Evaluation of predictive data mining algorithms in soil data classification for optimized crop recommendation

Arooj, Ansif; Riaz, Mohsin; Akram, Malik Naeem

doi:10.1109/icacs.2018.8333275

Cited by 18 publications

(7 citation statements)

References 12 publications

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“…When comparing these three algorithms zeroR, stacking produced the best results. Arooj et al [28] presented data mining study possibilities for soil classification utilizing wellknown classification algorithms such as J48, OneR, BF Tree, and Nave Bayes. The experiment was carried out on data from the Kasur district of Pakistan.…”

Section: Literature Reviewmentioning

confidence: 99%

Data Mining Classification Algorithms for Analyzing Soil Data

Taher

Abdulazeez

Zebari

2021

AJRCoS

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Rapid changes are occurring in our global ecosystem, and stresses on human well-being, such as climate regulation and food production, are increasing, soil is a critical component of agriculture. The project aims to use Data Mining (DM) classification techniques to predict soil data. Analysis DM classification strategies such as k-Nearest-Neighbors (k-NN), Random-Forest (RF), Decision-Tree (DT) and Naïve-Bayes (NB) are used to predict soil type. These classifier algorithms are used to extract information from soil data. The main purpose of using these classifiers is to find the optimal machine learning classifier in the soil classification. in this paper we are applying some algorithms of DM and machine learning on the data set that we collected by using Weka program, then we compare the experimental result with other papers that worked like our work. According to the experimental results, the highest accuracy is k-NN has of 84 % when compared to the NB (69.23%), DT and RF (53.85 %). As a result, it outperforms the other classifiers. The findings imply that k-NN could be useful for accurate soil type classification in the agricultural domain.

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Section: Literature Reviewmentioning

confidence: 99%

Data Mining Classification Algorithms for Analyzing Soil Data

Taher

Abdulazeez

Zebari

2021

AJRCoS

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“…Random forest looks for the most important parameter among all while doing splitting of any node, then from the subset of random features it searches for the best among them. This eventually generates a model which has higher accuracy in wide diversity [4], [11]. In this algorithm only selective features are taken into account for the splitting of a node [14], [16].…”

Section: Module-2 Random Forest Classifiermentioning

confidence: 99%

“…after this process is complete, unknown samples x' predictions are applied by taking average of these predictions from all individual regression trees on x': (11) To decrease the variance of our model, without increasing the bias we have applied a bootstrapping procedure that eventually leads to a better model performance. We have seen that if the trees do not have any relation the average of these trees are not so sensitive towards noise but on the other hand predictions made for a single tree are highly sensitive to noise in the training set [11]. By training many trees on a single dataset we can generate strongly correlated trees, to de-correlate these trees we can use different training sets on them which is known as bootstrap sampling.…”

Section: Module-2 Random Forest Classifiermentioning

confidence: 99%

Random Forest Algorithm for Soil Fertility Prediction and Grading Using Machine Learning

Shubha¹,

Sushma²

2019

IJITEE

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n society the population is increasing at a high rate, people are not aware of the advancement of technologies. Machine learning can be used to increase the crop yield and quality of crops in the agriculture sector. In this project we propose a machine learning based solution for the analysis of the important soil properties and based on that we are dealing with the Grading of the Soil and Prediction of Crops suitable to the land. The various soil nutrient EC (Electrical Conductivity), pH (Power of Hydrogen), OC (Organic Carbon), etc. are the feature variables, whereas the grade of the particular soil based on its nutrient content is the target variable. Dataset is preprocessed and regression algorithm is applied and RMSE (Root Mean Square Error) is calculated for predicting rank of soil and we applied various Classification Algorithm for crop recommendation and found that Random Forest has the highest accuracy score.

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“…Some farmers are cultivation rice in silty soil which is not relevant to this soil and could not provide more yield. Agronomists from the dry western plateau are growing melons and sorghum as major crops, but some are focusing millet which is less beneficial d) Loam Soils: Loam contains clay, sand and silt in the different proportions along with organic matters [26]. Various proportions of clay, silt, sand, and organic matter; the magnitudes of these defines the quality, productivity and behavior of the soil towards the cultivation.…”

Section: )mentioning

confidence: 99%

Knowledge based Soil Classification Towards Relevant Crop Production

Haider¹,

Durrani²,

Rehman³

et al. 2019

IJACSA

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Pakistan's economy is strongly associated with agriculture sector. For a country having 25 % of GDP contributed through agriculture, there is a need to modernize the agriculture by acclimatizing contemporary approaches. Unfortunately, it has become a common trend among farmers to cultivate crops, being used in food items or which can easily be sold out in the market without using knowledge about the suitability or relevancy of crops to the soil environment. Consequently, the farmers face financial losses. Many researchers have proposed soil classification methods for various soils related researches, but they have very little contribution towards guidance of the farmers to select most suitable crops for cultivation at a particular soil type. Without the use of technology and computer-assisted approaches, the process of classifying soil environments could not help the farmers in taking decisions regarding appropriate crop selection in their respective fields. In this paper, an effective knowledge-oriented approach for soil classification in Pakistan has been presented using crowd sourced data obtained from 1557 users regarding 103 agricultural zones. The data were also obtained from AIMS (Govt. of Punjab) and Ministry of National Food Security & Research. In this work, random forest classifier has been used for processing and predicting complex tiered relationship among soil types belonging to agricultural zones and major suitable crops for improving yield production. The proposed model helps in computing the degree of relevancy of crop to agricultural region that help former selecting suitable crops for their cultivated lands.489 | P a g e www.ijacsa.thesai.org cultivation of crops on the soil types. Experimental setup is given in Section IV, which is interlinked with Sections V and VI. These sections cover the implementation of the algorithm and discusses the results of different experiments respectively.

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Evaluation of predictive data mining algorithms in soil data classification for optimized crop recommendation

Cited by 18 publications

References 12 publications

Data Mining Classification Algorithms for Analyzing Soil Data

Data Mining Classification Algorithms for Analyzing Soil Data

Random Forest Algorithm for Soil Fertility Prediction and Grading Using Machine Learning

Knowledge based Soil Classification Towards Relevant Crop Production

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