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Research on the processing method of multi-source heterogeneous data in the intelligent agriculture cloud platform

Data publikacji: 20 May 2022
Tom & Zeszyt: AHEAD OF PRINT
Zakres stron: -
Otrzymano: 05 Oct 2021
Przyjęty: 15 Mar 2022
Informacje o czasopiśmie
License
Format
Czasopismo
eISSN
2444-8656
Pierwsze wydanie
01 Jan 2016
Częstotliwość wydawania
2 razy w roku
Języki
Angielski
Abstract

With the development of big data and blockchain technology, a large amount of multi-source heterogeneous data has been accumulated in the agricultural field by before, during and after production. Agricultural information service systems are often targeted at specific regions, specific applications and specific data resources. Due to the lack of effective analysis and refining, the conversion efficiency of data resources into useful information is too low, resulting in contradiction between the continuous enrichment of agricultural data resources and the relative lack of agricultural information services. Therefore, in view of the multi-source heterogeneous characteristics of agricultural data and the specific business needs of different agricultural scenarios, the intelligent processing method of agricultural data is analysed, and a heuristic algorithm based on K-Means limited clustering number is proposed to judge the accuracy of abnormal data processing. By inputting test sample data for testing, the algorithm has improved accuracy by nearly 30% compared to traditional K-Means.

Keywords

Introduction

Intelligent agriculture is an advanced stage of agricultural production. It integrates emerging Internet, mobile Internet, cloud computing and Internet of Things technologies. It relies on various sensor nodes (environmental temperature and humidity, soil moisture, carbon dioxide, image, etc.) and wireless communication networks to realise the intelligent perception, intelligent early warning, intelligent decision-making, intelligent analysis and online guidance of experts in the agricultural production environment, and provide accurate planting, visual management and intelligent decision-making for agricultural production.

The rapid growth of data has brought difficulties to the application of agricultural big data. Under normal circumstances, big data require corresponding data preprocessing to reduce noise and complexity, enhance data inter-operability, and then through mining and analysis of association rules, etc., to find the rule and value from the inexhaustible connections of many data, provide efficient application services for the agricultural field.

Data aggregation and extraction technologies based on intelligent perception and industrial Internet provide technical means for the rapid collection and online application of data resources in the agricultural industry. However, with the accumulation of a large number of heterogeneous data from multiple sources, the value density of data is getting lower and lower. The difficulty of utilisation is gradually increasing, and the formed big data needs to be cleaned, filtered, integrated, etc. before which to be effectively used. In contrast, with the differentiation of data types and the increase of data dimensions, the connections between data have become intricate and complicated, and it is difficult to quickly find patterns from them, so further application of data will become more difficult.

At present, domestic and foreign scholars and agricultural engineering technicians have carried out a lot of research work in each section of agricultural big data analysis and processing, which is mainly reflected in intelligent collection, mining and analysis, and personalised recommendation services.

In summary, our major contributions are:

The complexity and professionalism of agricultural multi-source heterogeneous data determines that it is difficult for general data processing and analysis methods to be directly applied in agricultural field. According to the characteristics of agricultural big data and the difference in analysis objectives, a large scale suitable for smart agricultural cloud platform is proposed. Data cleaning and feature extraction methods, and on this basis, realise the main correlation analysis method of coupling time and space factors, which can effectively capture abnormal market fluctuations and make up for the lack of agricultural big data adaptability analysis methods.

A heuristic algorithm based on K-Means to limit the number of clusters is proposed to judge abnormal data sets. By inputting sample data for testing, the corrected algorithm has improved accuracy by nearly 30% compared with the traditional algorithm.

The remainder of this paper is structured as follows. In Section 2, we describe our design motivation. The related work is presented in Section 3. In Section 4, we show characteristics and preprocessing methods of agricultural multi-source heterogeneous data. We propose a recognition algorithm of abnormal data based on K-Means in Section 5. In Section 6, we analyse the test results. Finally, we conclude the paper in Section 7.

Background and motivation

With the renewal of industrial technology, the agricultural field has also entered the era of big data and artificial intelligence. However, the cost of current agricultural information service and the threshold of information are relatively high, and farmers cannot afford the high construction, operation and subsequent maintenance costs. Information service systems for farmers and rural areas are still lacking, and they are often individually customised for a certain region, a certain application or a certain type of data resources, and the phenomenon of information islands is serious. In addition, these systems often lack in-depth information mining and exploitation, and are not well-targeted, and cannot provide individualised services to producers, operators and governments. The emergence of search engines has solved the problem of poor information acquisition by users to a certain extent, but these general search engines are for all users and all data, and adopt general processing, aiming at the accuracy and relevance of domain information collection. Very poor, lack of unified normative control, fuzzy scope of service objects and subject positioning, lack of depth and poor logic processing capabilities make it difficult to solve practical problems in the agricultural industry. At the same time, the data collected in the front-line environment of agricultural production has not been effectively managed, stored, processed and summarised, and the degree of real-time intelligence is not high.

