scholarly journals Rice Ear Counting Based on Image Segmentation and Establishment of a Dataset

Plants ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 1625
Author(s):  
Hongmin Shao ◽  
Rong Tang ◽  
Yujie Lei ◽  
Jiong Mu ◽  
Yan Guan ◽  
...  
Keyword(s):  
Rice Yield ◽  
Absolute Error ◽  
Single Shot ◽  
Manual Counting ◽  
Detection And Counting ◽  
Enhancement Method ◽  
Computer Vision Technology ◽  
The Mean ◽  
Variable Light ◽  
Counting Model

The real-time detection and counting of rice ears in fields is one of the most important methods for estimating rice yield. The traditional manual counting method has many disadvantages: it is time-consuming, inefficient and subjective. Therefore, the use of computer vision technology can improve the accuracy and efficiency of rice ear counting in the field. The contributions of this article are as follows. (1) This paper establishes a dataset containing 3300 rice ear samples, which represent various complex situations, including variable light and complex backgrounds, overlapping rice and overlapping leaves. The collected images were manually labeled, and a data enhancement method was used to increase the sample size. (2) This paper proposes a method that combines the LC-FCN (localization-based counting fully convolutional neural network) model based on transfer learning with the watershed algorithm for the recognition of dense rice images. The results show that the model is superior to traditional machine learning methods and the single-shot multibox detector (SSD) algorithm for target detection. Moreover, it is currently considered an advanced and innovative rice ear counting model. The mean absolute error (MAE) of the model on the 300-size test set is 2.99. The model can be used to calculate the number of rice ears in the field. In addition, it can provide reliable basic data for rice yield estimation and a rice dataset for research.

scholarly journals Spruce Counting Based on Lightweight Mask R-CNN with UAV Images

2021 ◽  
Vol 15 ◽  
pp. 634-642
Author(s):  
Wenjing Zhou ◽  
Xueyan Zhu ◽  
Mengmeng Gu ◽  
Fengjun Chen
Keyword(s):  
Absolute Error ◽  
Mobile Terminal ◽  
Experimental Results ◽  
Basic Model ◽  
Aerial Vehicle ◽  
Model Size ◽  
The Mean ◽  
Implementation Level ◽  
Uav Images ◽  
Counting Model

To achieve rapid and accurate counting of seedlings on mobile terminals such as Unmanned Aerial Vehicle (UAV), we propose a lightweight spruce counting model. Given the difficulties of spruce adhesion and complex environment interference, we adopt the Mask R-CNN as the basic model, which performs instance-level segmentation of the target. To successfully apply the basic model to the mobile terminal applications, we modify the Mask R-CNN model in terms of the light-weighted as follows: the feature extraction network is changed to MobileNetV1 network; NMS is changed to Fast NMS. At the implementation level, we expand the 403 spruce images taken by UAV to the 1612 images, where 1440 images are selected as the training set and 172 images are selected as the test set. We evaluate the lightweight Mask R-CNN model. Experimental results indicate that the Mean Counting Accuracy (MCA) is 95%, the Mean Absolute Error (MAE) is 8.02, the Mean Square Error (MSE) is 181.55, the Average Counting Time (ACT) is 1.514 s, and the Model Size (MS) is 90Mb. We compare the lightweight Mask R-CNN model with the counting effects of the Mask R-CNN model, the SSD+MobileNetV1 counting model, the FCN+Hough circle counting model, and the FCN+Slice counting model. ACT of the lightweight Mask R-CNN model is 0.876 s, 0.359 s, 1.691 s, and 2.443 s faster than the other four models, respectively. In terms of MCA, the lightweight Mask R-CNN model is similar to the Mask R-CNN model. It is 4.2%, 5.2%, and 9.3% higher than the SSD+MobileNetV1 counting model, the FCN+Slice counting model, and the FCN+Hough circle counting model, respectively. Experimental results demonstrate that the lightweight Mask R-CNN model achieves high accuracy and real-time performance, and makes a valuable exploration for the deployment of automatic seedling counting on the mobile terminal.

scholarly journals Estimation of Motion and Respiratory Characteristics during the Meditation Practice Based on Video Analysis

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3771
Author(s):  
Alexey Kashevnik ◽  
Walaa Othman ◽  
Igor Ryabchikov ◽  
Nikolay Shilov
Keyword(s):  
Respiratory Rate ◽  
Evaluation System ◽  
Body Movement ◽  
Absolute Error ◽  
The Body ◽  
Meditation Practice ◽  
Body Parts ◽  
Mental Health Training ◽  
Negative Thoughts ◽  
The Mean

