Crop Selection Method to maximize crop yield rate using machine learning technique

Kumar, Rakesh; Singh, M. P.; Kumar, Prabhat; Singh, Jyoti

doi:10.1109/icstm.2015.7225403

Cited by 202 publications

(64 citation statements)

References 15 publications

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“…It is built on the techniques of the machine learning to raise the crop yield [18]. Crop selection methods have been suggested to do crop selection with respect to forecasting [6].…”

Section: Selection and Prediction Of The Cropmentioning

confidence: 99%

Constructing Model for Forecasting the Production using the Classification Technique in Data Mining for the District of Tamil Nadu

2019

IJITEE

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Data mining plays an essential role in the cropproduction. It is a major field for forecasting and analyzing the crop. The vital role of the cultivator is to know about the production of the crop. In the years before, forecasting was carried out by taking into account the cultivator’s previous experience on the selected area. The forecasting was the important criteria which should be solved by considering the data on hand. By using Data mining method, the enhanced selection can be done. Various Data Mining methods have been used for calculating the upcoming year's production. This investigation helps to recommend a model for forecasting the yield from the earlier data. For accomplishing and forecasting the yield association rule mining in data mining has been used. This helps to focus on implementing a system which may be used for forecasting the yield in the upcoming years. This research aims at presenting a detailed study by forecasting the yield using association rules in data mining technique for the chosen area in India. The results haveshown that the anticipated work done is working well in order to predict the production of the yield.

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“…It is built on the techniques of the machine learning to raise the crop yield [18]. Crop selection methods have been suggested to do crop selection with respect to forecasting [6].…”

Section: Selection and Prediction Of The Cropmentioning

confidence: 99%

Constructing Model for Forecasting the Production using the Classification Technique in Data Mining for the District of Tamil Nadu

2019

IJITEE

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“…Using the majority voting techniques as Random tree, CHAID ,K-Nearest Neighbor and Naïve Bayes algorithms to build a ensemble recommendation model to propose a crop based on site specific parameters accurately and efficiently. Authors in [4] propose Crop Selection Method (CSM) to improve the net yield rate of the crop by suggesting the sequence of crops to be planted over season based on prediction of crop yield.…”

Section: Literature Reviewmentioning

confidence: 99%

Crop Recommendation using Machine Learning Techniques

2019

IJITEE

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Precision agriculture (PA) allows precise utilization of inputs like seed, water, pesticides, and fertilizers at the right time to the crop for maximizing productivity, quality and yields. By deploying sensors and mapping fields, farmers can understand their field in a better way conserve the resources being used and reduce adverse affects on the environment. Most of the Indian farmers practice traditional farming patterns to decide crop to be cultivated in a field. However, the farmers do not perceive crop yield is interdependent on soil characteristics and climatic condition. Thus this paper proposes a crop recommendation system which helps farmers to decide the right crop to sow in their field. Machine learning techniques provide efficient framework for data-driven decision making. This paper provides a review on set of machine learning techniques to support the farmers in making decision about right crop to grow depending on their field’s prominent attributes.

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“…A plant supplement administration framework has been proposed in view of ML strategies to address and manage the issues of soil and keep up its fertility levels and thus enhance the product yield (Ghosh and Koley, 2010) [50]. A crop choice technique called CSM has been proposed (Kumar et al 2015) [54] which helps in crop determination in light of its yield forecast and different environmental variables.The method depends on various parameters such as production rate, market price and government policies. Many researchers studied prediction of yield rate of crop, prediction of weather, soil classification and crop classification for agriculture planning using statistics methods or ML techniques.…”

Section: Bmentioning

confidence: 99%

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2018

International Journal of Innovative Research in Electrical, Ele

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Manual identification, classification and taxonomy of a definite and specific species or plant disease are generally obstructed by several difficulties such as time consumption, the limited number of expert specialists and the increase in the number of the defined characteristics of each species. The steady progress of the computational technology over the last few years concerning the automated species recognition including plant structures is be easier particularly in the presence of huge databases of information and the enhancement of classification and analyzing image technology. Several investigators are currently working in agriculture field mainly those concerned with plant identification or of plant disease recognition through image processing, extraction of identical features and classifying them into exact classes. In this review, the integration of computer science with agriculture has been discussed to detect, quantify, predict, early identify and classify plant diseases as well as forecasting agricultural crops using different machine learning techniques. Weather forecasting, smart irrigation system, plant leaf identification, etc will also discussed. The aim of this paper is to provide the investigators particularly biologists with computational recognition techniques as well as the possibility of using image informative morphological features of a species or a group of related species in recognition of many biological organisms including plants and microorganisms.

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Crop Selection Method to maximize crop yield rate using machine learning technique

Cited by 202 publications

References 15 publications

Constructing Model for Forecasting the Production using the Classification Technique in Data Mining for the District of Tamil Nadu

Constructing Model for Forecasting the Production using the Classification Technique in Data Mining for the District of Tamil Nadu

Crop Recommendation using Machine Learning Techniques

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