In response to the above problems, it is very necessary to study the massive amounts of Internet and Internet of Things agricultural data, to realise intelligent and automated data collection and integration, and to establish a unified agricultural data resource centre. Use data mining algorithms to extract and analyse valuable information, form knowledge, and continuously expand and update the knowledge base of agricultural experts. It also publishes data services in the form of cloud services to provide individualised information services to various users such as farmers, farmers’ professional cooperatives, brokers and agricultural enterprises. Realise the efficient collection and intelligent analysis of information, and improve the utilisation efficiency of agricultural information by agricultural practitioners. Provide some new ideas and methods for the application of key technologies of big data in the agricultural field.

Related Work

Chen Shengjie et al. [1] researched to facilitate the real-time monitoring and research of agricultural experiment-based environmental data by agricultural researchers and to help researchers to obtain and record all the environmental data of agricultural experimental base in real-time, including environmental temperature, air humidity, soil humidity and illuminance. It is proposed to develop a set of intelligent data acquisition system with the help of remote transmission of the Internet of Things and embedded technology. The system is easy to use, stable in transmission and highly reliable, effectively improving the work efficiency of researchers, and can be applied to a variety of agricultural scenarios.

Wang Lingling et al. [2] focused on the analysis of key technologies affecting agricultural field data acquisition and control. Based on the existing mature information technology and agricultural equipment, they put forward some ideas for the development of agricultural field data acquisition and control, and carried out systematic overall design.

Califf and Mooney [3] provided the RAPIER system, which can automatically generate relatively robust extraction rules. Its learning algorithm uses inductive logic training technology to learn template rules that contain text context restrictions. On the basis of natural language understanding, ontology-based information extraction technology has also emerged.

Buitelaar et al. [4] proposed SOBA, an ontology-based heterogeneous data information extraction system that can extract information from text and image titles.

Synthesise a related knowledge base, which can be used to query data information from different sources and accurately answer questions.

Zhao Di [6] used the Ebbinghaus forgetting curve to improve the time weight and the smallest diameter circle method to improve the distance weight, and constructed a hybrid recommendation algorithm for agricultural tourist attractions that integrates the LDA topic model and collaborative filtering. Through the accuracy rate, recall rate, and coverage rate of three evaluation indicators, the comparison experiment and analysis with the traditional recommendation algorithm have improved the accuracy of the recommendation results.

Zhen Xiaonan [7] propose the personalised collaborative filtering recommendation algorithm, using the MovieLens dataset to verify the algorithm. Experimental results showed that the proposed algorithm can effectively improve the accuracy of recommendation.

[14] In view of the widespread existence of ‘data barriers’ in the information systems used by public security organs, which are not conducive to the actual problems of investigating and handling cases, a multi-source heterogeneous big data platform was designed and implemented. The application results show that the construction of the platform can effectively aggregate data resources, realise the analysis and processing of multi-source heterogeneous data and forecast the situation, and has high industry application value.

[15] The paper uses a combination of relational and non-relational databases to build a multi-source heterogeneous detection big data storage architecture that integrates data acquisition and storage functions, solves the problem of detection data information islands, and improves detection big data analysis and mining efficiency.

Characteristics and preprocessing methods of agricultural multi-source heterogeneous data

Agricultural big data comes from the Internet of Things, Internet extraction and other channels. It involves the growth environment data of different crop varieties, growth stages, production links, regions, time periods, growth monitoring data and pest home monitoring data, as well as different regions. Time market price quotation data. The data forms include numerical values, images, text (knowledge base), etc. Therefore, monitoring objects and indicators of agricultural big data are different, with a large time and space span, and it has the characteristics of a wide range of sources, various types and multiple dimensions. It is mainly divided into production monitoring data, circulation monitoring data, market monitoring data and network public opinion data.

