Enhanced faster region-based convolutional neural network for oil palm tree detection

Oil palm trees are important economic crops in Malaysia. One of the audit procedures is to count the number of oil palm trees for plantation management, which helps the manager predict the plantation yield and the amount of fertilizer and labor force needed. However, the current counting method for oil palm tree plantation is manually counting using GIS software, which is tedious and inefficient for large scale plantation. To overcome this problem, researchers proposed automatic counting methods based on machine learning and image processing. However, traditional machine learning and image processing methods used handcrafted feature extraction methods. It can only extract low-middle level features from the image and lack of generalization ability. It’s applicable only for one application and will need reprogramming for other applications. The widely used feature extraction methods are local binary patterns (LBP), scale-invariant feature transform (SIFT), and the histogram of oriented gradients (HOG), which usually achieve low accuracy because of their limited feature representation ability and without generalization capability. Hence, this research aims to close the research gaps by exploring the deep learning-based object detection algorithm and the classical convolutional neural network (CNN) to build an automatic deep learning-based oil palm tree detection and counting framework. This study proposed a new deep learning method based on Faster RCNN for oil palm tree detection and counting. To reduce the overfitting problem during the training, this study uses the image processing method to augment the training dataset by random flipping the image and to increase the data’s contrast and brightness. The transfer learning model of ResNet50 was used as the CNN backbone and the Faster RCNN network was retrained to get the weight for automatic oil palm tree counting. To improve the performance of Faster RCNN, feature concatation method was used to integrate the high-level and low-level feature from ResNet50. The proposed model validated the testing dataset of three palm tree regions with mature, young, and mixed mature and young palm trees. The detection results were compared with two machine learning methods of ANN, SVM, image processing-based TM method, and the original Faster RCNN model respectively. The proposed enhanced Faster RCNN model shows a promising result of oil palm tree detection and counting. It achieved an overall accuracy of 97% in the testing dataset, 97.2% in the mixed palm tree region, and 96.9% in the mature and young palm tree region, while the traditional ANN, SVM, and TM methods are less than 90%. The accuracy of comparison reveals that the proposed EFRCNN model outperforms the Faster RCNN and the traditional ANN, SVM, and TM methods. It has the potential to apply in counting a large area of oil palm tree plantation

A review of Convolutional Neural Networks in Remote Sensing Image

Effectively analysis of remote-sensing images is very important in many practical applications, such as urban planning, geospatial object detection, military monitoring, vegetation mapping and precision agriculture. Recently, convolutional neural network based deep learning algorithm has achieved a series of breakthrough research results in the fields of objective detection, image semantic segmentation and image classification, etc. Their powerful feature learning capabilities have attracted more attention and have important research value. In this article, firstly we have summarized the basic structure and several classical convolutional neural network architectures. Secondly, the recent research problems on convolutional neural network are discussed. Later, we summarized the latest research results in convolutional neural network based remote sensing fields. Finally, the conclusion has made on the basis of current issue on convolutional neural networks and the future development direction

High-throughput sequencing and characterization of potentially pathogenic fungi from the vaginal mycobiome of giant panda (Ailuropoda melanoleuca) in estrus and non-estrus

IntroductionThe giant panda (Ailuropoda melanoleuca) reproduction is of worldwide attention, and the vaginal microbiome is one of the most important factors affecting the reproductive rate of giant pandas. The aim of this study is to investigate the diversity of vaginal mycobiota structure, and potential pathogenic fungi in female giant pandas during estrus and non-estrus.MethodsThis study combined with high-throughput sequencing and laboratory testing to compare the diversity of the vaginal mycobiota in giant pandas during estrus and non-estrus, and to investigate the presence of potentially pathogenic fungi. Potentially pathogenic fungi were studied in mice to explore their pathogenicity.Results and discussionThe results revealed that during estrus, the vaginal secretions of giant pandas play a crucial role in fungal colonization. Moreover, the diversity of the vaginal mycobiota is reduced and specificity is enhanced. The abundance of Trichosporon and Cutaneotrichosporon in the vaginal mycobiota of giant pandas during estrus was significantly higher than that during non-estrus periods. Apiotrichum and Cutaneotrichosporon were considered the most important genera, and they primarily originate from the environment owing to marking behavior exhibited during the estrous period of giant pandas. Trichosporon is considered a resident mycobiota of the vagina and is an important pathogen that causes infection when immune system is suppressed. Potentially pathogenic fungi were further isolated and identified from the vaginal secretions of giant pandas during estrus, and seven strains of Apiotrichum (A. brassicae), one strain of Cutaneotrichosporon (C. moniliiforme), and nine strains of Trichosporon (two strains of T. asteroides, one strain of T. inkin, one strain of T. insectorum, and five strains of T. japonicum) were identified. Pathogenicity results showed that T. asteroides was the most pathogenic strain, as it is associated with extensive connective tissue replacement and inflammatory cell infiltration in both liver and kidney tissues. The results of this study improve our understanding of the diversity of the vaginal fungi present in giant pandas and will significantly contribute to improving the reproductive health of giant pandas in the future

