Drone and GPS Sensors-Based Grassland Management Using Deep-Learning Image Segmentation

Tsuichihara, Satoki; Akita, Shingo; Ike, Reiichirou; Shigeta, Masahiro; Takemura, Hiroshi; Natori, Takahiro; Aikawa, Naoyuki; Shindo, Kazumasa; Ide, Yoshihiko; Tejima, Shigeki

doi:10.1109/irc.2019.00123

Cited by 9 publications

(4 citation statements)

References 6 publications

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“…Using U-Net, Lin et al [89] achieved high accuracy with a small dataset. Similarly, with only 48 images, Tsuichihara et al [93] achieved an accuracy of about 80% in detecting broad-leaved weeds. Table 7 provides a summary of studies using the U-Net architecture.…”

Section: Support Vector Machines (Svm)mentioning

confidence: 97%

“…U-Net models performed well with fewer training samples and provided better performance for segmentation tasks [133]. The authors of [25,[89][90][91][92][93]99] showed that U-Net outperformed other CNN models. In addition, Arun et al [25] showed that U-Net can be further optimized without compromising performance.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

“…CNNs: [25,66,68,81,[93][94][95][98][99][100]113,130,135] SSDs: [99,109] TSDs: [109,115,117] Transformers: [24,125] CNNs: [84,130,135] GANs: [134] CNNs: [78] Pesticides/Diseases Treatment Classical Methods: SVM [52,54] CNNs: [65,67,[69][70][71]73,74,76,79,80,92,96,112,114,116] SSDs: [77,[101][102][103]107] Classical Methods: SVM [129] Transformers: [120,121] GANs:…”

Section: Spatial Segregation and Segmentationmentioning

confidence: 99%

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Machine Learning for Precision Agriculture Using Imagery from Unmanned Aerial Vehicles (UAVs): A Survey

Zualkernan

Abuhani

Hussain

et al. 2023

Drones

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Unmanned aerial vehicles (UAVs) are increasingly being integrated into the domain of precision agriculture, revolutionizing the agricultural landscape. Specifically, UAVs are being used in conjunction with machine learning techniques to solve a variety of complex agricultural problems. This paper provides a careful survey of more than 70 studies that have applied machine learning techniques utilizing UAV imagery to solve agricultural problems. The survey examines the models employed, their applications, and their performance, spanning a wide range of agricultural tasks, including crop classification, crop and weed detection, cropland mapping, and field segmentation. Comparisons are made among supervised, semi-supervised, and unsupervised machine learning approaches, including traditional machine learning classifiers, convolutional neural networks (CNNs), single-stage detectors, two-stage detectors, and transformers. Lastly, future advancements and prospects for UAV utilization in precision agriculture are highlighted and discussed. The general findings of the paper demonstrate that, for simple classification problems, traditional machine learning techniques, CNNs, and transformers can be used, with CNNs being the optimal choice. For segmentation tasks, UNETs are by far the preferred approach. For detection tasks, two-stage detectors delivered the best performance. On the other hand, for dataset augmentation and enhancement, generative adversarial networks (GANs) were the most popular choice.

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Section: Support Vector Machines (Svm)mentioning

confidence: 97%

Section: Machine Learning Techniquesmentioning

confidence: 99%

Section: Spatial Segregation and Segmentationmentioning

confidence: 99%

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Machine Learning for Precision Agriculture Using Imagery from Unmanned Aerial Vehicles (UAVs): A Survey

Zualkernan

Abuhani

Hussain

et al. 2023

Drones

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“…Many kinds of research for autonomous flight of drones have assumed that monocular cameras are often used to detect and recognize objects around the drones [ 7 , 8 ]. Single monocular camera-based depth estimation is also actively researched [ 9 , 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Pix2Pix-Based Monocular Depth Estimation for Drones with Optical Flow on AirSim

Shimada

Nishikawa

Kong

et al. 2022

Sensors

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In this work, we propose a method for estimating depth for an image of a monocular camera in order to avoid a collision for the autonomous flight of a drone. The highest flight speed of a drone is generally approximate 22.2 m/s, and long-distant depth information is crucial for autonomous flights since if the long-distance information is not available, the drone flying at high speeds is prone to collisions. However, long-range, measurable depth cameras are too heavy to be equipped on a drone. This work applies Pix2Pix, which is a kind of Conditional Generative Adversarial Nets (CGAN). Pix2Pix generates depth images from a monocular camera. Additionally, this work applies optical flow to enhance the accuracy of depth estimation. In this work, we propose a highly accurate depth estimation method that effectively embeds an optical flow map into a monocular image. The models are trained with taking advantage of AirSim, which is one of the flight simulators. AirSim can take both monocular and depth images over a hundred meter in the virtual environment, and our model generates a depth image that provides the long-distance information than images captured by a common depth camera. We evaluate accuracy and error of our proposed method using test images in AirSim. In addition, the proposed method is utilized for flight simulation to evaluate the effectiveness to collision avoidance. As a result, our proposed method is higher accuracy and lower error than a state of work. Moreover, our proposed method is lower collision than a state of work.

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