Meditation practice is mental health training. It helps people to reduce stress and suppress negative thoughts. In this paper, we propose a camera-based meditation evaluation system, that helps meditators to improve their performance. We rely on two main criteria to measure the focus: the breathing characteristics (respiratory rate, breathing rhythmicity and stability), and the body movement. We introduce a contactless sensor to measure the respiratory rate based on a smartphone camera by detecting the chest keypoint at each frame, using an optical flow based algorithm to calculate the displacement between frames, filtering and de-noising the chest movement signal, and calculating the number of real peaks in this signal. We also present an approach to detecting the movement of different body parts (head, thorax, shoulders, elbows, wrists, stomach and knees). We have collected a non-annotated dataset for meditation practice videos consists of ninety videos and the annotated dataset consists of eight videos. The non-annotated dataset was categorized into beginner and professional meditators and was used for the development of the algorithm and for tuning the parameters. The annotated dataset was used for evaluation and showed that human activity during meditation practice could be correctly estimated by the presented approach and that the mean absolute error for the respiratory rate is around 1.75 BPM, which can be considered tolerable for the meditation application.

scholarly journals A Convolutional Neural Network-Based Method for Corn Stand Counting in the Field

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 507
Author(s):  
Le Wang ◽  
Lirong Xiang ◽  
Lie Tang ◽  
Huanyu Jiang
Keyword(s):  
Frame Rate ◽  
Flying Height ◽  
Battery Life ◽  
Growth Stages ◽  
Camera Motion ◽  
Long Duration ◽  
Manual Counting ◽  
Detection And Counting ◽  
Plant Image ◽  
Early Seedling

Accurate corn stand count in the field at early season is of great interest to corn breeders and plant geneticists. However, the commonly used manual counting method is time consuming, laborious, and prone to error. Nowadays, unmanned aerial vehicles (UAV) tend to be a popular base for plant-image-collecting platforms. However, detecting corn stands in the field is a challenging task, primarily because of camera motion, leaf fluttering caused by wind, shadows of plants caused by direct sunlight, and the complex soil background. As for the UAV system, there are mainly two limitations for early seedling detection and counting. First, flying height cannot ensure a high resolution for small objects. It is especially difficult to detect early corn seedlings at around one week after planting, because the plants are small and difficult to differentiate from the background. Second, the battery life and payload of UAV systems cannot support long-duration online counting work. In this research project, we developed an automated, robust, and high-throughput method for corn stand counting based on color images extracted from video clips. A pipeline developed based on the YoloV3 network and Kalman filter was used to count corn seedlings online. The results demonstrate that our method is accurate and reliable for stand counting, achieving an accuracy of over 98% at growth stages V2 and V3 (vegetative stages with two and three visible collars) with an average frame rate of 47 frames per second (FPS). This pipeline can also be mounted easily on manned cart, tractor, or field robotic systems for online corn counting.

scholarly journals Cotton Stand Counting from Unmanned Aerial System Imagery Using MobileNet and CenterNet Deep Learning Models

2021 ◽  
Vol 13 (14) ◽  
pp. 2822
Author(s):  
Zhe Lin ◽  
Wenxuan Guo
Keyword(s):  
Deep Learning ◽  
Cotton Plant ◽  
Unmanned Aerial System ◽  
Learning Models ◽  
Training Images ◽  
Testing Dataset ◽  
Cotton Plants ◽  
Detection And Counting ◽  
Different Dimensions ◽  
The Mean

An accurate stand count is a prerequisite to determining the emergence rate, assessing seedling vigor, and facilitating site-specific management for optimal crop production. Traditional manual counting methods in stand assessment are labor intensive and time consuming for large-scale breeding programs or production field operations. This study aimed to apply two deep learning models, the MobileNet and CenterNet, to detect and count cotton plants at the seedling stage with unmanned aerial system (UAS) images. These models were trained with two datasets containing 400 and 900 images with variations in plant size and soil background brightness. The performance of these models was assessed with two testing datasets of different dimensions, testing dataset 1 with 300 by 400 pixels and testing dataset 2 with 250 by 1200 pixels. The model validation results showed that the mean average precision (mAP) and average recall (AR) were 79% and 73% for the CenterNet model, and 86% and 72% for the MobileNet model with 900 training images. The accuracy of cotton plant detection and counting was higher with testing dataset 1 for both CenterNet and MobileNet models. The results showed that the CenterNet model had a better overall performance for cotton plant detection and counting with 900 training images. The results also indicated that more training images are required when applying object detection models on images with different dimensions from training datasets. The mean absolute percentage error (MAPE), coefficient of determination (R2), and the root mean squared error (RMSE) values of the cotton plant counting were 0.07%, 0.98 and 0.37, respectively, with testing dataset 1 for the CenterNet model with 900 training images. Both MobileNet and CenterNet models have the potential to accurately and timely detect and count cotton plants based on high-resolution UAS images at the seedling stage. This study provides valuable information for selecting the right deep learning tools and the appropriate number of training images for object detection projects in agricultural applications.