Age-related differences in risk factors, clinical characteristics, and outcomes for intracerebral hemorrhage

Background and purposeIntracerebral hemorrhage (ICH) is a severe form of stroke that remains understudied in the young adults. We aimed to investigate the clinical presentation, and risk factors associated with ICH in this age group and compare them to older patients.MethodsOur study included ICH patients admitted between March 2016 and December 2021 in the First Affiliated Hospital of Chongqing Medical University from our ongoing prospective cohort database. Demographic characteristics, etiology, risk factors, and clinical outcomes were compared between elderly and young patients. Furthermore, logistic regression analysis was employed to explore risk factors associated with the functional outcome at 3-months.ResultsWe selected 1,003 patients (mean age, 59.9 ±13.8 years old), 746 (74.4%) patients were aged >50 years. The logistic regression analysis showed young patients have a higher proportion of secondary ICH, higher white blood cell count and higher body mass index (BMI), but less diabetes mellitus. Of all patients, predictors of 3-month functional independence was first-ever ICH and age ≤50 years. The history of nephropathy and stroke, higher baseline NIHSS score, larger hematoma volume, and the presence of hydrocephalus were associated with poor outcomes. And the white blood cell count could significantly influence the prognosis among young ICH patients. Three-month functional outcome based on modified Rankin scale score was better in young patients than the elderly (OR, 1.232; 95% CI, 1.095–1.388; p < 0.001).ConclusionsThe highest incidence of ICH occurs in the age groups of 50–59 and 60–69. ICH in young adults had higher white blood cell and BMI compared to the elderly, and differs in etiological distribution. The young patients also had similar short-term mortality but more favorable functional outcomes than the elderly. Furthermore, NIHSS score and larger hematoma volumes were associated with poor outcome in all patients

A Survey on Deployment and Coverage Strategies in Three-Dimensional Wireless Sensor Networks

The deployment and coverage strategies are key issues in researches and the applications of WSNs, since it greatly influences the node energy, communication bandwidth and Quality of Service (QoS) for WSNs. The current literatures on sensor coverage control approaches mainly focused on the two-dimensional (2D) plane. However, many applications including underwater monitoring, indoor surveillance and others scenarios that are deployed on the three-dimensional (3D) space. This paper presents an extensive overview of various coverage and deployment problems and algorithms in three-dimensional wireless sensor networks. It focuses on different coverage strategies, vital characteristics, design schemes, advanced methods and practical constraints dealing with coverage and deployment in 3D WSNs

A hybrid range-free algorithm using dynamic communication range for wireless sensor networks

Location plays a backbone role in networks, since it will great influence basic wireless sensor networks (WSNs) architecture. Distance-Vector Hop (DV-Hop) is a representative range-free localization algorithm, which is widely utilized to locate node position in location-based application. However, with poor localization accuracy, it cannot satisfy precise location-based application requirement. Consequently, we proposed a hybrid range-free algorithm depends on dynamic communication range to address low localization accuracy problem, named as DCDV-Hop. Firstly, we applied statistical methods to analyze the relationship between location error and hop count under different communication ranges. Thereafter, we employed centroid algorithm to calculate target node coordinate based on hop threshold. Finally, a weighted least square is applied to locate remaining target nodes. We conducted considerable experiments, the results demonstrated that our proposed algorithm DCDV-Hop can effectively reduce accumulate localization error and improve localization accuracy of target nodes, with stable performance. Moreover, maximum localization accuracy reached up to 91.35% and localization error reduced more than 50%, compared with DV-Hop algorithm

