Data Mining In Agriculture Research Articles

Design of agricultural question answering information extraction method based on improved BILSTM algorithm

With the rapid growth of the agricultural information and the need for data analysis, how to accurately extract useful information from massive data has become an urgent first step in agricultural data mining and application. In this study, an agricultural question-answering information extraction method based on the BE-BILSTM (Improved Bidirectional Long Short-Term Memory) algorithm is designed. Firstly, it uses Python’s Scrapy crawler framework to obtain the information of soil types, crop diseases and pests, and agricultural trade information, and remove abnormal values. Secondly, the information extraction converts the semi-structured data by using entity extraction methods. Thirdly, the BERT (Bidirectional Encoder Representations from Transformers) algorithm is introduced to improve the performance of the BILSTM algorithm. After comparing with the BERT-CRF (Conditional Random Field) and BILSTM algorithm, the result shows that the BE-BILSTM algorithm has better information extraction performance than the other two algorithms. This study improves the accuracy of the agricultural information recommendation system from the perspective of information extraction. Compared with other work that is done from the perspective of recommendation algorithm optimization, it is more innovative; it helps to understand the semantics and contextual relationships in agricultural question and answer, which improves the accuracy of agricultural information recommendation systems. By gaining a deeper understanding of farmers’ needs and interests, the system can better recommend relevant and practical information.

Sugarcane Biomass Prediction with Multi-Mode Remote Sensing Data Using Deep Archetypal Analysis and Integrated Learning

The use of multi-mode remote sensing data for biomass prediction is of potential value to aid planting management and yield maximization. In this study, an advanced biomass estimation approach for sugarcane fields is proposed based on multi-source remote sensing data. Since feature interpretability in agricultural data mining is significant, a feature extraction method of deep archetypal analysis (DAA) that has good model interpretability is introduced and aided by principal component analysis (PCA) for feature mining from the multi-mode multispectral and light detection and ranging (LiDAR) remote sensing data pertaining to sugarcane. In addition, an integrated regression model integrating random forest regression, support vector regression, K-nearest neighbor regression and deep network regression is developed after feature extraction by DAA to precisely predict biomass of sugarcane. In this study, the biomass prediction performance achieved using the proposed integrated learning approach is found to be predominantly better than that achieved by using conventional linear methods in all the time periods of plant growth. Of more significance, according to model interpretability of DAA, only a small set of informative features maintaining their physical meanings (four informative spectral indices and four key LiDAR metrics) can be extracted which eliminates the redundancy of multi-mode data and plays a vital role in accurate biomass prediction. Therefore, the findings in this study provide hands-on experience to planters with indications of the key or informative spectral or LiDAR metrics relevant to the biomass to adjust the corresponding planting management design.

Multi-Class Classification of Agricultural Data Based on Random Forest and Feature Selection

Agricultural production and operation produce a large amount of data, which hides valuable knowledge. Data mining technology can effectively explore the connection between various factors from the massive agricultural data. Classification prediction is one of the most valuable agricultural data mining techniques. This paper presents a new algorithm consisting of machine learning algorithms, feature ranking method and instance filter, which aims to enhance the capability of the random forest algorithm and better solve the problem of agricultural multi-class classification. The performance of the new algorithm was tested by using four standard agricultural multi-class datasets, and the experimental results showed that the newly proposed method performed well on all datasets. Among them, substantial rise in classification accuracy is observed for Eucalyptus dataset. Applying random forest algorithm on Eucalyptus dataset results in classification accuracy as 53.4% and after applying the new algorithm (rough set) the classification accuracy significantly increases to 83.7%.

Retracted: Adaptive neuro fuzzy inference system to enhance the classification performance in smart irrigation system

