A Crop Pests Image Classification Algorithm Based on Deep Convolutional Neural Network

Conventional pests image classification methods may not be accurate due to the complex farmland background, sunlight and pest gestures. To raise the accuracy, the deep convolutional neural network (DCNN), a concept from Deep Learning, was used in this study to classify crop pests image. On the ground of our experiments, in which LeNet-5 and AlexNet were used to classify pests image, we have analyzed the effects of both convolution kernel and the number of layers on the network, and redesigned the structure of convolutional neural network for crop pests. Further more, 82 common pest types have been classified, with the accuracy reaching 91%. The comparison to conventional classification methods proves that our method is not only feasible but preeminent

Rosmarinic Acid Alleviates the Endothelial Dysfunction Induced by Hydrogen Peroxide in Rat Aortic Rings via Activation of AMPK

Endothelial dysfunction is the key player in the development and progression of vascular events. Oxidative stress is involved in endothelial injury. Rosmarinic acid (RA) is a natural polyphenol with antioxidative, antiapoptotic, and anti-inflammatory properties. The present study investigates the protective effect of RA on endothelial dysfunction induced by hydrogen peroxide (H2O2). Compared with endothelium-denuded aortic rings, the endothelium significantly alleviated the decrease of vasoconstrictive reactivity to PE and KCl induced by H2O2. H2O2 pretreatment significantly injured the vasodilative reactivity to ACh in endothelium-intact aortic rings in a concentration-dependent manner. RA individual pretreatment had no obvious effect on the vasoconstrictive reaction to PE and KCl, while its cotreatment obviously mitigated the endothelium-dependent relaxation impairments and the oxidative stress induced by H2O2. The RA cotreatment reversed the downregulation of AMPK and eNOS phosphorylation induced by H2O2 in HAEC cells. The pretreatment with the inhibitors of AMPK (compound C) and eNOS (L-NAME) wiped off RA’s beneficial effects. All these results demonstrated that RA attenuated the endothelial dysfunction induced by oxidative stress by activating the AMPK/eNOS pathway

Prior knowledge auxiliary for few-shot pest detection in the wild

One of the main techniques in smart plant protection is pest detection using deep learning technology, which is convenient, cost-effective, and responsive. However, existing deep-learning-based methods can detect only over a dozen common types of bulk agricultural pests in structured environments. Also, such methods generally require large-scale well-labeled pest data sets for their base-class training and novel-class fine-tuning, and these significantly hinder the further promotion of deep convolutional neural network approaches in pest detection for economic crops, forestry, and emergent invasive pests. In this paper, a few-shot pest detection network is introduced to detect rarely collected pest species in natural scenarios. Firstly, a prior-knowledge auxiliary architecture for few-shot pest detection in the wild is presented. Secondly, a hierarchical few-shot pest detection data set has been built in the wild in China over the past few years. Thirdly, a pest ontology relation module is proposed to combine insect taxonomy and inter-image similarity information. Several experiments are presented according to a standard few-shot detection protocol, and the presented model achieves comparable performance to several representative few-shot detection algorithms in terms of both mean average precision (mAP) and mean average recall (mAR). The results show the promising effectiveness of the proposed few-shot detection architecture

Applications of Internet of Things in the Facility Agriculture

International audienceIt is a trend to use information technology to lead the development of modern agriculture. The IntelliSense Internet of Things will be an important support for intensive, high-yield, high-quality, efficient, ecological security agriculture. In this paper, we give solutions and key technologies of facilities agriculture based on the Internet of Things technology. On this basis it designs and implements facility cultivation greenhouses. Practice has proved that the Internet of Things is the development of modern agriculture productivity. It has an important significance in raising the level of agricultural development, improving the overall efficiency of agriculture, promoting the upgrade of modern agricultural transformation

