Agriculture Analysis Using Data Mining And Machine Learning Techniques

Cn, Vanitha; Archana, N.; Sowmiya, R.

doi:10.1109/icaccs.2019.8728382

Cited by 19 publications

(6 citation statements)

References 6 publications

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“…The dataset spans the years 2000-2018 and contains information on rainfall, temperature, crop, ET, acreage, and output. Precision was increased with the help of K-means clustering, K-nearest neighbours, support vector machines, and Bayesian network techniques [11]. This Provides a baseline for monitoring and assessing Indian agriculture.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning based Disease and Pest detection in Agricultural Crops

Nelson,

et al. 2024

EAI Endorsed Trans IoT

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INTRODUCTION: Most Indians rely on agricultural work as their primary means of support, making it an essential part of the country’s economy. Disasters and the expected loss of farmland by 2050 as a result of global population expansion raise concerns about food security in that year and beyond. The Internet of Things (IoT), Big Data and Analytics are all examples of smart agricultural technologies that can help the farmers enhance their operation and make better decisions. OBJECTIVES: In this paper, machine learning based system has been developed for solving the problem of crop disease and pest prediction, focussing on the chilli crop as a case study. METHODS: The performance of the suggested system has been assessed by employing performance metrics like accuracy, Mean Squared Error (MSE), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). RESULTS: The experimental results reveals that the proposed method obtained accuracy of 0.90, MSE of 0.37, MAE of 0.15, RMSE of 0.61 CONCLUSION: This model will predict pests and diseases and notify farmers using a combination of the Random Forest Classifier, the Ada Boost Classifier, the K Nearest Neighbour, and Logistic Regression. Random Forest is the most accurate model.

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Section: Related Workmentioning

confidence: 99%

Machine Learning based Disease and Pest detection in Agricultural Crops

Nelson,

et al. 2024

EAI Endorsed Trans IoT

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“…In 2019, Vanitha et al [22] explored tech's role in agriculture. They advocated Python as a front end for agri-data analysis, using Jupyter for crop prediction via k-means, k-nearest neighbors (KNN), support vector machine (SVM), and Bayesian network algorithms.…”

Section: Literature Surveymentioning

confidence: 99%

Enhancing data analysis through k-means with foggy centroid selection

2023

IJACR

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Clustering algorithms have emerged as indispensable tools across diverse sectors such as engineering, ecommerce, and healthcare in the contemporary era. Among the prominently utilized methods, k-means, fuzzy c-means, and hierarchical clustering have garnered substantial attention [1,2]. The effectiveness of these algorithms hinges on their ability to discern data clusters through inter-centroid calculations [2].

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“…In 2019, Vanitha et al [20] discussed about the modern technology and their prospect helpful in agriculture field for the farmers. They have suggested that the python can be used as a front end for analyzing the agricultural data set.…”

Section: Literature Surveymentioning

confidence: 99%

An Efficient K-Means Method Based on Centroid Handling for the Similarity Estimation

Ladha,

Pippal

2019

IJEAT

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The main aim of this paper is to handle centroid calculation in k-means efficiently. So that the distance estimation will be more accurate and prominent results will be fetched in terms of clustering. For this PIMA database has been considered. Data preprocessing has been performed for the unwanted data removal in terms of missing values. Then centroid initialization has been performed based on centroid tuning and randomization. For distance estimation Euclidean, Pearson Coefficient, Chebyshev and Canberra algorithms has been used. In this paper the evaluation has been performed based on the computational time analysis. The time calculation has been performed on different random sets. It is found to be prominent in all the cases considering the variations in all aspects of distance and population

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Agriculture Analysis Using Data Mining And Machine Learning Techniques

Cited by 19 publications

References 6 publications

Machine Learning based Disease and Pest detection in Agricultural Crops

Machine Learning based Disease and Pest detection in Agricultural Crops

Enhancing data analysis through k-means with foggy centroid selection

An Efficient K-Means Method Based on Centroid Handling for the Similarity Estimation

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