AbstractTraditional agricultural systems require huge amounts of money for on‐site irrigation power. Irrigation is the process of giving water to plants for growth and development. The cost of global warming creates the potential effects of water adaptation measures to ensure food production and water availability for consumption. It requires continuous monitoring of soil moisture content at the root zone and initiating irrigation as per the pre‐programmed schedule depending upon the plant's nature, growth, soil type, and environment. Thus, the cost to farmers using conventional drip irrigation has become so high that they have to go manually and monitor the land frequently. So when the previous method was used for irrigation, it did not give correct classification results. Agricultural data mining technology provides the best crops for travel water to increase the number of crops produced. Agricultural data can be collected more efficiently using data collection and capable analytics sensors. This proposed work introduces an adaptive neuro‐fuzzy inference system (ANFIS) technique for analyzing agricultural plant growth based on soil, water level, temperature, and moisture conditions. The proposed technique has three phases: environment data collection using sensor node, preprocessing, feature selection, and classification. The initial phase captures the plant's environment condition via a sensor device, then transmits data to servers with the help of an edge node or gateway. This recursive adaptive filter method normalizes the data and removes irrelevant noise data from the sensor node. The feature selection method to extract the environment feature value with the help of a linear regression model. The ANFIS method's plant environment feature value has trained the neurons with the help of the stochastic gradient descent method. The classification method introduces a fuzzy rule to compute and validate the input parameters (e.g., soil moisture, temperature, and humidity) to predict any environment value changes. The comparison results prove that efficient monitoring is obtained through the proposed smart irrigation system.

RETRACTED ARTICLE: Data mining and statistics issues of precision and intelligent agriculture based on big data analysis

This article has been retracted from publication in the Taylor & Francis journal, Acta Agriculturae Scandinavica, Section B — Soil & Plant Science. Following publication, concerns were raised by multiple third-parties around the content of the special issue and the decision-making process. Following an investigation by the Taylor & Francis Publishing Ethics & Integrity team in full cooperation with the Editor-in-Chief, it was confirmed that this article included in Special Issue titled “ Envisage Computer Modelling and Statistics for Agriculture”, guest edited by Gunasekaran Manogaran was not peer-reviewed appropriately, in line with the Journal's peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special Issue. The journal has not confirmed if the authors were aware of this compromised peer review process. The journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines.

Lemuria: A Novel Future Crop Prediction Algorithm Using Data Mining

Abstract Agriculture exhibitions an important role in the progression and enlargement of the economy of any country. Prediction of crop yield will be useful for farmers, but it is difficult to predict crop yield because of the climatic factors such as rainfall, soil factors and so on. To tackle these issues, we are implementing a novel algorithm called Lemuria by applying data mining in agriculture especially for crop yield analysis and prediction. This novel algorithm is the hybridization of classifiers for pre-training, training and testing: deep belief network for feature learning, k-means clustering together with particle swarm optimization (PSO) to get the global solution as well as naïve Bayes clustering with PSO for testing. The performance of the Lemuria algorithm is evaluated in Python, which provides an accuracy of 97.74% for crop prediction by considering the rainfall dataset and also stated that this gives the optimum results in comparison with the existing methodologies.

A Survey on Deep learning Models for Disease Detection and Diagnosis in Agriculture Field

This paper shows a brief thought of a percentage of the broadly utilized data mining systems over agri-business information sets. It is a contemporary system to discover the arrangement over the customary and traditional technique. It is a new concept as for data mining in the field of farming. Data mining is a procedure of separating/comparing the important information from the extensive database by utilizing diverse tools and methods. Different Data Mining techniques are in use, such as K-Means, K-Nearest Neighbor(KNN), Artificial Neural Networks(ANN) and Support Vector Machines(SVM) for very recent applications of Data Mining techniques in agriculture field. Bi clustering is a technique that can also utilized in agriculture mining. Data mining in agriculture is an emerging research field. It is our opinion that efficient techniques can be developed and tailored for solving complex agricultural problems using data mining. At the end of this study we provide suggestions for future research directions in agriculture-related fields

Data warehouse and decision support on integrated crop big data

In recent years, precision agriculture is becoming very popular. The introduction of modern information and communication technologies for collecting and processing Agricultural data revolutionise the agriculture practises. This has started a while ago (early 20th century) and it is driven by the low cost of collecting data about everything; from information on fields such as seed, soil, fertiliser, pest, to weather data, drones and satellites images. Specially, the agricultural data mining today is considered as Big Data application in terms of volume, variety, velocity and veracity. Hence it leads to challenges in processing vast amounts of complex and diverse information to extract useful knowledge for the farmer, agronomist, and other businesses. It is a key foundation to establishing a crop intelligence platform, which will enable efficient resource management and high quality agronomy decision making and recommendations. In this paper, we designed and implemented a continental level agricultural data warehouse (ADW). ADW is characterised by its (1) flexible schema; (2) data integration from real agricultural multi datasets; (3) data science and business intelligent support; (4) high performance; (5) high storage; (6) security; (7) governance and monitoring; (8) consistency, availability and partition tolerant; (9) cloud compatibility. We also evaluate the performance of ADW and present some complex queries to extract and return necessary knowledge about crop management.

