The invention discloses an environmental-friendly agricultural safe making device of straw feeds. The environmental-friendly agricultural safe making device of straw feeds comprises a feeding pipe, acleaning tank, a sieve plate, vibration exciters, a water tank, a spraying device, a rotating roller, rolling-over cutters, a compounding tank, a dryer, crushing rollers, crushing teeth, a filter plate and a discharge pipe, wherein the feeding pipe is arranged at the top end of the cleaning tank; a rotating drum is arranged in the cleaning tank; moving rakes are uniformly arranged on the rotatingdrum; the sieve plate is arranged in the cleaning tank in an inclined manner; the rotating roller is arranged above the lowest end of the sieve plate; the rolling-over cutters are uniformly arranged along the outer wall of the rotating roller; the rotating roller is connected with an external motor; the water tank is mounted on the cleaning tank; the side wall of the cleaning tank is connected with the compounding tank through a material conveying pipe; the dryer is mounted at the top end of the compounding tank; a plurality of groups of crushing rollers are arranged in the compounding tank; the crushing teeth are uniformly arranged on the crushing rollers; and the filter plate is arranged in the compounding tank. According to the environmental-friendly agricultural safe making device of straw feeds disclosed by the invention, the sand, the dust and the like on straw are washed off; the safety of the subsequent making of the straw feeds is facilitated; and the straw is made into feeds,so that resources are saved and waste is avoided.

Environmental-friendly agricultural safe making device of straw feeds

In this paper, according to the characteristics of maize disease knowledge, OWL DL language was used to build maize diseases ontology, and the reasoning rule of maize diseases was defined by using the expressive ability of SWRL rule language.The author introduced several realizable reasoning functions,and achieved the diagnostic reasoning of maize disease knowledge by Jess inference engine.The results indicated that constructing the maize diseases ontology,and introducing SWRL rule into maize disease ontology provided an effective way for the construction of high-intelligent, shareable and reused maize disease knowledge database and diagnostic rule database.

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Research on Construction and SWRL Reasoning of Ontology of Maize Diseases

Tendency to pursue graduate study has become a positive trend in Indonesia. This is supported by the growing number of agency or institution that organizes a postgraduate scholarship program with a wide range of requirements. Generally, finding information through the Internet is done by a keyword-based search engine which possibly gives several irrelevant results and relies on user's ability in selecting keywords. This research aims to provide an alternative by building semantic search system for finding scholarship information that allows natural language sentence instead of keywords. It implements rule reasoning to obtain implicit knowledge by defining the rules from basic knowledge that explicitly defined. Among the rules that are used in this system, data range restriction is used to determine data based on certain thresholds. The experiment results show that the system has recall rate 98.51% and precision rate 100% from 90 of 105 input sentences that are able to be processed by the system.

Semantic search with rule reasoning for scholarship information search

The invention discloses non-dust straw smashing equipment for intelligent agriculture The non-dust straw smashing equipment for the intelligent agriculture structurally comprises a sealing hood, a storage tank, a material transfer cavity, a dust suction fan, a first material inlet, a second material inlet, a non-dust smashing cavity, a wiring box, a base and a straw smasher body, wherein the bottom of the straw smasher body is movably connected with a rack, and the straw smasher body is connected to the base through the rack and is perpendicular to the base According to the non-dust straw smashing equipment for the intelligent agriculture, the optimal jet angle is achieved by moving a water sprayer up and down on a sliding track, dust generated after materials are smashed can be sucked and collected into a dust inlet trough by a one-position dust collecting electrode and a two-position dust collecting electrode in the water flowing direction, and the sucked dust is guided into a dustfalling trough through a slope and an inner groove; meanwhile, the inner groove and a dust guide rotary wheel adopt the design of compact convex points so that dust adhesion can be avoided, and dustis collected into a dust collecting hopper in a large range; and a current limiting bent plate limits the flowing direction of water so that the situation that too much water exists and affects the use effect of the dust collecting electrodes can be avoided

Non-dust straw smashing equipment for intelligent agriculture

Soil fertility is the comprehensive reflection of related factors and the related factors. Soil fertility evaluation knowledge is stored by relational database as usually, and it is difficult to show the correlation and constraints among attributes .In this paper, Nongan county farmland productivity data is as the research object, Using rough set approach to do attribute reduction, using ontology method to establish the soil fertility level knowledge base, using multi Agent technology to implement the prototype system, and complete the reuse and sharing of knowledge, lay the foundation for semantic level reasoning.