Multi-source heterogeneous agricultural big data needs operations such as preprocessing and integration to ensure the quality and efficiency of data applications and provide reliable data sets for in-depth data mining applications. Generally, multi-source heterogeneous data cannot be directly used for analysis [8]. Data preprocessing such as feature extraction, validity check, and redundant abnormal data item cleaning is used to perform data integration while ensuring data quality. This kind of analysis uses targets to transform data, match data, and establish data set to provide reliable data support for agricultural management decision-making. The process is shown in Figure 1.

Fig. 1

Preprocessing of agricultural multi-source heterogeneous data

Feature extraction

Data processing should be distinguished according to different data types and application goals, and feature extraction should be carried out in a targeted manner, so as to carry out data preparation, cleaning and conversion with the goal of enhancing data quality. To confirm the data characteristics, we need to determine what we need from the many data characteristics based on the target and combined with the industry's prior knowledge. For example: the data needed to calculate the yield of crops requires crop varieties, stubbles, soil temperature and humidity, air temperature and humidity, soil fertility, average rainfall, phase rainfall, etc., and iterative data for pest prediction and analysis. When calculating profitability, data such as the purchase price, market demand and production supply in the same period are needed, and the ratio of increase and decrease calculated in combination with the trend of change is used to obtain the expected profit value of the crop [9].

After determining the characteristics of the data, the validity of the data needs to be analysed. If more data is difficult to obtain, the workload of parameter optimisation and adjustment in the final analysis model will be relatively large, and the analysis accuracy will be greatly affected. Agricultural multi-dimensional heterogeneous data usually faces complex application goals, and often requires feature extraction through model or pattern recognition.

Data conversion and reduction

There may also be a process of data transformation in the converter of data integration. The form of data transformation is divided into feature normalisation, continuous data discretisation, default value processing and multi-dimensional feature dimensionality reduction.

Data mining requires a suitable data set to achieve results. Data reduction is a processing method to meet this demand. The main way is to reduce data based on data aggregation, dimension reduction, numerical reduction, sampling, data compression, etc. The processing range is the process of converting the original data into more compact data without losing useful information [10, 11].

Data aggregation: According to business or analysis goals, collect statistics on existing data items, thereby reducing the amount of data and accelerating the process of data processing and mining. For example, the output or selling price of agricultural products is summarised by region.

Dimension reduction: Under the premise of not affecting the analysis results, data attributes that are not related to the analysis target or business are eliminated to reduce the amount of data analysis calculations. For example, there is a linear relationship between characteristic soil moisture, rainfall, and evaporation. Select soil moisture as the key feature to remove rainfall and evaporation, thereby reducing redundancy, improving accuracy and reducing analysis time.

Numerical reduction: Choose less or smaller data to represent the original data. Generally, methods such as regression and log-linear model can be used to establish data reduction by sampling, clustering and histogram.

Sampling: Data mining and analysis are often very complex, large in data volume, and expensive in calculations. Under the premise of not affecting the analysis results, sampling is used to obtain samples with similar characteristics such as variance and mean, instead of original data to participate in the analysis.

Data compression: It mainly performs lossless compression or lossy compression on data with large storage capacity (such as video data) according to a certain transformation method. At present, the more common lossy compression methods include wavelet transform and principal component analysis. These compressions have the characteristics of high compression ratio and low information distortion rate, which can greatly reduce the amount of data calculation and improve analysis efficiency.

Recognition of abnormal data based on K-Means

K-Means algorithm is a clustering algorithm, which belongs to unsupervised learning [13]. The K-Means algorithm mainly does two things: Cluster allocation and Move cluster centres. The main process of the algorithm:

Randomly select k objects, and each object initially represents the centre of a cluster;

For each remaining object, assign it to the nearest cluster according to its distance from the centre of each cluster;

Recalculate the average value of each cluster and update it to the new cluster centre;

Repeat (2) and (3) until the criterion function converges.

Data quality is a prerequisite to ensure the correct analysis results. In this paper, we propose a heuristic algorithm based on K-Means to limit the number of clusters. According to distance clustering, the cluster centre is obtained heuristically, and the abnormal data set is judged by the cumulative number of cluster elements.

First, when selecting the initial seed element, sort the attribute data of each crop (such as the price of agricultural products), and select three different elements arranged from small to large as the seed element to obtain three cluster centres. The constituted piles are the minimum pile, the middle pile and the maximum pile, respectively [16, 17]. The basic idea of the K-Means algorithm based on outlier detection is: after each clustering is completed, then use the improved K-Means algorithm to cluster the smallest heap, and traverse to the left until the smallest heap is detected. When abnormal price data in the smallest heap is detected, return to the ROOT node and perform clustering calculation on the largest heap until abnormal data in the largest heap is detected. If it is still not found after traversing 4 layers, stop the traversal [12]. Finally, a tree-like structure is obtained, and the idea of algorithm is shown in Figure 2.