An Enhanced Agricultural Data Mining Technique for Dynamic Soil Texture Prediction

Abstract The soil texture classification is a prime factor for obtaining sustainable agriculture management because texture classification is indirectly influence the soil fertility management. However, the classical approach of texture classdetermination system is complex and time-consuming when using the large dataset. This paper proposes an enhanced agricultural data mining technique to classify the soil texture classes called “TEXTRI-FCM”(Texture Triangle-Fuzzy C-Means) classifier. The objective of proposed agriculture data mining technique is establishes the texture triangle and automatically plots the texture classes. In addition to the auto-generation of texture triangle, the Fuzzy C-Means (FCM) algorithm adopted with textural triangle system to classifying the vital soil groups in the textural triangle. The TEXTRI-FCM classifier simulated and analysed on MATLAB simulation environment with real-time collected data from soil testing laboratory. The resulting system showed that proposed texture triangle framework is highly predictive capability and improved response time when using the large dataset.

Data warehouse and decision support on integrated crop big data

The introduction of modern information technologies for collecting and processing agricultural data revolutionise the agriculture practices. The agricultural data mining today is considered a Big Data application in terms of volume, variety, velocity and veracity. Hence, it is a challenge and a key foundation to establishing a crop intelligence platform. The platform, which processes vast amounts of complex and diverse information, will enable efficient resource management and high quality agronomy decision making. In this paper, we designed and implemented a continental level agricultural data warehouse (ADW). ADW is characterised by its (1) flexible schema; (2) data integration from real agricultural multi datasets; (3) data science and business intelligent support; (4) high performance; (5) high storage; (6) security; (7) governance and monitoring; (8) consistency, availability and partition tolerant; (9) cloud compatibility. We also evaluate the performance of ADW and present some complex queries to extract and return necessary knowledge about crop management.

Prediction of Suitability of Soil for Different Crops using Spatial Data Mining

The main Objective of Data mining in agriculture is to improvise the productivity based on the data observed and timelines of cultivation. Spatial Data mining, a key to capture the data by proposing sensors on a particular geographical location and observe various parameters to enhance the productivity based on the statistical analysis of data collected. In general, Data mining is an anticipating measurement and prognosticates the various data sets and mutate into useful data sets which can be applied on various applications. In this paper, data mining is applied in bridging the soil conditions to the applicable crop for cultivation in enhancing the productivity and multiple crops cultivation for enriched productivity based on the data sets acquired. A Statistical analysis resulted from a backend algorithm with the data sets and displayed as dashboard with the forecasted productivity. A Grid based clustering algorithm is adhered at the backend for performing analysis on the collected data sets results crop selectivity & productivity timelines. Geographical analysis forms a grid pattern with multiple data sets as matrix results in multiple crop selectivity based on the soil conditions and analyzed data sets obtained from various sensor parameters on a particular location. Data visualization is performed after the algorithmic process at the backend and data stored in the cloud server. Spatial Survey & Collective data Sets analyzed with the algorithm are used to elevate the Crop Selectivity and productivity on a soil based on the Biological Predicts, defoliant and manure usage timelines yields Improved Monetary generation.

Reporting pesticide abuse through data mining in agriculture: The case of Nazilli - Turkey

Data mining involves certain methods of obtaining or inferring meaningful and otherwise-unknown information from the data. With the increasing significance of precision agricultural practices, farmers have become inclined to be engaged in a more conscious strategy of agriculture. Farmers use pesticides to destroy a disease or other hazard on their plants. Nevertheless, it has been learned over time that pesticides have harmful effects on human health and the environment. Although many farmers are aware of the risks of excessive use of agricultural pesticides, they still use them to get a faster yield and to maximize financial gain or minimize financial loss. Data received from Aydin Nazilli District Directorate of Agriculture was carefully organized and evaluated with the classification algorithms in the SPSS Clementine software. C5.0 and CR&T algorithms were employed in this study. It was thereby observed that, contrary to the conventional wisdom of most farmers, the excessive use of pesticides actually leads to a decrease in the yield obtained from the product, proportional to the measured dose.

