scholarly journals Classification of Rice Yield Using UAV-Based Hyperspectral Imagery and Lodging Feature

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Jian Wang ◽  
Bizhi Wu ◽  
Markus V. Kohnen ◽  
Daqi Lin ◽  
Changcai Yang ◽  
...  
Keyword(s):  
Large Scale ◽  
Rice Yield ◽  
Nondestructive Method ◽  
Hyperspectral Data ◽  
Classification Model ◽  
High Yield ◽  
Multiple Time ◽  
Japonica Rice ◽  
Rice Breeding

High-yield rice cultivation is an effective way to address the increasing food demand worldwide. Correct classification of high-yield rice is a key step of breeding. However, manual measurements within breeding programs are time consuming and have high cost and low throughput, which limit the application in large-scale field phenotyping. In this study, we developed an accurate large-scale approach and presented the potential usage of hyperspectral data for rice yield measurement using the XGBoost algorithm to speed up the rice breeding process for many breeders. In total, 13 japonica rice lines in regional trials in northern China were divided into different categories according to the manual measurement of yield. Using an Unmanned Aerial Vehicle (UAV) platform equipped with a hyperspectral camera to capture images over multiple time series, a rice yield classification model based on the XGBoost algorithm was proposed. Four comparison experiments were carried out through the intraline test and the interline test considering lodging characteristics at the midmature stage or not. The result revealed that the degree of lodging in the midmature stage was an important feature affecting the classification accuracy of rice. Thus, we developed a low-cost, high-throughput phenotyping and nondestructive method by combining UAV-based hyperspectral measurements and machine learning for estimation of rice yield to improve rice breeding efficiency.

scholarly journals Uncertainty-Aware Deep Learning-Based Cardiac Arrhythmias Classification Model of Electrocardiogram Signals

Computers ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 82
Author(s):  
Ahmad O. Aseeri
Keyword(s):  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Probabilistic Approach ◽  
Classification Model ◽  
Gating Mechanism ◽  
Uncertainty Estimates ◽  
Wide Range

Deep Learning-based methods have emerged to be one of the most effective and practical solutions in a wide range of medical problems, including the diagnosis of cardiac arrhythmias. A critical step to a precocious diagnosis in many heart dysfunctions diseases starts with the accurate detection and classification of cardiac arrhythmias, which can be achieved via electrocardiograms (ECGs). Motivated by the desire to enhance conventional clinical methods in diagnosing cardiac arrhythmias, we introduce an uncertainty-aware deep learning-based predictive model design for accurate large-scale classification of cardiac arrhythmias successfully trained and evaluated using three benchmark medical datasets. In addition, considering that the quantification of uncertainty estimates is vital for clinical decision-making, our method incorporates a probabilistic approach to capture the model’s uncertainty using a Bayesian-based approximation method without introducing additional parameters or significant changes to the network’s architecture. Although many arrhythmias classification solutions with various ECG feature engineering techniques have been reported in the literature, the introduced AI-based probabilistic-enabled method in this paper outperforms the results of existing methods in outstanding multiclass classification results that manifest F1 scores of 98.62% and 96.73% with (MIT-BIH) dataset of 20 annotations, and 99.23% and 96.94% with (INCART) dataset of eight annotations, and 97.25% and 96.73% with (BIDMC) dataset of six annotations, for the deep ensemble and probabilistic mode, respectively. We demonstrate our method’s high-performing and statistical reliability results in numerical experiments on the language modeling using the gating mechanism of Recurrent Neural Networks.

scholarly journals A Efficient Solution for Classification of Crops using Hyper Spectral Satellite Images

2019 ◽  
Vol 9 (2) ◽  
pp. 5204-5211
Keyword(s):  
Hyperspectral Image ◽  
Satellite Image ◽  
Hyperspectral Data ◽  
Classification Model ◽  
Research Activity ◽  
Research Issues ◽  
Computational Overhead ◽  
Wide Range ◽  
Crop Classification

