Counting plants using deep learning

Ribera, Javier; Chen, Yuhao; Boomsma, Christopher R.; Delp, Edward J.

doi:10.1109/globalsip.2017.8309180

Cited by 37 publications

(35 citation statements)

References 15 publications

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“…Then, an end-to-end deep CNN was presented to count the number of cells in a microscopic image. Ribera et al [99] proposed a regression model to estimate plants in an image (taken through a UAV). They minimized the number of neurons in the final layer to reduce the computational complexity of the network.…”

Section: Aerial-view-cnn-cc Techniquesmentioning

confidence: 99%

Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, and Performance Evaluation

Ilyas

Shahzad

Kim

2019

Sensors

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Traditional handcrafted crowd-counting techniques in an image are currently transformed via machine-learning and artificial-intelligence techniques into intelligent crowd-counting techniques. This paradigm shift offers many advanced features in terms of adaptive monitoring and the control of dynamic crowd gatherings. Adaptive monitoring, identification/recognition, and the management of diverse crowd gatherings can improve many crowd-management-related tasks in terms of efficiency, capacity, reliability, and safety. Despite many challenges, such as occlusion, clutter, and irregular object distribution and nonuniform object scale, convolutional neural networks are a promising technology for intelligent image crowd counting and analysis. In this article, we review, categorize, analyze (limitations and distinctive features), and provide a detailed performance evaluation of the latest convolutional-neural-network-based crowd-counting techniques. We also highlight the potential applications of convolutional-neural-network-based crowd-counting techniques. Finally, we conclude this article by presenting our key observations, providing strong foundation for future research directions while designing convolutional-neural-network-based crowd-counting techniques. Further, the article discusses new advancements toward understanding crowd counting in smart cities using the Internet of Things (IoT).

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Section: Aerial-view-cnn-cc Techniquesmentioning

confidence: 99%

Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, and Performance Evaluation

Ilyas

Shahzad

Kim

2019

Sensors

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“…Gnädinger y Schmidhalter [22] counted corn plants in images, improving the image contrast and using segmentation techniques with an error smaller than 5%; some other authors used deep learning, segmentation algorithms and neural networks to count corn plants. [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Digital Count of Corn Plants Using Images Taken by Unmanned Aerial Vehicles and Cross Correlation of Templates

et al. 2020

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The number of plants, or planting density, is a key factor in corn crop yield. The objective of the present research work was to count corn plants using images obtained by sensors mounted on an unmanned aerial vehicle (UAV). An experiment was set up with five levels of nitrogen fertilization (140, 200, 260, 320 and 380 kg/ha) and four replicates, resulting in 20 experimental plots. The images were taken at 23, 44 and 65 days after sowing (DAS) at a flight altitude of 30 m, using two drones equipped with RGB sensors of 12, 16 and 20 megapixels (Canon PowerShot S100_5.2, Sequoia_4.9, DJI FC6310_8.8). Counting was done through normalized cross-correlation (NCC) for four, eight and twelve plant samples or templates in the a* channel of the CIELAB color space because it represented the green color that allowed plant segmentation. A mean precision of 99% was obtained for a pixel size of 0.49 cm, with a mean error of 2.2% and a determination coefficient of 0.90 at 44 DAS. Precision values above 91% were obtained at 23 and 44 DAS, with a mean error between plants counted digitally and visually of ±5.4%. Increasing the number of samples or templates in the correlation estimation improved the counting precision. Good precision was achieved in the first growth stages of the crop when the plants do not overlap and there are no weeds. Using sensors and unmanned aerial vehicles, it is possible to determine the emergence of seedlings in the field and more precisely evaluate planting density, having more accurate information for better management of corn fields.

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“…In the past few years, low-cost Unmanned Airborne Vehicles (UAVs) equipped with consumer-grade imaging systems (e.g., commercial off-the-shelf digital camera) have emerged as a potential remote sensing platform that could satisfy the needs of a wide range of applications, such as precision agriculture [1][2][3][4][5][6][7][8][9], environmental monitoring [10][11][12][13][14], forest inventory [15,16], wildlife research [17,18], and archaeological applications [19,20]. Compared to conventional human-operated terrestrial and airborne mapping/remote sensing systems, the advantages of UAVs include their low-cost, small size, low flying height, ease of storage and deployment, and the capability of providing high spatial resolution geospatial data at a higher data collection rate [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, there must be four additional constraints that can be imposed on the nine elements of the Essential matrix [31]. Given that the Essential matrix has rank two, the first cubic constraint on the nine unknown parameters of the Essential matrix is presented as in Equation (4), where the determinant of the matrix has to be zero. Then, another two constraintsnamely the trace constraints-are deduced from the equality as established in Equation (5).…”

mentioning

confidence: 99%

“…The first closed-form solution of the Essential matrix is proposed by Longuet-Higgins for recovering the structure of a scene from two views that have been captured by a calibrated camera [33]. In spite of its simplicity, this approach fails to consider both the cubic and trace constraints as shown in Equations (4) and (5), respectively. Therefore, a minimum of eight conjugate points is required in this approach, and it has been criticized for its excessive sensitivity to noise in the image coordinates of conjugate point pairs as well as having an object space that is almost planar.…”

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confidence: 99%

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Automated Aerial Triangulation for UAV-Based Mapping

Zhou

Xiong

et al. 2018

Remote Sensing

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Accurate 3D reconstruction/modelling from unmanned aerial vehicle (UAV)-based imagery has become the key prerequisite in various applications. Although current commercial software has automated the process of image-based reconstruction, a transparent system, which can be incorporated with different user-defined constraints, is still preferred by the photogrammetric research community. In this regard, this paper presents a transparent framework for the automated aerial triangulation of UAV images. The proposed framework is conducted in three steps. In the first step, two approaches, which take advantage of prior information regarding the flight trajectory, are implemented for reliable relative orientation recovery. Then, initial recovery of image exterior orientation parameters (EOPs) is achieved through either an incremental or global approach. Finally, a global bundle adjustment involving Ground Control Points (GCPs) and check points is carried out to refine all estimated parameters in the defined mapping coordinate system. Four real image datasets, which are acquired by two different UAV platforms, have been utilized to evaluate the feasibility of the proposed framework. In addition, a comparative analysis between the proposed framework and the existing commercial software is performed. The derived experimental results demonstrate the superior performance of the proposed framework in providing an accurate 3D model, especially when dealing with acquired UAV images containing repetitive pattern and significant image distortions.

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Counting plants using deep learning

Cited by 37 publications

References 15 publications

Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, and Performance Evaluation

Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, and Performance Evaluation

Digital Count of Corn Plants Using Images Taken by Unmanned Aerial Vehicles and Cross Correlation of Templates

Automated Aerial Triangulation for UAV-Based Mapping

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