Fig. 2

The idea of algorithm

Among them, the degree of deviation of the outlier data is reflected by calculating the ratio of the number of prices a, b, c, e, f and g. Calculate the values of Sum(a+b+c) and Sum(e+f+g), respectively. If Sum(a+b+c) is greater than a certain closed value, detect the abnormal data of the smallest heap and obtain the minimum attribute value boundary, Similarly, if Sum(e+f+g) is greater than a certain closed value, the maximum abnormal data is detected, and the maximum attribute value boundary is obtained. Among them, the calculation formulas of a and e are as follows: a=count(Maxheap+Midheap)count(Minheap) a = count\left( {Maxheap + Midheap} \right)count\left( {Minheap} \right) e=count(Minheap+Midheap)count(Maxheap) e = count\left( {Minheap + Midheap} \right)count\left( {Maxheap} \right)

For the left subtree, the calculation formulas for b and c are the same as (1). Similarly, for the right subtree, the calculation formula for f and g is the same as (2).

The specific steps of the algorithm are as follows:

Sort the prices of a certain crop variety, and select three different data objects as the initial clustering centres.

Calculate the distance between other data objects and selected data objects.

Calculate the distance between each non-outlier data object and the cluster centre, and divide the object into the closest cluster based on the distance.

Repeatedly calculate the average value of the objects in each cluster and update the cluster centre.

Repeat steps 3 and 4 until the criterion function e converges.

Count the number of price data of the largest heap, intermediate heap and smallest heap.

Traverse from the root node to the left, calculate the ratio of the number of prices of the largest and middle heaps to the number of the smallest heaps, repeat the above process, add the above ratios, and find the smallest price anomaly if it exceeds a certain threshold.

Traverse from the root node to the right again, and calculate the ratio of the number of price data of the minimum and intermediate heaps to the number of the largest heaps. Repeat the above process, add these ratios, and find the maximum price anomaly if the ratio exceeds a certain threshold [18, 19].

The pseudo code of the algorithm is shown in Algorithm 1.

After amended K-means algorithm

Input: s,k /*s=datafile()*/
Output: K clusters /*K cluster centers no longer change*/
1: Initialization;
2: g = new BasicKMeans().cluster(s, k);
3: root = g;
4: t = getBorderPrice(g, k);
5: int num = 1; double sum = 0.0;
6: while (num! = 5) do
7:   if (t[0][0] > 0) then
8:     if (sum >= 100) then
9:       min = t[1][1];
10:       break;
11:     end if
12:     if (t[0][1] == t[0][2]) then
13:       min = t[0][1];
14:       break;
15:     end if
16:     if (num == 4) then
17:       min = t[0][1];
18:       break;
19:     end if
20:   end if
21:   g = new BasicKMeans().cluster(g[0], k);
22:   t = getBorderPrice(g, k);
23:   num + +;
24:   sum = sum + (t[1][0] + t[2][0])/t[0][0];
25: end while

The K-means algorithm is easy to understand and has a good clustering effect. When processing large datasets, the algorithm can ensure good scalability and low algorithm complexity. But it is very sensitive to outliers, so data without normalisation and unity cannot be directly involved in calculations and comparisons. In addition, the selection of the K value has a great influence on the algorithm. Common methods for selecting K value are: elbow method and Gap statistic method. However, it is generally believed when the optimal clustering number K is 3, the algorithm has the best effect.

Analysis of test results

In the experiment, we use the C language to write the code and compile, debug and run in the Visual C++ compiler. We use Algorithm 1 to perform abnormal data monitoring and analysis, assuming that the input yellow peach 50000 data object set S and cluster number K=3.

After the program runs, it outputs the number, minimum and maximum price data of each node in the traversal tree structure of the minimum, intermediate and maximum heaps. Since a+b+c+d=22<threshold 100, no minimum anomaly is found. Data, the minimum boundary is 2.2, since e+f=841>threshold 100, the maximum abnormal data 600–620 is found, and the maximum boundary is 72. From the above process, the minimum boundary value of yellow peach can be obtained: 2.2, and the maximum boundary value: 72.0. The hierarchical iterations and experimental results of the smallest and largest heaps are shown in Tables 1 and 2 [20, 21].