Crop identification (CI) utilizing hyperspectral pictures/images (HSI) collected from satellite is one of the effective research area considering various agriculture related applications. Wide range of research activity is carried out and modelled in the area of crop recognition (CR) for building efficient model. Correlation filter (CF) is considered to be one of an effective method and are been applied by existing methodologies for identifying similar signal features. Nonetheless, very limited is work is carried out using CF for crop classification using hyperspectral data. Further, effective method is required that bring good tradeoffs between memory and computational overhead. The crop classification model can be improved by combining machine learning (ML) technique with CF. HSI is composed of hundreds of channels with large data dimension that gives entire information of imaging. Thus, using classification model is very useful for real-time application uses. However, the accuracy of classification task is affected as HSI is composed of high number of redundant and correlated feature sets. Along with, induce computational overhead with less benefits using redundant features. Thus, effective band selection, texture analysis, and classification method is required for accurately classifying multiple crops. This paper analyses various existing techniques for identification and classification of crops using satellite imagery detection method. Then, identify the research issues, challenges, and problems of existing model for building efficient techniques for identification and classification of crops using satellite image. Experiment are conducted on standard hyperspectral data. The result attained shows proposed model attain superior classification accuracy when compared with existing hyperspectral image classification model.

scholarly journals Rapid and Accurate Varieties Classification of Different Crop Seeds Under Sample-Limited Condition Based on Hyperspectral Imaging and Deep Transfer Learning

2021 ◽  
Vol 9 ◽  
Author(s):  
Na Wu ◽  
Fei Liu ◽  
Fanjia Meng ◽  
Mu Li ◽  
Chu Zhang ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Transfer Learning ◽  
Large Scale ◽  
Accurate Method ◽  
Classification Model ◽  
Screening Process ◽  
Germplasm Screening ◽  
Limited Condition ◽  
Crop Germplasm

Rapid varieties classification of crop seeds is significant for breeders to screen out seeds with specific traits and market regulators to detect seed purity. However, collecting high-quality, large-scale samples takes high costs in some cases, making it difficult to build an accurate classification model. This study aimed to explore a rapid and accurate method for varieties classification of different crop seeds under the sample-limited condition based on hyperspectral imaging (HSI) and deep transfer learning. Three deep neural networks with typical structures were designed based on a sample-rich Pea dataset. Obtained the highest accuracy of 99.57%, VGG-MODEL was transferred to classify four target datasets (rice, oat, wheat, and cotton) with limited samples. Accuracies of the deep transferred model achieved 95, 99, 80.8, and 83.86% on the four datasets, respectively. Using training sets with different sizes, the deep transferred model could always obtain higher performance than other traditional methods. The visualization of the deep features and classification results confirmed the portability of the shared features of seed spectra, providing an interpreted method for rapid and accurate varieties classification of crop seeds. The overall results showed great superiority of HSI combined with deep transfer learning for seed detection under sample-limited condition. This study provided a new idea for facilitating a crop germplasm screening process under the scenario of sample scarcity and the detection of other qualities of crop seeds under sample-limited condition based on HSI.

scholarly journals Effects of Selenium Fertilizer Application on Yield and Selenium Accumulation Characteristics of Different Japonica Rice Varieties

2021 ◽  
Vol 13 (18) ◽  
pp. 10284
Author(s):  
Juan Yan ◽  
Xiaoju Chen ◽  
Tonggui Zhu ◽  
Zhongping Zhang ◽  
Jianbo Fan
Keyword(s):  
Rice Yield ◽  
Fertilizer Application ◽  
High Yield ◽  
Utilization Rate ◽  
Japonica Rice ◽  
Rice Varieties ◽  
Polished Rice ◽  
Selenium Accumulation ◽  
Different Levels ◽  
Total Selenium