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Research on the Construction and Implementation of Soil Fertility Knowledge Based on Ontology

The utility model discloses a dustless crushing apparatus of straw that wisdom agricultural was used, the power distribution box comprises a box body, the surface front end fixed mounting of box has first switch, second switch, third switch and storage water tank, the interior subordinate fixed surface of box is connected with the collecting vat, the last fixed surface of collecting vat installs crushing case, the lower surface of smashing the case is equipped with the feed opening, the inside fixed surface who smashes the case is connected with fixed blade, the inside rotation of smashing the case is connected with rod for crushing, rod for crushing's outer fixed surface installs movable blade, the upper surface of smashing the case cuts off the case through connector fixed mounting, the inside that cuts off the case is rotated to be connected with and is cut broken rod through cutting off the axle, the outer fixed surface who cuts broken rod is connected with the cutting off cutter This dustless crushing apparatus of straw that wisdom agricultural was used cuts off the procedure through adding together, not only can improve work efficiency greatly, can also guarantee to smash the effect, keeps good operational environment

Dustless crushing apparatus of straw that wisdom agricultural was used

Semantic not consistency, and knowledge base is difficult to reuse and sharing are the key problems affecting the system development and application. This paper studies how to express the soil fertility level information using of the ontology and generate OWL (Ontology Web Language) document, and how to make use of SWRL (Semantic Web Rule Language) to express inference rules. On this basis, this paper integrates SWRL rules editor and JESS (java expert shell system) rules engine, establishes the reasoning framework based on JESS reasoning engine, and realizes the productivity grade evaluation based on ontology and SWRL.

https://hal.inria.fr/hal-01351836/document

The Knowledge Representation and Semantic Reasoning Realization of Productivity Grade Based on Ontology and SWRL

Highlights Modify the U-Net segmentation network to reduce the loss of segmentation accuracy. Reducing the number of layers U-net network, modifying the loss function, and the increase in the output layer dropout. It can be well extracted after splitting blade morphological model and color feature. Abstract. From the perspective of computer vision, the shortcut to extract phenotypic information from a single crop in the field is image segmentation. Plant segmentation is affected by the background environment and illumination. Using deep learning technology to combine depth maps with multi-view images can achieve high-throughput image processing. This article proposes an improved U-Net segmentation network, based on small sample data enhancement, and reconstructs the U-Net model by optimizing the model framework, activation function and loss function. It is used to realize automatic segmentation of plant leaf images and extract relevant feature parameters. Experimental results show that the improved model can provide reliable segmentation results under different leaf sizes, different lighting conditions, different backgrounds, and different plant leaves. The pixel-by-pixel segmentation accuracy reaches 0.94. Compared with traditional methods, this network achieves robust and high-throughput image segmentation. This method is expected to provide key technical support and practical tools for top-view image processing, Unmanned Aerial Vehicle phenotype extraction, and phenotype field platforms.

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Plant Leaf Segmentation and Phenotypic Analysis Based on Fully Convolutional Neural Network

This paper adopts statistical learning theory and optimization theory to the analysis of the algorithm theory, probe into its theoretical foundation. The existing theoretical analysis on the basis of the establishment of clustering model algorithm design, code realization and finally a lot of different data set of test, choose soil data as a test database, will be in the database on a large number of data mining experiment to verify the performance of the proposed algorithm. The test result feedback back will further deepen the theoretical research or correct theory already mistakes, new theory and will continue to guide experiments, both mutual promoting common development.

Unsupervised Multi-Scale Fuzzy Clustering Algorithm Application in the Evaluation of Soil Fertility

according to the characteristics of current higher education, the status quo and the network development, the development of the MOOC course has been imminent, online curriculum resources by the user is completely open, self-organization, has been generally recognized learners and teachers in the world. In the current domestic micro class prevails, and micro situation course resources website under the less, taking MOOC courses construction design concept of micro class website, is beneficial to the popularization and development of the application of micro class. This article absorbs the characteristics of MOOC course construction, design for the construction of colleges and universities are open, flexible, interactive strong comprehensive function of educational technology resources website.

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Guifen Chen

Papers

Dustless crushing apparatus of straw that wisdom agricultural was used

The Knowledge Representation and Semantic Reasoning Realization of Productivity Grade Based on Ontology and SWRL

Plant Leaf Segmentation and Phenotypic Analysis Based on Fully Convolutional Neural Network

Unsupervised Multi-Scale Fuzzy Clustering Algorithm Application in the Evaluation of Soil Fertility

The construction of resource of educational technology program design based on MOOC