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Author

Wang Xiangyu

Bio: Wang Xiangyu is an academic researcher. The author has contributed to research in topics: Warning system & Plant disease. The author has an hindex of 2, co-authored 6 publications receiving 19 citations.

Papers
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Patent
27 Aug 2014
TL;DR: The early warning method and system of facility spinach diseases can carry out early warning of various diseases simply and effectively as mentioned in this paper, and the early warning level of the current environment is obtained, if the early-warning level is zero, the S1 is executed, and otherwise the S5 is executed; S5, the corresponding level warning function is executed according to the earlywarning level; S6, warning is released and the S 1 is executed
Abstract: The invention provides an early-warning method and system of facility spinach diseases The method includes the steps that S1, air temperature and humidity sensors and soil temperature and humidity sensors are deployed in a greenhouse, and the deployed sensors collect environment information data of facility vegetables according to the preset time interval, wherein the information data include the air temperature, the air humidity, the soil temperature and the soil humidity; S2, the collected environment information data are processed; S3, a classification early-warning model is built based on SVM; S4, the environment information data processed in the S2 are analyzed through the classification early-warning model built in the S3, the early-warning level of the current environment is obtained, if the early-warning level is zero, the S1 is executed, and otherwise the S5 is executed; S5, the corresponding level warning function is executed according to the early-warning level; S6, warning is released and the S1 is executed The early-warning method and system of the facility spinach diseases can carry out early warning of various diseases simply and effectively

10 citations

Journal ArticleDOI
TL;DR: A new Otsu algorithm based on three-dimensional histogram reconstruction and dimension reduction to segment powdery mildew from cucumber disease images is proposed and reduced the algorithm dimension to save time and spatial complexity and achieved the desired results.

5 citations

Patent
27 May 2015
TL;DR: In this article, a greenhouse lettuce disease early warning method and device is presented, where the early warning includes timely early warning, time integration early warning and short-term early warning.
Abstract: The invention relates to the technical field of vegetable disease prevention, in particular to a greenhouse lettuce disease early warning method and device. According to the greenhouse lettuce disease early warning method and device provided by the invention, the infestation time is determined in different environments according to an environmental temperature-humidity threshold on which lettuce diseases tend to occur and different pathogens based on a plant disease triangle principle, and early warning includes timely early warning, time integration early warning and short-term early warning. A better effect is achieved in comparison to a single early warning method, the influences of pathogens on the lettuce at the contact period, invasion period, incubation period and disease period can be comprehensively considered respectively, and early warning results are more reliable. Meanwhile, peasant households can understand and grasp conveniently, and convenience is brought to popularization and application.

2 citations

Patent
19 Apr 2017
TL;DR: In this article, a spectrum technology-based facility cucumber disease early warning method and a device were proposed to increase timeliness and accuracy of cucumber early warning, and guiding farmers to take corresponding disease prevention measures in time.
Abstract: The invention relates to a spectrum technology-based facility cucumber disease early warning method, and a device. The spectrum technology-based facility cucumber disease early warning method comprises following steps: spectral information of sample facility cucumber is collected; principal component analysis is adopted to process the collected spectral information of sample facility cucumber; a neural network-based facility cucumber disease early warning model is established based on the processing results of the collected spectral information of sample facility cucumber; spectral information of target facility cucumber is collected; the spectral information of target facility cucumber is subjected to disease discrimination based on the neural network-based facility cucumber disease early warning model, and disease early warning of target facility cucumber is carried out based on obtained discrimination results. The spectrum technology-based facility cucumber disease early warning method and the device are capable of increasing timeliness and accuracy of cucumber disease early warning, and guiding farmers to take corresponding disease prevention measures in time.

2 citations

Patent
02 Feb 2018
TL;DR: In this article, the utility model discloses a pick end effector of robot, which includes: frame and rack-mounted power take-off, power transfer device and tail end executing device, wherein the first end and the supports active connections of lead screw, the output shaft with power take off is held to thesecond, the screwnut cover is established on the lead screw and the guide arm is fixed on screwnut, and the push rod is connected with screw-nut, tail end execution device includes: scissors andconnecting rod, the rotation fulcrum of scissors is fixed
Abstract: The utility model discloses a pick end effector of robot. This pick end effector of robot includes: frame and rack -mounted power take -off, power transfer device and tail end executing device, wherein: power transfer device include: lead screw, screw -nut, guide arm and push rod, the first end and the supports active connections of lead screw, the output shaft with power take -off is held to thesecond, the screw -nut cover is established on the lead screw, and the guide arm is fixed on screw -nut, and the push rod is connected with screw -nut, tail end executing device includes: scissors andconnecting rod, the rotation fulcrum of scissors is fixed on the support, the connecting rod respectively with the scissors cut the handle and the push rod is connected. This simple structure part isfew, and it is convenient to dismantle, has made things convenient for the user to use greatly and has reduced user use cost, but also had the flexibility of operation of harvesting and pick efficient advantage.

1 citations


Cited by
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Journal ArticleDOI
Archana Jain1, Surendra Sarsaiya1, Qin Wu1, Yuan-Fu Lu1, Jingshan Shi1 
TL;DR: It is determined that the advent of emergent fungal leaf diseases is closely connected to environmental speciation, and their distribution, virulence, incidence, and severity could be attenuated.
Abstract: There is increasing difficulty in identifying new plant leaf diseases as a result of environmental change. There is a need to identify the factors influencing the emergence and the increasing incidences of these diseases. Here, we present emerging fungal plant leaf diseases and describe their environmental speciation. We considered the factors controlling for local adaptation associated with environmental speciation. We determined that the advent of emergent fungal leaf diseases is closely connected to environmental speciation. Fungal pathogens targeting the leaves may adversely affect the entire plant body. To mitigate the injury caused by these pathogens, it is necessary to be able to detect and identify them early in the infection process. In this way, their distribution, virulence, incidence, and severity could be attenuated.