In this study, three japonica rice varieties—Nanjing 9108, Jiahua 1 and Wuyunjing 29—were supplied with different levels of nano-foliar selenium fertilizers (0, 40 and 80 kg Se ha−1) under field conditions. Their rice yield and absorption, accumulation, transportation and utilization of selenium were studied to find suitable selenium-rich rice cultivars and optimal selenium supply levels, while providing references for the development of selenium-rich rice. On an average basis, the Nanjing 9108, Jiahua 1 and Wuyunjing 29 yielded 8755 ± 190, 8200 ± 317 and 9098 ± 72.7 kg ha−1, respectively. The selenium content in polished rice of the three rice varieties is between 0.210 and 0.933 mg kg−1. When 40 g Se ha−1 nano-selenium fertilizer was used, the selenium accumulation in the shoots of Nanjing 9108, Jiahua 1 and Wuyunjing 29 was, respectively, 11.4 g Se ha−1, 12.3 g Se ha−1 and 12.2 g Se ha−1, and when 80 g Se ha−1 selenium fertilizer was applied, the total selenium accumulation of three rice varieties was, respectively, 2.45, 1.75 and 2.40 times that of 40 g Se ha−1 selenium fertilizer. No evident diversity was observed in the selenium transport coefficient and the apparent utilization rate of selenium among the three varieties. The three rice varieties in this experiment had a strong selenium enrichment capacity, and they could be planted as selenium-enriched and high-yield rice varieties. Further, the amount of selenium fertilizer should not exceed 40 g Se ha−1.

scholarly journals Hierarchical Classification of Urban ALS Data by Using Geometry and Intensity Information

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4583 ◽  
Author(s):  
Xiaoqiang Liu ◽  
Yanming Chen ◽  
Shuyi Li ◽  
Liang Cheng ◽  
Manchun Li
Keyword(s):  
Supervised Learning ◽  
Laser Scanning ◽  
Large Scale ◽  
Three Dimensional ◽  
Hierarchical Classification ◽  
Training Data ◽  
Classification Model ◽  
Learning Method ◽  
Intensity Information

Airborne laser scanning (ALS) can acquire both geometry and intensity information of geo-objects, which is important in mapping a large-scale three-dimensional (3D) urban environment. However, the intensity information recorded by ALS will be changed due to the flight height and atmospheric attenuation, which decreases the robustness of the trained supervised classifier. This paper proposes a hierarchical classification method by separately using geometry and intensity information of urban ALS data. The method uses supervised learning for stable geometry information and unsupervised learning for fluctuating intensity information. The experiment results show that the proposed method can utilize the intensity information effectively, based on three aspects, as below. (1) The proposed method improves the accuracy of classification result by using intensity. (2) When the ALS data to be classified are acquired under the same conditions as the training data, the performance of the proposed method is as good as the supervised learning method. (3) When the ALS data to be classified are acquired under different conditions from the training data, the performance of the proposed method is better than the supervised learning method. Therefore, the classification model derived from the proposed method can be transferred to other ALS data whose intensity is inconsistent with the training data. Furthermore, the proposed method can contribute to the hierarchical use of some other ALS information, such as multi-spectral information.

Automatic tumor tissue classification of oncological esophageal resectates based on hyperspectral data

2019 ◽  
Author(s):  
M Maktabi ◽  
H Köhler ◽  
R Thieme ◽  
JP Takoh ◽  
SM Rabe ◽  
...  
Keyword(s):  
Tumor Tissue ◽  
Hyperspectral Data ◽  
Tissue Classification

Classification of woody-shrub vegetation of the forest-steppe complex of Privolzhskaya Upland

2009 ◽  
pp. 27-53
Author(s):  
A. Yu. Kudryavtsev
Keyword(s):  
Plant Community ◽  
Plant Communities ◽  
Dominant Species ◽  
Large Scale ◽  
Nature Reserve ◽  
Vegetation Mapping ◽  
Vegetation Classification ◽  
Forest Steppe ◽  
Mapping Data

Diversity of plant communities in the nature reserve “Privolzhskaya Forest-Steppe”, Ostrovtsovsky area, is analyzed on the basis of the large-scale vegetation mapping data from 2000. The plant community classi­fication based on the Russian ecologic-phytocoenotic approach is carried out. 12 plant formations and 21 associations are distinguished according to dominant species and a combination of ecologic-phytocoenotic groups of species. A list of vegetation classification units as well as the characteristics of theshrub and woody communities are given in this paper.