scholarly journals Design and Implementation of Intelligent Inspection and Alarm Flight System for Epidemic Prevention

Drones ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 68
Author(s):  
Jiwei Fan ◽  
Xiaogang Yang ◽  
Ruitao Lu ◽  
Xueli Xie ◽  
Weipeng Li
Keyword(s):  
Deep Learning ◽  
Autonomous Navigation ◽  
Detection Method ◽  
Active Role ◽  
Absolute Error ◽  
Face Mask ◽  
Learning Technology ◽  
Flight System ◽  
Crowd Density ◽  
The Mean

Unmanned aerial vehicles (UAV) and related technologies have played an active role in the prevention and control of novel coronaviruses at home and abroad, especially in epidemic prevention, surveillance, and elimination. However, the existing UAVs have a single function, limited processing capacity, and poor interaction. To overcome these shortcomings, we designed an intelligent anti-epidemic patrol detection and warning flight system, which integrates UAV autonomous navigation, deep learning, intelligent voice, and other technologies. Based on the convolution neural network and deep learning technology, the system possesses a crowd density detection method and a face mask detection method, which can detect the position of dense crowds. Intelligent voice alarm technology was used to achieve an intelligent alarm system for abnormal situations, such as crowd-gathering areas and people without masks, and to carry out intelligent dissemination of epidemic prevention policies, which provides a powerful technical means for epidemic prevention and delaying their spread. To verify the superiority and feasibility of the system, high-precision online analysis was carried out for the crowd in the inspection area, and pedestrians’ faces were detected on the ground to identify whether they were wearing a mask. The experimental results show that the mean absolute error (MAE) of the crowd density detection was less than 8.4, and the mean average precision (mAP) of face mask detection was 61.42%. The system can provide convenient and accurate evaluation information for decision-makers and meets the requirements of real-time and accurate detection.

Explanation Plus Prediction—The Logical Focus of Project Management Research

2021 ◽  
pp. 875697282199994
Author(s):  
Joseph F. Hair ◽  
Marko Sarstedt
Keyword(s):  
Project Management ◽  
Statistical Models ◽  
Predictive Power ◽  
Mean Absolute Error ◽  
Explanatory Power ◽  
Absolute Error ◽  
Model Parameters ◽  
Mean Square ◽  
Management Research ◽  
The Mean

Most project management research focuses almost exclusively on explanatory analyses. Evaluation of the explanatory power of statistical models is generally based on F-type statistics and the R 2 metric, followed by an assessment of the model parameters (e.g., beta coefficients) in terms of their significance, size, and direction. However, these measures are not indicative of a model’s predictive power, which is central for deriving managerial recommendations. We recommend that project management researchers routinely use additional metrics, such as the mean absolute error or the root mean square error, to accurately quantify their statistical models’ predictive power.

scholarly journals An Estimation Method of Continuous Non-Invasive Arterial Blood Pressure Waveform Using Photoplethysmography: A U-Net Architecture-Based Approach

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1867
Author(s):  
Tasbiraha Athaya ◽  
Sunwoong Choi
Keyword(s):  
Blood Pressure ◽  
Cardiovascular Health ◽  
Estimation Method ◽  
Absolute Error ◽  
British Hypertension Society ◽  
Non Invasive ◽  
Arterial Blood ◽  
Blood Pressure Waveform ◽  
The Mean ◽  
Mimic Iii

