Prediction of Hot Topics of Agricultural Public Opinion Based on Attention Mechanism LSTM Model

In order to timely and accurately analyze the focus and appeal of public opinion on the Internet, A LSTM-ATTN model was proposed to extract the hot topics and predict their changing trend based on tens of thousands of news and commentary messages. First, an improved LDA model was used to extract hot words and classify the hot topics. Aimed to more accurately describe the detailed characteristics and long-term trend of topic popularity, a prediction model is proposed based on attention mechanism Long Short-Term Memory (LSTM) network, which named LSTM-ATTN model. A large number of numerical experiments were carried out using the public opinion information of "African classical swine fever" event in China. According to results of evaluation indexes, the relative superiority of LSTM-ATTN model was demonstrated. It can capture and reflect the inherent characteristics and periodic fluctuations of the agricultural public opinion information. Also, it has higher convergence efficiency and prediction accuracy.

Research on Multi-cooperative Combine Integrated Scheduling Based on Improved NSGA-II Algorithm

To promote the integration and optimal allocation of agricultural machinery resources to achieve the purpose of reducing cost and increasing efficiency, the scheduling problem of agricultural machinery in agricultural machinery cooperatives based on the trans-regional operation mode was studied in this paper, Considering multiple agricultural machinery points, multiple types, operation time windows, space distance and other factors, the multi-objective programming mathematical model with the lowest total cost of deployment, the highest service punctuality and the least use of harvester was established by applying path optimization and theory of job shop scheduling. NSGA-II was used to solve the model in this paper. According to the model features, this paper designed chromosome coding and the process of emergence, crossover and variation of initial population. Combined with the actual situation of rice harvesting in Wuchang City, the above scheduling theory was applied. The experimental results showed the validity and feasibility of the scheduling model and the algorithm.

OntoCSA

The modelling of agriculture with ontologies has been of interest to many authors in the past years. However, no research, currently, has focused on building a knowledge base ontology for the Climate Smart Agriculture (CSA) domain. This study attempts to fill this gap through the development of a Climate Smart Agriculture Ontology (OntoCSA). Information was gathered from secondary sources including websites, published research articles and reports as well as related ontologies, to formalize the OntoCSA ontology in Description Logics (DLs). The OntoCSA ontology was developed in Web Ontology Language (OWL) with Protégé. Furthermore, the OntoCSA ontology was successfully validated with the HermiT reasoner within Protégé. The resulting OntoCSA ontology is a machine-readable model of CSA that can be leveraged in web-based applications for the storage, open and automated access and sharing of CSA information/data, for research and dissemination of best practices

Prediction of Soybean Price Trend via a Synthesis Method With Multistage Model

Soybean is an important crop, so it is very important to forecast soybean price trend, which can stabilize the market. This paper presents a Synthesis Method with Multistage Model (SMwMM) in order to identify and forecast soybean price trend in China. In the previous work,Toeplitz Inverse Covariance-based Clustering(TICC) has been applied to cluster the prices of four variables. The research have found that there are four patterns in soybean market price, which could be explained by economic theory. This paper consider four patterns as market risk levels. Based on the clustering results, we used Long short-term memory(LSTM) to forecast the prices of these four variables. Multivariate long short-term memory(MLSTM) is then used to classify soybean price to determine level of risk . Experimental results show that :(1)The LSTM model has achieved great fitting effect and high prediction accuracy;(2) The performance of MLSTM-FCN and MALSTM-FCN is better than that of LSTM-FCN and ALSTM-FCN. Furthermore,MALSTM-FCN had the higher accuracy than MLSTM-FCN, which reached 76.39%.

LLC Converter Based PV Water Pumping System With Enhanced User Safety Approach

In this paper, LLC resonant converter based Photovoltaic (PV) water pumping is proposed. Commercially, the available PV based water pumping system consists of a non-isolated DC-DC converter, which is suitable for low power applications but results in a less safe operating environment for human. In the case of PV based water pumping systems, the safety of humans should be the main concern, as these systems are normally being operated by farmers, their families and it may possible that they are not that much aware of operational hazards. Therefore, this paper attempts to present an LLC converter based PV water pumping system, considering human safety as a major concern. The proposed system is simulated in MATLABenvironment and results shows that proposed system configuration has no adverse impact on system efficiency and it enhances safety for operating personals. Presented results can be further exploited for hardware verification as the future scope of this work.

