVs) in biological control is of great significance.RESULTS: An intelligent delivery system comprising an insect natural enemy (Trichogramma) capsule based on an M45 electric multirotor UAV was built to release Trichogramma ostriniae in corn fields under full autonomous mode. During operation, the M45 UAV can fly accurately in accordance with operational requirements; average deviation between the actual and planned routes was 0.24 m, the average flight speed was 9.18 m s −1 and the average altitude was 5.08 m. The effective coverage rate of natural enemies was 99.33% and the coverage redundancy was 36.75%. After delivery, an investigation into the field control effect showed an average control effect of 83.70%, which can result in effective pest control.CONCLUSION: A natural enemy carrier intelligent delivery system based on an M45 electric multirotor UAV was found to have good flight performance. In addition, natural enemies were evenly distributed, giving even coverage of the target fields.The system had a good control effect.

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“…These efforts would give far-reaching consequences on UAV-WSN-IoT roles in smart farming and precision agriculture system construction ( Saif et al, 2017 ; Almalki et al, 2021 ). In addition, with the development of artificial intelligence, UAV-RSP can take advantage of high throughput to monitor the nutrient level, plant disease, and insect pest automatically ( Freitas et al, 2020 ; Tetila et al, 2020 ; Lpo et al, 2021 ). Therefore, UAV-RSP, coupled with IoT and 5G technology, plays a more and more important role in building the intelligent monitoring network for smart inspection of plant growth status ( Syed et al, 2020 ; Almalki et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Identification of High Nitrogen Use Efficiency Phenotype in Rice (Oryza sativa L.) Through Entire Growth Duration by Unmanned Aerial Vehicle Multispectral Imagery

et al. 2021

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Identification of high Nitrogen Use Efficiency (NUE) phenotypes has been a long-standing challenge in breeding rice and sustainable agriculture to reduce the costs of nitrogen (N) fertilizers. There are two main challenges: (1) high NUE genetic sources are biologically scarce and (2) on the technical side, few easy, non-destructive, and reliable methodologies are available to evaluate plant N variations through the entire growth duration (GD). To overcome the challenges, we captured a unique higher NUE phenotype in rice as a dynamic time-series N variation curve through the entire GD analysis by canopy reflectance data collected by Unmanned Aerial Vehicle Remote Sensing Platform (UAV-RSP) for the first time. LY9348 was a high NUE rice variety with high Nitrogen Uptake Efficiency (NUpE) and high Nitrogen Utilization Efficiency (NUtE) shown in nitrogen dosage field analysis. Its canopy nitrogen content (CNC) was analyzed by the high-throughput UAV-RSP to screen two mixed categories (51 versus 42 varieties) selected from representative higher NUE indica rice collections. Five Vegetation Indices (VIs) were compared, and the Normalized Difference Red Edge Index (NDRE) showed the highest correlation with CNC (r = 0.80). Six key developmental stages of rice varieties were compared from transplantation to maturation, and the high NUE phenotype of LY9348 was shown as a dynamic N accumulation curve, where it was moderately high during the vegetative developmental stages but considerably higher in the reproductive developmental stages with a slower reduction rate. CNC curves of different rice varieties were analyzed to construct two non-linear regression models between N% or N% × leaf area index (LAI) with NDRE separately. Both models could determine the specific phenotype with the coefficient of determination (R2) above 0.61 (Model I) and 0.86 (Model II). Parameters influencing the correlation accuracy between NDRE and N% were found to be better by removing the tillering stage data, separating the short and long GD varieties for the analysis and adding canopy structures, such as LAI, into consideration. The high NUE phenotype of LY9348 could be traced and reidentified across different years, locations, and genetic germplasm groups. Therefore, an effective and reliable high-throughput method was proposed for assisting the selection of the high NUE breeding phenotype.

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Use of UAVs for an efficient capsule distribution and smart path planning for biological pest control

Cited by 30 publications

References 16 publications

Fixed-Wing UAV Energy Efficient 3D Path Planning in Cluttered Environments

Fixed-Wing UAV Energy Efficient 3D Path Planning in Cluttered Environments

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

Identification of High Nitrogen Use Efficiency Phenotype in Rice (Oryza sativa L.) Through Entire Growth Duration by Unmanned Aerial Vehicle Multispectral Imagery

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