A Survey of using Data Mining Techniques for Soil Fertility

Data Mining is a technique which focuses on large data sets to extract information for prediction and discovery of hidden patterns. Data Mining is applicable on various areas like healthcare, insurance, marketing, retail, communication, agriculture. Agriculture is the backbone of country’s economy. It is the important source of livelihood. Agriculture mainly depends on climate, topography, soil, biology. Agricultural Mining is a technology which can bring knowledge to agriculture development. Data Mining in agriculture plays a role in weather forecasting, yield prediction, soil fertility, fertilizers usage, fruit grading, plant disease and weed detection. The current study presents the different data mining techniques and their role in context of soil fertility, nutrient analysis.

Study of high yielding crops cultivation in India using data mining techniques

Data mining in agriculture is a modern and emerging research technique. Data mining provide many techniques like k means algorithm, support vector machine, association rule mining and Bayesian belief network [1]. This technique can be used in agriculture for various purposes. This paper describes about how association rules mining and apriori algorithm can be used in agriculture field. This paper also describes about soil, its types and crops grown in each type of soil. The technique that has been used here can be a rough set study, but like this many efficient techniques can be applied to solve many problems in agriculture.

A REVIEW ON DATA MINING: SCOPE AND APPLICATIONS IN AGRICULTURE

Data Mining is becoming a trending topic in the field of Agriculture. Crop Yield prediction, Quality Measurement, Soil classification, Crop Disease prediction etc. are some of the recently explored topics. A proper analysis and recommendation on the basis of data mining can help farmers in better understanding and management of their crops and land. Apart from that, patterns evaluated via. Data mining can help the governments for making better policies for Agriculture. As compare to industrial data mining, education data mining, business data mining and medical data mining, agriculture data mining is a novel field. This paper emphasizes on the applications which have been developed using Data Mining and further scope of the data mining. This paper discusses about the use of data mining in some topics relating to agriculture which need immediate attention. Some tools and techniques useful for the purpose of analysis are described.

Agricultural Data Mining –Exploratory and Predictive Model for Finding Agricultural Product Patterns

In India the agriculture was practiced as subsistence basis; farmers products used to exchange with his needs with other commodities on barter system in olden days. Now in better usage of agricultural technology and timely inputs, the agriculture becomes commercial in nature. The current scenario is totally different farmers want remunerative prices on his produced commodity and increased awareness marketing becomes part of agricultural system. Now situation is much more demanding if people are not competent enough then survival becomes difficult. Still in India dream of technology reaching to poor farmers is distant issue. However government is taking initiative to empower them with new ICT tools. The small effort of this paper is intended to give insight of one such technology called as Data Mining. In this paper we have taken certain conceptual Data Mining Techniques and Algorithms to implement and incorporate various methodologies to create concurrent result for decision making and

Data Mining in Agriculture on Crop Price Prediction: Techniques and Applications

crop price analysis, Data mining is emerging as an important research field. In this paper, we will discuss about the applications and techniques of Data mining in agriculture. There are various data mining techniques such as K-Means, K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN) and Support Vector Machines (SVM) which are used for very recent applications of Data Mining techniques. This paper will consider the problem of price prediction of crops. Price Prediction, nowadays, has become very important agricultural problem which is to be solved only based on the available data. Data Mining techniques can be used to solve this problem. This work is based on finding suitable data models that helps in achieving high accuracy and generality for price prediction. For solving this problem, different Data Mining techniques were evaluated on different data sets.

A survey of data mining techniques applied to agriculture

In this survey we present some of the most used data mining techniques in the field of agriculture. Some of these techniques, such as the k-means, the k nearest neighbor, artificial neural networks and support vector machines, are discussed and an application in agriculture for each of these techniques is presented. Data mining in agriculture is a relatively novel research field. It is our opinion that efficient techniques can be developed and tailored for solving complex agricultural problems using data mining. At the end of this survey we provide recommendations for future research directions in agriculture-related fields.