A review of unmanned aerial vehicle-based methods for plant stand count evaluation in row crops

Pathak, Harsh; Igathinathane, C.; Zhang, Z.; Archer, David W.; Hendrickson, John

doi:10.1016/j.compag.2022.107064

Cited by 23 publications

(5 citation statements)

References 73 publications

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“…In recent years, the UAV is more frequently used for plant counting because the captured data has higher spatial and temporal resolution [23]. João Valente et al proposed using high-resolution UAV imagery as data to count the plant number of spinach.…”

Section: Plant Countingmentioning

confidence: 99%

Automated Counting of Tobacco Plants Using Multispectral UAV Data

Lin,

Chen,

Qiang

et al. 2023

Agronomy

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Plant counting is an important part in precision agriculture (PA). The Unmanned Aerial Vehicle (UAV) becomes popular in agriculture because it can capture data with higher spatiotemporal resolution. When it is equipped with multispectral sensors, more meaningful multispectral data is obtained for plants’ analysis. After tobacco seedlings are raised, they are transplanted into the field. The counting of tobacco plant stands in the field is important for monitoring the transplant survival rate, growth situation, and yield estimation. In this work, we adopt the object detection (OD) method of deep learning to automatically count the plants with multispectral images. For utilizing the advanced YOLOv8 network, we modified the architecture of the network to adapt to the different band combinations and conducted extensive data pre-processing work. The Red + Green + NIR combination obtains the best detection results, which reveal that using a specific band or band combinations can obtain better results than using the traditional RGB images. For making our method more practical, we designed an algorithm that can handling the image of a whole plot, which is required to be watched. The counting accuracy is as high as 99.53%. The UAV, multispectral data combined with the powerful deep learning methods show promising prospective in PA.

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Section: Plant Countingmentioning

confidence: 99%

Automated Counting of Tobacco Plants Using Multispectral UAV Data

Lin,

Chen,

Qiang

et al. 2023

Agronomy

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“…The dynamics of the autonomous navigation landscape change and the effectiveness and robustness of path planning algorithms are critical for the operational success of Unmanned Ground Vehicles (UGVs) [1][2][3][4][5][6][7][8][9][10][11][12]. These vehicles are used in various applications including military operations [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30], disaster reliefs, as well as agricultural automation and require sophisticated navigation techniques that can allow them to navigate safely and effectively through difficult environments [31][32][33][34][35][36][37][38][39][40][41][42][43][44][45]. Traditional path planning methods mainly face an optimization task of path length and computational efficiency in dynamic and unpredictable terrains .…”

Section: Introductionmentioning

confidence: 99%

Path planning for a UGV using Salp Swarm Algorithm

AlShabi,

Ballous,

Bou Nassif

et al. 2024

Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2024

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The paper researches a utilization of the Salp Swarm Algorithm (SSA), a bio-mimetic optimization technique, to improve path planning in Unmanned Ground Vehicles (UGVs). Because of the crucial role of the efficient and reliable path planning in the implementation of UGVs in such sectors as military, rescue operations, and agriculture, there is a need for algorithms that are capable of navigating complex environments. The concept of SSA, based on the natural swarming behavior of salps, represents a very promising approach that is characterized by the exploration and exploitation properties of the algorithm. This study evaluates the performance of the SSA relative to existing particle swarm optimization (PSO), in terms of path optimality, computational efficiency, and dynamic obstacle adaptability, through a number of simulated environments. Results show that the SSA has the potential to compete with the traditional algorithms in path efficiency and computational load. However, PSO shows slight superiority results compared to SSA. This study highlights the potency of bio-inspired algorithms, specifically the SSA, in enhancing the field of autonomous navigation for UGVs. It introduces new possibilities of practical application of SSA in real-life scenarios, demonstrating its scalability and resilience. The findings of this study make a contribution to the general discussion on the improvement of planning of autonomous routes and provide a possible way for more sustainable and effective UGV activities.

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“…Indeed, the precision and reliability of these complex flying robots have been vital in a wide range of areas, such as military missions, disaster control and environmental surveillance. The process of parameter estimation represents the core of realizing these properties and is a major way of improving performance and safety of UAV operations [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. Some of the activities carried out in parameter estimation in UAVs include establishing the basic flight parameters including the aerodynamic coefficients that are crucial in the proper control and navigation during flight.…”

Section: Introductionmentioning

confidence: 99%

Parameter estimation of an unmanned aerial vehicle using dandelion algorithm

AlShabi,

Saleh,

Obaideen

et al. 2024

Unmanned Systems Technology XXVI

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In this study, we introduce a novel approach to the parameter estimation of Unmanned Aerial Vehicles (UAVs) utilizing the dandelion algorithm, a bio-inspired optimization technique that simulates the seed dispersal mechanism of dandelions. With UAVs increasingly becoming integral to various sectors, accurate parameter estimation emerges as a critical factor in ensuring their optimal performance and safety. Traditional parameter estimation methods often fall short, plagued by computational inefficiencies and a propensity for local optima, which can significantly hinder UAV operations. The dandelion algorithm, with its unique global search capabilities and adeptness in navigating multidimensional spaces, presents a solution that markedly enhances the precision and speed of parameter estimation. Through a series of simulations involving diverse UAV models, this study compares the performance of the dandelion algorithm against the conventional technique; the Particle Swarm Optimization (PSO), demonstrating its superior ability in achieving rapid convergence, higher accuracy, and an exceptional aptitude for avoiding local optima. Our findings not only underscore the algorithm's potential to revolutionize UAV parameter estimation but also highlight its applicability in advancing UAV technology and bio-inspired computational algorithms. This research contributes to the aerospace engineering field by offering an innovative, efficient alternative to existing parameter estimation methods, promising significant improvements in the design, operation, and safety of UAV systems across a spectrum of applications.

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A review of unmanned aerial vehicle-based methods for plant stand count evaluation in row crops

Cited by 23 publications

References 73 publications

Automated Counting of Tobacco Plants Using Multispectral UAV Data

Automated Counting of Tobacco Plants Using Multispectral UAV Data

Path planning for a UGV using Salp Swarm Algorithm

Parameter estimation of an unmanned aerial vehicle using dandelion algorithm

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