Blood pressure (BP) monitoring has significant importance in the treatment of hypertension and different cardiovascular health diseases. As photoplethysmogram (PPG) signals can be recorded non-invasively, research has been highly conducted to measure BP using PPG recently. In this paper, we propose a U-net deep learning architecture that uses fingertip PPG signal as input to estimate arterial BP (ABP) waveform non-invasively. From this waveform, we have also measured systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP). The proposed method was evaluated on a subset of 100 subjects from two publicly available databases: MIMIC and MIMIC-III. The predicted ABP waveforms correlated highly with the reference waveforms and we have obtained an average Pearson’s correlation coefficient of 0.993. The mean absolute error is 3.68 ± 4.42 mmHg for SBP, 1.97 ± 2.92 mmHg for DBP, and 2.17 ± 3.06 mmHg for MAP which satisfy the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) standard and obtain grade A according to the British Hypertension Society (BHS) standard. The results show that the proposed method is an efficient process to estimate ABP waveform directly using fingertip PPG.

scholarly journals A Multi-Channel Uncertainty-Aware Multi-Resolution Network for MR to CT Synthesis

2021 ◽  
Vol 11 (4) ◽  
pp. 1667
Author(s):  
Kerstin Klaser ◽  
Pedro Borges ◽  
Richard Shaw ◽  
Marta Ranzini ◽  
Marc Modat ◽  
...  
Keyword(s):  
Medical Image Analysis ◽  
Absolute Error ◽  
Magnetic Resonance Images ◽  
The Body ◽  
Whole Body ◽  
Image Size ◽  
Body Images ◽  
Image Translation ◽  
The Mean ◽  
Synthetic Ct

Synthesising computed tomography (CT) images from magnetic resonance images (MRI) plays an important role in the field of medical image analysis, both for quantification and diagnostic purposes. Convolutional neural networks (CNNs) have achieved state-of-the-art results in image-to-image translation for brain applications. However, synthesising whole-body images remains largely uncharted territory, involving many challenges, including large image size and limited field of view, complex spatial context, and anatomical differences between images acquired at different times. We propose the use of an uncertainty-aware multi-channel multi-resolution 3D cascade network specifically aiming for whole-body MR to CT synthesis. The Mean Absolute Error on the synthetic CT generated with the MultiResunc network (73.90 HU) is compared to multiple baseline CNNs like 3D U-Net (92.89 HU), HighRes3DNet (89.05 HU) and deep boosted regression (77.58 HU) and shows superior synthesis performance. We ultimately exploit the extrapolation properties of the MultiRes networks on sub-regions of the body.

Visualizing the Variance of a Random Variable

2011 ◽  
Vol 18 (01) ◽  
pp. 71-85
Author(s):  
Fabrizio Cacciafesta
Keyword(s):  
Stochastic Dominance ◽  
Mean Absolute Error ◽  
Random Variable ◽  
Absolute Error ◽  
Second Order ◽  
Taylor Formula ◽  
Order Stochastic Dominance ◽  
The Mean ◽  
Second Order Stochastic Dominance ◽  
Options Theory

We provide a simple way to visualize the variance and the mean absolute error of a random variable with finite mean. Some application to options theory and to second order stochastic dominance is given: we show, among other, that the "call-put parity" may be seen as a Taylor formula.

scholarly journals Day-Ahead Forecasting of Hourly Photovoltaic Power Based on Robust Multilayer Perception

2018 ◽  
Vol 10 (12) ◽  
pp. 4863 ◽  
Author(s):  
Chao Huang ◽  
Longpeng Cao ◽  
Nanxin Peng ◽  
Sijia Li ◽  
Jing Zhang ◽  
...  
Keyword(s):  
Power Plants ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Multilayer Perception ◽  
Squared Error ◽  
The Mean ◽  
Effectiveness And Efficiency ◽  
Mlp Network ◽  
Grid Operation ◽  
Better Than

Photovoltaic (PV) modules convert renewable and sustainable solar energy into electricity. However, the uncertainty of PV power production brings challenges for the grid operation. To facilitate the management and scheduling of PV power plants, forecasting is an essential technique. In this paper, a robust multilayer perception (MLP) neural network was developed for day-ahead forecasting of hourly PV power. A generic MLP is usually trained by minimizing the mean squared loss. The mean squared error is sensitive to a few particularly large errors that can lead to a poor estimator. To tackle the problem, the pseudo-Huber loss function, which combines the best properties of squared loss and absolute loss, was adopted in this paper. The effectiveness and efficiency of the proposed method was verified by benchmarking against a generic MLP network with real PV data. Numerical experiments illustrated that the proposed method performed better than the generic MLP network in terms of root mean squared error (RMSE) and mean absolute error (MAE).

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