Analysis of agriculture data using data mining techniques: application of big data

Majumdar, Jharna; Naraseeyappa, Sneha; Ankalaki, Shilpa

doi:10.1186/s40537-017-0077-4

Cited by 172 publications

(75 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These include crop yield increments, pest control, early warning, and farm management. In [13], data mining techniques were applied on crop, soil and climatic datasets to maximise the crop production. While, [4] combined surveys of farming practices with model-based simulations to determine the relation between weeds and crop yield.…”

Section: Related Workmentioning

confidence: 99%

Crop Knowledge Discovery Based on Agricultural Big Data Integration

Ngo

Kechadi

2020

Proceedings of the 4th International Conference on Machine Learning and Soft Computing

View full text Add to dashboard Cite

Nowadays, the agricultural data can be generated through various sources, such as: Internet of Thing (IoT), sensors, satellites, weather stations, robots, farm equipment, agricultural laboratories, farmers, government agencies and agribusinesses. The analysis of this big data enables farmers, companies and agronomists to extract high business and scientific knowledge, improving their operational processes and product quality. However, before analysing this data, different data sources need to be normalised, homogenised and integrated into a unified data representation. In this paper, we propose an agricultural data integration method using a constellation schema which is designed to be flexible enough to incorporate other datasets and big data models. We also apply some methods to extract knowledge with the view to improve crop yield; these include finding suitable quantities of soil properties, herbicides and insecticides for both increasing crop yield and protecting the environment.

show abstract

Section: Related Workmentioning

confidence: 99%

Crop Knowledge Discovery Based on Agricultural Big Data Integration

Ngo

Kechadi

2020

Proceedings of the 4th International Conference on Machine Learning and Soft Computing

View full text Add to dashboard Cite

show abstract

“…Existing machine learning techniques can be applied to a wide array of problems, such as using patient and population-level data in health informatics [24], applying deep learning models to AI tasks [38], or leveraging clustering algorithms to improve agricultural yield estimates [36]. For example, text mining can be used not only to extract libraries from source code, but also to determine the key words in a health study [26] or infer product characteristics from user reviews [39].…”

Section: Big Data Analysismentioning

confidence: 99%

Library adoption in public software repositories

Krohn

Weninger

2019

J Big Data

View full text Add to dashboard Cite

We study the the spread and adoption of libraries within Python projects hosted in public software repositories on GitHub. By modelling the use of Git pull, merge, commit, and other actions as deliberate cognitive activities, we are able to better understand the dynamics of what happens when users adopt new and cognitively demanding information. For this task we introduce a large corpus containing all commits, diffs, messages, and source code from 259,690 Python repositories (about 13% of all Python projects on Github), including all Git activity data from 89,311 contributing users. In this initial work we ask two primary questions: (1) What kind of behavior change occurs near an adoption event? (2) Can we model future adoption activity of a user? Using a fine-grained analysis of user behavior, we show that library adoptions are followed by higher than normal activity within the first 6 h, implying that a higher than normal cognitive effort is involved with an adoption. Further study is needed to understand the specific types of events that surround the adoption of new information, and the cause of these dynamics. We also show that a simple linear model is capable of classifying future commits as being an adoption or not, based on the commit contents and the preceding history of the user and repository. Additional work in this vein may be able to predict the content of future commits, or suggest new libraries to users.

show abstract

“…This exploration can be reached out by considering different elements like Minimum Support Price (MSP), Cost Price Index (CPI), Wholesale Price Index (WPI) and so on and their association with harvest yield. Jharna Majumdar et.al [2] has expanded the work referenced in [1] by utilizing different linear regression in their investigation model. Numerous linear regression is a variety of " linear regression " investigation.…”

Section: Literature Surveymentioning

confidence: 99%

Data Analytics on Agrometeorological Parameters for Building A Utility System for Farmer Community

Sowmya¹,

Mundada²,

Hegde³

et al. 2019

IJRTE

View full text Add to dashboard Cite

The day-to-day evolution of the world is throwing many challenges, thereby demanding the humans to be on par with the modern tools and technologies that are on offer. These technologies are contributing to the overall growth across all the domains. One such domain that is highly affected by the modern tools and techniques is Agriculture. Gone are the days, when only a handful methodologies and tools were utilized to understand about agriculture. Although the farmers are sceptical to the modernization, the end result has been encouraging at many a times. With the use of analytics, one can get to know more about the type of soil, crops and fertilizers, amount of water to be utilized depending on the climatic conditions and thereby have an effective yield. In this work, the elementary task of portraying the effect of climatic conditions on the production of different varieties of crops is carried out. Also, the application of Multivariate Linear Regression and Artificial Neural Network (ANN) techniques provides a significance outcome in the prediction of yield with the focus on Ragi and Rice. The work provides an accuracy of 65% with the 3-fold cross validation technique and 68% accuracy with ANN model.

show abstract

Analysis of agriculture data using data mining techniques: application of big data

Cited by 172 publications

References 12 publications

Crop Knowledge Discovery Based on Agricultural Big Data Integration

Crop Knowledge Discovery Based on Agricultural Big Data Integration

Library adoption in public software repositories

Data Analytics on Agrometeorological Parameters for Building A Utility System for Farmer Community

Contact Info

Product

Resources

About