Reducing energy bill of data center via flexible partial execution

Several Demand Response (DR) strategies are emerged recently to modulate the workloads of Data Center (DC) and shave the corresponding energy bill. However, since most of these DR strategies will result in the increase of latency, they can only be used for modulating the elastic workloads, which are delay-tolerant. To improve the flexibility of workload modulation and reduction of energy bill, we propose flexible partial execution for DC, which can be used to handle inelastic workloads. Further, to incentivize users of DC grant flexible partial execution of their workloads, we offer them time-varying price discount, on top of commonly-applied usage-based pricing policy. With real-world data traces, the results show that a DC with our proposed flexible partial execution can shave its peak power consumption and energy bill by 30.9%30.9% and 20.8%20.8% while improving its profit by 18.8%18.8% when comparing against the one with rigid partial execution, i.e., a fixed percentage of requests/workloads can be partially executed, which is commonly employed by today’s DCs

Sustainable Oil Palm Resource Assessment Based on an Enhanced Deep Learning Method

Knowledge of the number and distribution of oil palm trees during the crop cycle is vital for sustainable management and predicting yields. The accuracy of the conventional image processing method is limited for the hand-crafted feature extraction method and the overfitting problem occurs due to the insufficient dataset. We propose a modification of the Faster Region-based Convolutional Neural Network (FRCNN) for palm tree detection to reduce the overfitting problem and improve the detection accuracy. The enhanced FRCNN (EFRCNN) leads to improved performance for detecting objects (in the same image) when they are of multiple sizes by using a feature concatenation method. Transfer learning based on a ResNet50 model is used to extract the features of the input image. High-resolution images of oil palm trees from a drone are used to form the data set, containing mature, young, and mixed oil palm tree regions. We train and test the EFRCNN, the FRCNN, a CNN used recently for oil palm image detection, and two standard methods, namely, the support vector machine (SVM) and template matching (TM). The results reveal an overall accuracy of ≥96.8% for the EFRCNN on the three test sets. The accuracy is higher than the CNN and FRCNN and substantially higher than SVM and TM. For large-scale plantations, the accuracy improvement is significant. This research provides a method for automatically counting the oil palm trees in large-scale plantations

Experimental Study on Tribological and Leakage Characteristics of a Rotating Spring-Energized Seal under High and Low Temperature

A spring-energized seal, whose PTFE plastic shell has excellent self-lubrication and a low temperature stability, is used widely in liquid fuel valves’ rotating end-face seals. However, in practical application, temperature has a larger effect on not only the physical and tribological properties of materials, but also on the leakage performance of spring-energized rings. Thus, a high and low temperature sealing test of the spring-energized seal that applies to an engine was carried out. In this paper, the leakage characteristics, friction torque and wear characteristics of a spring-energized ring under different temperatures were studied. The friction torque at high temperature was less than that at normal temperature, and a low temperature could effectively reduce the wear amount of PTFE material. In order to study the influence of temperature on PTFE filled with graphite, the friction and wear test of PTFE-2 was carried out. The results showed that the amount of wear of PTFE-2 was only 27.8% of that at the normal temperature but the friction coefficient was three times larger when the temperature was −45 °C

Deep learning faster region-based convolutional neural network technique for oil palm tree counting

With the current development of image processing techniques, deep learning and machine learning methods have achieved tremendous performance specifically in aerial view image classification and detection. Deep learning convolutional neural network (CNN) has been known to be a state-of-art technique that produces high accuracy and efficiency of detection. Faster Region-Based Convolutional Neural Network (Faster RCNN) model is one of the detection methods that can be used in the field of aerial image classification specifically for high-resolution images from drones. In the oil palm tree counting, the traditional method of hand-crafted image processing is known to be computationally intensive and lack of generalization capability due to their highly dependent on the image appearance. Furthermore, the extracted features by the image processing method are only applicable and dependent on one application and need to be designed again for other different applications. In this paper, we propose a deep learning method of Faster RCNN for oil palm tree counting by using a pre-trained network ResNet50. The transfer learning model of ResNet50 then was trained again by the Faster RCNN network to get the weight for automatic oil palm tree counting. The proposed model is validated on the young, matured and mixed (young and matured) palm trees respectively, and we also compare the result with other machine learning methods of ANN and SVM. The Faster RCNN shows a promising result of oil palm tree counting where we achieved overall accuracy up to 97%