118 citations

Journal ArticleDOI
TL;DR: This article is primarily focusing on a cucumber leaf diseases detection and classification method, which is comprised of five stages including image enhancement, infected spots segmentation, deep features extraction, feature selection, and finally classification.
Abstract: In the agriculture farming business, weeds, pests, and other plant diseases are the major reason for monetary misfortunes around the globe. It is an imperative factor, as it causes a significant diminution in both quality and capacity of crop growing. Therefore, detection and taxonomy of various plants diseases are crucial, and it demands utmost attention. However, this loss can be minimized by detecting crops diseases at their earlier stages. In this article, we are primarily focusing on a cucumber leaf diseases detection and classification method, which is comprised of five stages including image enhancement, infected spots segmentation, deep features extraction, feature selection, and finally classification. Image enhancement is performed as a pre-processing step, which efficiently improves the local contrast and makes infected regions more visible, which is later segmented with a novel Sharif saliency-based (SHSB) method. The segmentation results are further improved by fusing active contour segmentation and proposed saliency method. This step is much important for correct and useful feature extraction. In this work, pre-trained models- VGG-19 & VGG-M are utilized for features extraction and later select the most prominent features based on three selected parameters - local entropy, local standard deviation, and local interquartile range method. These refined features are finally fed to multi-class support vector machine for diseases identification. To prove the authenticity of the proposed algorithm, five cucumber leaf diseases are considered and classified to achieve classification accuracy of 98.08% in 10.52 seconds. Additionally, the proposed method is also compared with the recent techniques so as to prove its authenticity.

55 citations

Patent
06 Jul 2016
TL;DR: In this paper, a greenhouse vegetable environmental parameter data fusion method, a data fusion device, and data fusion system is presented. But the method does not consider the influence of the arrangement positions of the sensors on the greenhouse integrated environmental parameters.
Abstract: The invention provides a greenhouse vegetable environmental parameter data fusion method, a data fusion device, and a data fusion system. The greenhouse vegetable environmental parameter data fusion method is characterized in that at least one environmental parameter value of greenhouse vegetables on various preset positions in a greenhouse at different moments can be acquired; various environmental parameter values at the same moment can be used to form environmental parameter sequences, and various environmental parameter sequences at different moments can be acquired; the weighting fusion calculation of the environmental parameter sequences can be carried out to acquire the fusion value of various environmental parameter sequences at different moments. By adopting the greenhouse vegetable environmental parameter data fusion method, the data fusion device, and the data fusion system, the technical problems of the prior art of the inaccurate monitoring of the greenhouse integrated environmental parameters caused by the lack of consideration of the influence of the arrangement positions of the sensors on the greenhouse integrated environmental parameters can be solved.

7 citations

Journal ArticleDOI
TL;DR: In this paper, the authors used close-range multispectral imagery over cucumber plants inside a commercial greenhouse to detect powdery mildew due to Podosphaera xanthii.
Abstract: This study used close-range multispectral imagery over cucumber plants inside a commercial greenhouse to detect powdery mildew due to Podosphaera xanthii. It was collected using a MicaSense® RedEdge camera at 1.5 m over the top of the plant. Image registration was performed using Speeded-Up Robust Features (SURF) with an affine geometric transformation. The image background was removed using a binary mask created with the aligned NIR band of each image, and the illumination was corrected using Cheng et al.’s algorithm. Different features were computed, including RGB, image reflectance values, and several vegetation indices. For each feature, a fine Gaussian Support Vector Machines algorithm was trained and validated to classify healthy and infected pixels. The data set to train and validate the SVM was composed of 1000 healthy and 1000 infected pixels, split 70–30% into training and validation datasets, respectively. The overall validation accuracy was 89, 73, 82, 51, and 48%, respectively, for blue, green, red, red-edge, and NIR band image. With the RGB images, we obtained an overall validation accuracy of 89%, while the best vegetation index image was the PMVI-2 image which produced an overall accuracy of 81%. Using the five bands together, overall accuracy dropped from 99% in the training to 57% in the validation dataset. While the results of this work are promising, further research should be considered to increase the number of images to achieve better training and validation datasets.

7 citations

Patent
31 May 2017
TL;DR: In this article, a method for predicting plant diseases and insect pests in planting equipment is proposed, which comprises the following steps of: determining the type of a target plant growing in the planting equipment; obtaining current growth environmental data of the target plant; respectively comparing the growth environmental datasets of the targeted plant with environmental data corresponding to each type of diseases and pests in a pre-established disease and insect pest model library, so that a comparison result is obtained, wherein the disease and pest model libraries correspond to the specific type of target plants; and predicting whether the target plants has diseases and
Abstract: The invention discloses a method for predicting plant diseases and insect pests in planting equipment. The method comprises the following steps of: determining the type of a target plant growing in the planting equipment; obtaining current growth environmental data of the target plant; respectively comparing the growth environmental data of the target plant with environmental data corresponding to each type of diseases and insect pests in a pre-established disease and insect pest model library, so that a comparison result is obtained, wherein the disease and insect pest model library corresponds to the type of the target plant; and predicting whether the target plant has diseases and insect pests or not in the next time period according to the comparison result. By means of the technical scheme provided in the embodiment of the invention, whether the target plant has diseases and insect pests or not in the next time period can be predicted more accurately; therefore, corresponding measures can be conveniently adopted in time by the planting equipment or users; loss due to diseases and insect pests can be reduced; and the user experience is improved. The invention further discloses a device for predicting plant diseases and insect pests in the planting equipment; and the device has the corresponding technological effect.

5 citations