Structure and Doping Optimization of IDT-Based Copolymers for Thermoelectrics

π-conjugated backbones play a fundamental role in determining the thermoelectric (TE) properties of organic semiconductors. Understanding the relationship between the structure–property–function can help us screen valuable materials. In this study, we designed and synthesized a series of conjugated copolymers (P1, P2, and P3) based on an indacenodithiophene (IDT) building block. A copolymer (P3) with an alternating donor–acceptor (D-A) structure exhibits a narrower band gap and higher carrier mobility, which may be due to the D-A structure that helps reduce the charge carrier transport obstacles. In the end, its power factor reaches 4.91 μW m−1 K−2 at room temperature after doping, which is superior to those of non-D-A IDT-based copolymers (P1 and P2). These results indicate that moderate adjustment of the polymer backbone is an effective way to improve the TE properties of copolymers

Insect Detection and Classification Based on an Improved Convolutional Neural Network

Regarding the growth of crops, one of the important factors affecting crop yield is insect disasters. Since most insect species are extremely similar, insect detection on field crops, such as rice, soybean and other crops, is more challenging than generic object detection. Presently, distinguishing insects in crop fields mainly relies on manual classification, but this is an extremely time-consuming and expensive process. This work proposes a convolutional neural network model to solve the problem of multi-classification of crop insects. The model can make full use of the advantages of the neural network to comprehensively extract multifaceted insect features. During the regional proposal stage, the Region Proposal Network is adopted rather than a traditional selective search technique to generate a smaller number of proposal windows, which is especially important for improving prediction accuracy and accelerating computations. Experimental results show that the proposed method achieves a heightened accuracy and is superior to the state-of-the-art traditional insect classification algorithms

Robust Image Segmentation Using LBP Embedded Region Merging

This paper is under in-depth investigation due to suspicion of possible plagiarism on a high similarity index. The region merging algorithm by the maximal similarity with color histogram has been applied successfully in color image segmentation. However, only using color histogram information is not sufficient and effective for superior segmentation. We propose a novel color image segmentation method based on region merging by using joint color-texture histogram in this paper. The proposed method incorporates both color histogram and texture histogram information to measure the similarity of different regions and thus to guide the region merging process. Experiments show that our method is more accurate and robust than traditional image segmentation methods based on region merging

Multiple instance learning tracking method with local sparse representation

When objects undergo large pose change, illumination variation or partial occlusion, most existed visual tracking algorithms tend to drift away from targets and even fail in tracking them. To address this issue, in this study, the authors propose an online algorithm by combining multiple instance learning (MIL) and local sparse representation for tracking an object in a video system. The key idea in our method is to model the appearance of an object by local sparse codes that can be formed as training data for the MIL framework. First, local image patches of a target object are represented as sparse codes with an overcomplete dictionary, where the adaptive representation can be helpful in overcoming partial occlusion in object tracking. Then MIL learns the sparse codes by a classifier to discriminate the target from the background. Finally, results from the trained classifier are input into a particle filter framework to sequentially estimate the target state over time in visual tracking. In addition, to decrease the visual drift because of the accumulative errors when updating the dictionary and classifier, a two‐step object tracking method combining a static MIL classifier with a dynamical MIL classifier is proposed. Experiments on some publicly available benchmarks of video sequences show that our proposed tracker is more robust and effective than others

Visual Feature Learning on Video Object and Human Action Detection: A Systematic Review

Video object and human action detection are applied in many fields, such as video surveillance, face recognition, etc. Video object detection includes object classification and object location within the frame. Human action recognition is the detection of human actions. Usually, video detection is more challenging than image detection, since video frames are often more blurry than images. Moreover, video detection often has other difficulties, such as video defocus, motion blur, part occlusion, etc. Nowadays, the video detection technology is able to implement real-time detection, or high-accurate detection of blurry video frames. In this paper, various video object and human action detection approaches are reviewed and discussed, many of them have performed state-of-the-art results. We mainly review and discuss the classic video detection methods with supervised learning. In addition, the frequently-used video object detection and human action recognition datasets are reviewed. Finally, a summarization of the video detection is represented, e.g., the video object and human action detection methods could be classified into frame-by-frame (frame-based) detection, extracting-key-frame detection and using-temporal-information detection; the methods of utilizing temporal information of adjacent video frames are mainly the optical flow method, Long Short-Term Memory and convolution among adjacent frames