A Comparative Study on Various Data Mining Algorithms with Special Reference to Crop Yield Prediction

Patel, Hetal; Patel, Dharmendra

doi:10.17485/ijst/2016/v9i22/92713

Cited by 8 publications

(4 citation statements)

References 25 publications

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“…The crop yield is a function of several variables, including soil moisture, soil fertility, weather conditions and health of the plants during the crop growing season [82]- [83]. During the 1980's, the yield was predicted based on the past years' yield scenario and the current weather conditions [84].…”

Section: E Crop Yieldmentioning

confidence: 99%

Advances in Radar Remote Sensing of Agricultural Crops: A Review

Sivasankar

Kumar

Srivastava

et al. 2018

International Journal on Advanced Science, Engineering and Information Technology

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There are enormous advantages of a review article in the field of emerging technology like radar remote sensing applications in agriculture. This paper aims to report select recent advancements in the field of Synthetic Aperture Radar (SAR) remote sensing of crops. In order to make the paper comprehensive and more meaningful for the readers, an attempt has also been made to include discussion on various technologies of SAR sensors used for remote sensing of agricultural crops viz. basic SAR sensor, SAR interferometry (InSAR), SAR polarimetry (PolSAR) and polarimetric interferometry SAR (PolInSAR). The paper covers all the methodologies used for various agricultural applications like empirically based models, machine learning based models and radiative transfer theorem based models. A thorough literature review of more than 100 research papers indicates that SAR polarimetry can be used effectively for crop inventory and biophysical parameters estimation such are leaf area index, plant water content, and biomass but shown less sensitivity towards plant height as compared to SAR interferometry. Polarimetric SAR Interferometry is preferable for taking advantage of both SAR polarimetry and SAR interferometry. Numerous studies based upon multi-parametric SAR indicate that optimum selection of SAR sensor parameters enhances SAR sensitivity as a whole for various agricultural applications. It has been observed that researchers are widely using three models such are empirical, machine learning and radiative transfer theorem based models. Machine learning based models are identified as a better approach for crop monitoring using radar remote sensing data. It is expected that the review article will not only generate interest amongst the readers to explore and exploit radar remote sensing for various agricultural applications but also provide a ready reference to the researchers working in this field. Keywords-biophysical parameters retrieval; crop inventory; synthetic aperture radar (SAR); synthetic aperture radar interferometry (InSAR); SAR polarimetry (PolSAR); polarimetric SAR interferometry (PolInSAR).

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Section: E Crop Yieldmentioning

confidence: 99%

Advances in Radar Remote Sensing of Agricultural Crops: A Review

Sivasankar

Kumar

Srivastava

et al. 2018

International Journal on Advanced Science, Engineering and Information Technology

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“…The dataset employed in this study for Crop Yield Prediction was sourced from the Food and Agriculture Organization (FAO) and the World Data Bank, both publicly accessible [48]- [50]. These databases furnish information on annual precipitation, mean annual temperature, pesticide application across various crops, and the outcomes of these applications.…”

Section: Data Collectionmentioning

confidence: 99%

Incorporating Meteorological Data and Pesticide Information to Forecast Crop Yields Using Machine Learning

Hoque,

Islam,

Uddin

et al. 2024

IEEE Access

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The agricultural sector is more vulnerable to the adverse effects of climate change and excessive pesticide application, posing a significant risk to global food security. Accurately predicting crop yields is essential for mitigating these risks and providing information for sustainable agricultural practices. This research presents a novel crop yield prediction system utilizing a year's worth of meteorological data, pesticide records, crop yield data, and machine learning techniques. We employed rigorous methods to gather, clean, and enhance data and then trained and evaluated three machine learning models: Gradient Boosting, K-Nearest Neighbors, and Multivariate Logistic Regression. We utilized the GridSearchCV method for hyper-parameter tweaking to identify the most suitable hyper-parameter throughout K-Fold cross-validation, aiming to improve the model's performance by avoiding overfitting. The remarkable performance of the Gradient Boosting model, with an almost flawless coefficient of determination (R 2 ) of 99.99%, demonstrates its promise for precise yield prediction. This research also examined the correlation between projected and actual crop yields and identified the ideal meteorological conditions. It paves the way for data-driven methods in sustainable agriculture and resource distribution, ultimately leading to a more secure future regarding food availability and robustness to climate change.INDEX TERMS Agriculture; crop yield prediction; machine learning; deep learning

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“…Data analysis has numerous applications in farming. The scientists [2] looked at the efficacy of information in the classification algorithms J48, Random Forest, and Ada-Boost, and Using a variety of rating criteria, such as the false positive rate, actual positive rate, precision, ROC area, and F-Measure, Naive Bayes is used. It facilitates the creation of an effective crop forecast system..…”

Section: Literature Reviewmentioning

confidence: 99%

Crop Yield Prediction Using Machine Learning: A Pragmatic Approach

Surana,

Khandelwal

2024

Preprint

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The agricultural sector is the major driver of revenues in India. organic , economic, and seasonal factors all have got a bearing on an agricultural producer's manufacturing. Accurate crop yield prediction (CYP) is required due to the agricultural indus-try's rapid innovation and liberalized market economy. Accurate prediction is greatly aided by the chosen characteristics and machine learning (ML) techniques Any ML Algorithm’s performance may be enhanced by using a unique set of features from the same training dataset. This study assesses the key characteristics of an accurate crop yield prediction. For greater accuracy, it uses machine learning (ML) methods like Random Forest (RF), Adaboost, Gradient Boost, and Support Vector Machine (SVM). The agriculture dataset has 2201 instances in it. 80% of them are randomly chosen for themodeìs training, while 20% are used to test themodeìs predictive power. The results show that the Random Forest approach gets the highest level of accuracy. The goal of this paper is to predict crop yields. Using different Machine Learning Algorithms so that farmers can make their yields higher.

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A Comparative Study on Various Data Mining Algorithms with Special Reference to Crop Yield Prediction

Cited by 8 publications

References 25 publications

Advances in Radar Remote Sensing of Agricultural Crops: A Review

Advances in Radar Remote Sensing of Agricultural Crops: A Review

Incorporating Meteorological Data and Pesticide Information to Forecast Crop Yields Using Machine Learning

Crop Yield Prediction Using Machine Learning: A Pragmatic Approach

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