Fusion of Region Extraction and Cross-Entropy SVM Models for Wheat Rust Diseases Classification

Wheat rust diseases are one of the major types of fungal diseases that cause substantial yield quality losses of 15%–20%every year.The wheat rust diseases are identified either through experienced evaluators or computerassisted techniques.The experienced evaluators take time to identify the disease which is highly laborious and too costly.If wheat rust diseases are predicted at the development stages,then fungicides are sprayed earlier which helps to increase wheat yield quality.To solve the experienced evaluator issues,a combined region extraction and cross-entropy support vector machine(CE-SVM)model is proposed for wheat rust disease identification.In the proposed system,a total of 2300 secondary source images were augmented through flipping,cropping,and rotation techniques.The augmented images are preprocessed by histogram equalization.As a result,preprocessed images have been applied to region extraction convolutional neural networks(RCNN);Fast-RCNN,Faster-RCNN,and Mask-RCNN models for wheat plant patch extraction.Different layers of region extraction models construct a feature vector that is later passed to the CE-SVM model.As a result,the Gaussian kernel function in CE-SVM achieves high F1-score(88.43%)and accuracy(93.60%)for wheat stripe rust disease classification.

Knowledge based recognition of harbor target

A fast knowledge based recognition method of the harbor target in large gray remote-sensing image is presented. First, the distributed features and the inherent feature are analyzed according to the knowledge of harbor targets; then, two methods for extracting the candidate region of harbor are devised in accordance with different sizes of the harbors; after that, thresholds are used to segment the land and the sea with strategies of the segmentation error control; finally, harbor recognition is implemented according to its inherent character （semi-closed region of seawater）.

Electric Potential in Surface Produced Negative Ion Source with Magnetic Field Increasing Toward a Wall

Electric potential near a wall for plasma with the surface produced negative ions with magnetic field increasing toward a wall is investigated analytically. The potential profile is derived analytically by using a plasma-sheath equation, where negative ions produced on the plasma grid （PG） surface are considered in addition to positive ions and electrons. The potential profile depends on the amount and the temperature of the surface produced negative ions and the profile of the magnetic field. The negative potential peak is formed in the sheath region near the PG surface for the case of strong surface production of negative ions or low temperature negative ions. As the increase rate of the magnetic field near the wall becomes large, the negative potential peak becomes small.

Image based leaf segmentation and counting in rosette plants

This paper proposes an efficient method to extract the leaf region and count the number of leaves in digital plant images.The plant image analysis plays a significant role in viable and productive agriculture.It is used to record the plant growth,plant yield,chlorophyll fluorescence,plant width and tallness,leaf area,etc.frequently and accurately.Plant growth is a major character to be analyzed among these plant characters and it directly depends on the number of leaves in the plants.In this paper,a new method is presented for leaf region extraction from plant images and counting the number of leaves.The proposed method has three steps.The first step involves a new statistical based technique for image enhancement.The second step involves in the extraction of leaf region in plant image using a graph based method.The third step involves in counting the number of leaves in the plant image by applying Circular Hough Transform(CHT).The proposed work has been experimented on benchmark datasets of Leaf Segmentation Challenge(LSC).The proposed method achieves the segmentation accuracy of 95.4%and it also achieves the counting accuracy of
0.7(DiC)and 2.3(|DiC|)for datasets(A1,A2 and A3),which are better than the state-of-the-art methods.

APFD:an effective approach to taxi route recommendation with mobile trajectory big data

With the rapid development of data-driven intelligent transportation systems,an efficient route recommendation method for taxis has become a hot topic in smart cities.We present an effective taxi route recommendation approach(called APFD)based on the artificial potential field(APF)method and Dijkstra method with mobile trajectory big data.Specifically,to improve the efficiency of route recommendation,we propose a region extraction method that searches for a region including the optimal route through the origin and destination coordinates.Then,based on the APF method,we put forward an effective approach for removing redundant nodes.Finally,we employ the Dijkstra method to determine the optimal route recommendation.In particular,the APFD approach is applied to a simulation map and the real-world road network on the Fourth Ring Road in Beijing.On the map,we randomly select 20 pairs of origin and destination coordinates and use APFD with the ant colony(AC)algorithm,greedy algorithm(A*),APF,rapid-exploration random tree(RRT),non-dominated sorting genetic algorithm-II(NSGA-II),particle swarm optimization(PSO),and Dijkstra for the shortest route recommendation.Compared with AC,A*,APF,RRT,NSGA-II,and PSO,concerning shortest route planning,APFD improves route planning capability by 1.45%–39.56%,4.64%–54.75%,8.59%–37.25%,5.06%–45.34%,0.94%–20.40%,and 2.43%–38.31%,respectively.Compared with Dijkstra,the performance of APFD is improved by 1.03–27.75 times in terms of the execution efficiency.In addition,in the real-world road network,on the Fourth Ring Road in Beijing,the ability of APFD to recommend the shortest route is better than those of AC,A*,APF,RRT,NSGA-II,and PSO,and the execution efficiency of APFD is higher than that of the Dijkstra method.

Oenanthe Javanica Extract Protects Against Experimentally Induced Ischemic Neuronal Damage via its Antioxidant Effects

Background： Water dropwort （Oenanthejavanica） as a popular traditional medicine in Asia shows various biological properties including antioxidant activity. In this study, we firstly examined the neuroprotective effect of Oenanthejavanica extract （OJE） in the hippocampal comus ammonis 1 region （CA 1 region） of the gerbil subjected to transient cerebral ischemia. Methods： Gerbils were established by the occlusion of common carotid arteries for 5 min. The neuroprotective effect of OJE was estimated by cresyl violet staining. In addition, 4 antioxidants （copper, zinc superoxide dismutase [SOD], manganese SOD, catalase, and glutathione peroxidase） immunoreactivities were investigated by immunohistochemistry. Results： Pyramidal neurons in the CA1 region showed neuronal death at 5 days postischemia; at this point in time, all antioxidants immunoreactivities disappeared in CA1 pyramidal neurons and showed 100 mg/kg, OJE protected CA 1 pyramidal neurons from ischemic damage in many nonpyramidal cells. Treatment with 200 mg/kg, not In addition, 200 mg/kg OJE treatment increased or maintained antioxidants immunoreactivities. Especially, among the antioxidants, glutathione peroxidase immunoreactivity was effectively increased in the CA 1 pyramidal neurons of the OJE-treated sham-operated and ischemia-operated groups. Conclusion： Our present results indicate that treatment with OJE can protect neurons from transient ischemic damage and that the neuroprotective effect may be closely associated with increased or maintained intracellular antioxidant enzymes by OJE.