Classification of vegetation in Cucphuong National Park with the aim of large-scale mapping, Vietnam

1996 ◽  
pp. 64-67 ◽  
Author(s):  
Nguen Nghia Thin ◽  
Nguen Ba Thu ◽  
Tran Van Thuy
Keyword(s):  
National Park ◽  
Large Scale ◽  
Satellite Images ◽  
Field Work ◽  
Aerial Photographs ◽  
Vegetation Map ◽  
Dense Forest ◽  
Broad Leaved Forest ◽  
Leaved Forest

The tropical seasonal rainy evergreen broad-leaved forest vegetation of the Cucphoung National Park has been classified and the distribution of plant communities has been shown on the map using the relations of vegetation to geology, geomorphology and pedology. The method of vegetation mapping includes: 1) the identifying of vegetation types in the remote-sensed materials (aerial photographs and satellite images); 2) field work to compile the interpretation keys and to characterize all the communities of a study area; 3) compilation of the final vegetation map using the combined information. In the classification presented a number of different level vegetation units have been identified: formation classes (3), formation sub-classes (3), formation groups (3), formations (4), subformations (10) and communities (19). Communities have been taken as mapping units. So in the vegetation map of the National Park 19 vegetation categories has been shown altogether, among them 13 are natural primary communities, and 6 are the secondary, anthropogenic ones. The secondary succession goes through 3 main stages: grassland herbaceous xerophytic vegetation, xerophytic scrub, dense forest.

Connectionist Temporal Classification Model for Dynamic Hand Gesture Recognition using RGB and Optical flow Data

2020 ◽  
Vol 17 (4) ◽  
pp. 497-506
Author(s):  
Sunil Patel ◽  
Ramji Makwana
Keyword(s):  
Neural Network ◽  
Optical Flow ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Classification Model ◽  
Hand Gesture ◽  
Flow Data ◽  
Dynamic Hand Gesture Recognition ◽  
Connectionist Temporal Classification

Automatic classification of dynamic hand gesture is challenging due to the large diversity in a different class of gesture, Low resolution, and it is performed by finger. Due to a number of challenges many researchers focus on this area. Recently deep neural network can be used for implicit feature extraction and Soft Max layer is used for classification. In this paper, we propose a method based on a two-dimensional convolutional neural network that performs detection and classification of hand gesture simultaneously from multimodal Red, Green, Blue, Depth (RGBD) and Optical flow Data and passes this feature to Long-Short Term Memory (LSTM) recurrent network for frame-to-frame probability generation with Connectionist Temporal Classification (CTC) network for loss calculation. We have calculated an optical flow from Red, Green, Blue (RGB) data for getting proper motion information present in the video. CTC model is used to efficiently evaluate all possible alignment of hand gesture via dynamic programming and check consistency via frame-to-frame for the visual similarity of hand gesture in the unsegmented input stream. CTC network finds the most probable sequence of a frame for a class of gesture. The frame with the highest probability value is selected from the CTC network by max decoding. This entire CTC network is trained end-to-end with calculating CTC loss for recognition of the gesture. We have used challenging Vision for Intelligent Vehicles and Applications (VIVA) dataset for dynamic hand gesture recognition captured with RGB and Depth data. On this VIVA dataset, our proposed hand gesture recognition technique outperforms competing state-of-the-art algorithms and gets an accuracy of 86%

A Simple Synthesis of the Anticancer Drug Altretamine

2020 ◽  
Vol 17 (8) ◽  
pp. 628-630
Author(s):  
Vu Binh Duong ◽  
Pham Van Hien ◽  
Tran Thai Ngoc ◽  
Phan Dinh Chau ◽  
Tran Khac Vu
Keyword(s):  
Aqueous Solution ◽  
Anticancer Drug ◽  
Potassium Hydroxide ◽  
Large Scale ◽  
Synthesis Method ◽  
Practical Method ◽  
Cyanuric Chloride ◽  
Simple Synthesis ◽  
High Yield ◽  
One Step

A simple and practical method for the synthesis on a large scale of altretamine (1), a wellknown antitumor drug, has been successfully developed. The synthesis method involves the conversion of cyanuric chloride (2) into altretamine (1) by dimethylamination of 2 with an aqueous solution of 40% dimethylamine and potassium hydroxide in 1, -dioxan 4in one step to give altretamine (1) in high yield.

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