Iteration process of minimum heap

Node type Quantity Lowest price Highest price Ratio

Min heap 40224 2.2 9.8 a=(7567+2489)/40224=0.25
Mid heap 7567 9.3 15.2
Max heap 2489 15.6 600
Min heap 7656 2.2 5.8 b=(16993+15665)/7656=4.26
Mid heap 16993 5.3 6.85
Max heap 15665 5.6 9.1
Min heap 1205 2.2 4.1 c=(2178+4262)/1205=5.34
Mid heap 2178 4.3 4.7
Max heap 4262 4.6 5.8
Min heap 91 2.2 3.8 d=(534+580)/91=12.25
Mid heap 534 3.3 3.8
Max heap 580 3.8 4.1

Iteration process of maximum heap

Node type Quantity Lowest price Highest price Ratio

Min heap 40224 2.2 9.8 e=(40224+7567)/2489=19.2
Mid heap 7567 9.3 15.2
Max heap 2489 15.6 600
Min heap 2344 15.4 21 f=(2344+124)/3=823
Mid heap 124 10.3 72
Max heap 3 600 620

When the implemented algorithm and the traditional K-Means algorithm aggregate and calculate market price data, the accuracy rates calculated by the two algorithms are shown in Figure 3. The abscissa represents the scale of agricultural product market price data in the dataset, and the ordinate represents abnormal price detection. The algorithm implemented in this paper gradually shows a trend of steady growth with the increase of the data scale of the agricultural product market price dataset. The traditional K-Means algorithm affects the stability of the result because of the instability of the clustering centre.

Fig. 3

Comparisons between the algorithm and traditional K-means

Conclusion

With the in-depth development of big data technology, digitization has been extensively developed in the agricultural field at an unprecedented speed. The broadening of traditional collection channels through Internet extraction and Internet of Things monitoring is a recurring manifestation of this, but at the same time, there are also problems such as explosive growth in data distribution and lack of analysis capabilities. This paper solves industry application problems, expounds the characteristics of multi-source heterogeneous agricultural big data and corresponding data preprocessing methods, and proposes a method for processing abnormal data of heterogeneous multi-source data, and verifies that it is suitable for smart agriculture through examples. The cloud platform provides a feasible way for the prospective mining of massive data and has achieved good results.

Fig. 1

Preprocessing of agricultural multi-source heterogeneous data
Preprocessing of agricultural multi-source heterogeneous data

Fig. 2

The idea of algorithm
The idea of algorithm

Fig. 3

Comparisons between the algorithm and traditional K-means
Comparisons between the algorithm and traditional K-means

Iteration process of maximum heap

Node type Quantity Lowest price Highest price Ratio

Min heap 40224 2.2 9.8 e=(40224+7567)/2489=19.2
Mid heap 7567 9.3 15.2
Max heap 2489 15.6 600
Min heap 2344 15.4 21 f=(2344+124)/3=823
Mid heap 124 10.3 72
Max heap 3 600 620

After amended K-means algorithm

Input: s,k /*s=datafile()*/
Output: K clusters /*K cluster centers no longer change*/
1: Initialization;
2: g = new BasicKMeans().cluster(s, k);
3: root = g;
4: t = getBorderPrice(g, k);
5: int num = 1; double sum = 0.0;
6: while (num! = 5) do
7:   if (t[0][0] > 0) then
8:     if (sum >= 100) then
9:       min = t[1][1];
10:       break;
11:     end if
12:     if (t[0][1] == t[0][2]) then
13:       min = t[0][1];
14:       break;
15:     end if
16:     if (num == 4) then
17:       min = t[0][1];
18:       break;
19:     end if
20:   end if
21:   g = new BasicKMeans().cluster(g[0], k);
22:   t = getBorderPrice(g, k);
23:   num + +;
24:   sum = sum + (t[1][0] + t[2][0])/t[0][0];
25: end while

Iteration process of minimum heap

Node type Quantity Lowest price Highest price Ratio

Min heap 40224 2.2 9.8 a=(7567+2489)/40224=0.25
Mid heap 7567 9.3 15.2
Max heap 2489 15.6 600
Min heap 7656 2.2 5.8 b=(16993+15665)/7656=4.26
Mid heap 16993 5.3 6.85
Max heap 15665 5.6 9.1
Min heap 1205 2.2 4.1 c=(2178+4262)/1205=5.34
Mid heap 2178 4.3 4.7
Max heap 4262 4.6 5.8
Min heap 91 2.2 3.8 d=(534+580)/91=12.25
Mid heap 534 3.3 3.8
Max heap 580 3.8 4.1

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