Identification of Tomato Leaf Diseases Using Deep Convolutional Neural Networks

Crop disease is a major issue now days; as it drastically reduces food production rate. Tomato is cultivated in major part of the world. The most common diseases that affect tomato crops are bacterial spot, early blight, septoria leaf spot, late blight, leaf mold, target spot, etc. In order to increase the production rate of tomato, early identification of diseases is highly required. The existing work contains very less accurate system for identification of tomato crop diseases. The goal of our work is to propose cost effective and efficient deep learning model inspired from Alexnet for identification of tomato crop diseases. To validate the performance of proposed model, experiments have also been done on standard pretrained models. The plantVillage dataset is used for the same, which contains 18,160 images of diseased and non-diseased tomato leaf. The disease identification accuracy of proposed model is compared with standard pretrained models and found that proposed model gave more promising results for tomato crop diseases identification.

LLC Converter Based PV Water Pumping System with Enhanced User Safety Approach

In this paper, LLC resonant converter based Photovoltaic (PV) water pumping is proposed. Commercially, the available PV based water pumping system consists of a non-isolated DC-DC converter, which is suitable for low power applications but results in a less safe operating environment for human. In the case of PV based water pumping systems, the safety of humans should be the main concern, as these systems are normally being operated by farmers, their families and it may possible that they are not that much aware of operational hazards. Therefore, this paper attempts to present an LLC converter based PV water pumping system, considering human safety as a major concern. The proposed system is simulated in MATLABenvironment and results shows that proposed system configuration has no adverse impact on system efficiency and it enhances safety for operating personals. Presented results can be further exploited for hardware verification as the future scope of this work.

Prediction of Soybean Price Trend via a Synthesis Method with Multistage Model

Soybean is an important crop, so it is very important to forecast soybean price trend, which can stabilize the market. This paper presents a Synthesis Method with Multistage Model (SMwMM) in order to identify and forecast soybean price trend in China. In the previous work,Toeplitz Inverse Covariance-based Clustering(TICC) has been applied to cluster the prices of four variables. The research have found that there are four patterns in soybean market price, which could be explained by economic theory. This paper consider four patterns as market risk levels. Based on the clustering results, we used Long short-term memory(LSTM) to forecast the prices of these four variables. Multivariate long short-term memory(MLSTM) is then used to classify soybean price to determine level of risk . Experimental results show that :(1)The LSTM model has achieved great fitting effect and high prediction accuracy;(2) The performance of MLSTM-FCN and MALSTM-FCN is better than that of LSTM-FCN and ALSTM-FCN. Furthermore,MALSTM-FCN had the higher accuracy than MLSTM-FCN, which reached 76.39%.

Rice Crop Disease Prediction Using Machine Learning Technique

Crop yields are affected at large scale due to spread of unchecked diseases. The spread of these diseases is similar to the spreading of cancer in human body. But, unlike cancer these diseases can be identified at early stages through plant phenotyping traits analysis. In order to effectively identify these diseases, effective segmentation, feature extraction, feature selection and classification processes must be followed. Selection of the best combination for the given methods is very complex due to the presence of a large number of the aforementioned methods. Thereby disease prediction models are generally found to be ineffective. This paper proposes a highly effective machine learning-based formulation approach to select a proper classification process which improves the overall accuracy of crop disease detection with different dimensionality of plant dataset and included maximum features also. Hence, the proposed adaptive learning algorithm gives 99.2% accuracy compared to other techniques like Back-propagation Neural Network (BPNN), Convolutional Neural Network (CNN), and SVM.

Mapping Intrinsic Vulnerability to Pollution Using the DRASTIC Method in the Temara Groundwater (Northwestern Morocco)

the DRASTIC method was chosen because it can be adapted to different environments and because it combines the seven criteria that directly influence groundwater: depth, recharge, geology, soil, slope, unsaturated zone and conductivity. Located in the North West of Morocco, the Temara aquifer is a very important water resource, but it is overexploited and deteriorated as never before. This issue is of concern to decision-makers in the field of water management. The objective of this work is to create a map of vulnerability to pollution by the Bay of Geographic Information Systems (GIS) and the DRASTIC model. Thus, the highest vulnerabilities are located around the drinking water treatment plant of Ain Atiq, at the mouth of the Bouregreg River and scattered in places throughout the study area. The results of the physico-chemical analysis showed compatibility with the results of the DRASTIC model.