Classification of agricultural soil parameters in India

Sirsat, Manisha Sanjay; Cernadas, E.; Fernndez-Delgado, M.; Khan, Rehanullah

doi:10.1016/j.compag.2017.01.019

Cited by 68 publications

(16 citation statements)

References 32 publications

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“…The author describes Plant development advancing microscopic organisms give various advantages in cultivating by reproducing crop health and more, vitamins and smothering help in improving earth quality [16]. The author describes the importance of the Indian economy in horticulture, which is molded by the poor soil quality [17]. The author describes the examination included data collection from 100 paddy-developing nuclear families during the 2010 tempest season across five town tracts in Myanmar [18].…”

Section: Literature Foundationmentioning

confidence: 99%

Novel Methods for Crop Prediction based on Influencing Parameters in Indian Regions using Data Mining

Joseph,

Manohar

2020

IJEAT

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Agriculture plays a crucial role for the production of food in Indian regions. Indian regions mainly produces crops like rice, wheat, maize and many other types of crop. There are several factors required for the productivity of any harvest, but we know that soil, climate, pesticides, Fertilizers and ground water is most influencing essential factor for the productivity of any harvest. Let us consider soil which is the key element as it provides nutrients for proper development and growth of crops. Secondly, climate is also having major role in agriculture as crop growth depends on rainfall, humidity, temperature etc. Thirdly, Pesticides is widely used to control pest and prevents the damage of crops. Fourthly, Fertilizers can improve the quality of crops. Finally, ground water which will enrich nutrients in soil. The current preparation centers around different information mining procedures utilized in various regions of India and anticipate future harvest along with reasonable information mining procedure saw during the period(1920-2019).The parameters considered for the examination were soil, atmosphere, water thickness, pesticides and composts and Crop informational collection. The Classification calculations utilized in preparation were Adaptive boosting classification, Excess tree classification, neural based classification, Multiple Process classification, Decision making classification, K-closest neighbors, Bayesian theory classification, decision Forest classification, support group machine, and Randomized Gradient Classification. By using the techniques mentioned above we can improve the harvest prediction using information mining techniques which in turn help the farmers to take better decisions in future and it can be used in other technologies like image analyzing etc. The Experimental results show predicted crop, suitable algorithm and algorithm accuracy in that particular state of India respectively.

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Section: Literature Foundationmentioning

confidence: 99%

Novel Methods for Crop Prediction based on Influencing Parameters in Indian Regions using Data Mining

Joseph,

Manohar

2020

IJEAT

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“…Machine learning techniques are used in increasing the productivity of agriculture in almost all countries of the world. Sirsat, M. S., et al [15] have explained how supervised learning algorithms such as SVM, decision trees, nearest neighbor classifiers and so on helps the farmers in making decisions regarding improving soil quality and crop quality by classifying the soil parameters. The input taken in this approach are the various soil parameters such as village wise fertility indices of organic carbon (OC), phosphorus pent oxide (P2O5), manganese (Mn) and iron (Fe), soil pH and type, soil nitrous oxide (N2O) and potassium oxide (K2O).…”

Section: Increasing the Productivity Of Agriculture Using Machine Leamentioning

confidence: 99%

Study on Machine Learning for Identification of Farmer’s Query in Kannada Language

Pai¹,

Of²,

Hegde³

2019

IJCA

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Agriculture is considered as the economic backbone of almost all the countries in the world. As there is increase in population, the need for basic requirements such as food, clothing, shelters, and medicines also increases. To meet all these needs, advancement in agriculture is needed. Various technologies have been introduced in agriculture to increase the yield of several crops. One among them is the machine learning technology. Machine learning is an important area in computer science that allows the computers to write programs on its own without being explicitly programmed. Machine learning techniques in agriculture help to improve the traditional farming techniques in a very efficient way. The main purpose of this paper is to learn different use of machine learning techniques in agriculture and to develop machine learning model for agriculture.

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“…This paper [13] explained the j48, K-nearest neighbors (KNN) to classify and predict the wheat yield. In this paper [14] the soil parameters are classified by different classifiers of various families such as decision tree, support vector machine, random forest in India. Regression methods were applied in this paper [15] to predict soil fertility for various available nutrients in Maharashtra.…”

Section: Introductionmentioning

confidence: 99%

“…A wide range of statistical approaches [21] were able to analyze soil quality that is directly involved in good farming and crop production with good crop health. [14] focused to apply a combination of 20 classifiers to classify various soil nutrients and fertility indices. Machine Learning can be widely used in each area of research for classification and prediction.…”

Section: Introductionmentioning

confidence: 99%

Soil Quality Prediction for Determining Soil Fertility in Bhimtal Block of Uttarakhand (India) Using Machine Learning

Pant

Pant²,

Pant³

et al. 2021

ijaa

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Classification of agricultural soil parameters in India

Cited by 68 publications

References 32 publications

Novel Methods for Crop Prediction based on Influencing Parameters in Indian Regions using Data Mining

Novel Methods for Crop Prediction based on Influencing Parameters in Indian Regions using Data Mining

Study on Machine Learning for Identification of Farmer’s Query in Kannada Language

Soil Quality Prediction for Determining Soil Fertility in Bhimtal Block of Uttarakhand (India) Using Machine Learning

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