Analysis of Soil Properties and Climatic Data to Predict Crop Yields and Cluster Different Agricultural Regions of Bangladesh

Afrin, Sadia; Khan, Abu Talha; Mahia, Mahrin Alam; Ahsan, Rahbar; Mishal, Mahbubur Rahman; Ahmed, Wasit; Rahman, Rashedur M.

doi:10.1109/icis.2018.8466397

Cited by 26 publications

(6 citation statements)

References 2 publications

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“…This methodology consists of a data mining technique that groups the subjects into homogeneous clusters regarding the criterion of observation density in a specific radius. This technique was proposed by Ester et al [ 53 ] and has been recently applied to agriculture-related research [ 54 , 55 , 56 , 57 ].…”

Section: Methodsmentioning

confidence: 99%

Aquifer Sustainability and the Use of Desalinated Seawater for Greenhouse Irrigation in the Campo de Níjar, Southeast Spain

Aznar-Sánchez

Ureña

Velasco-Muñoz

et al. 2019

IJERPH

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In the Campo de Níjar (southeast Spain), an intensive horticulture model under plastic has been developed based on the use of groundwater. For many years, aquifers have been overexploited, almost generating an environmental collapse. The construction of a desalination plant was planned to improve this situation and to achieve sustainable aquifer management. However, the aquifer is still being overexploited, since farmers scarcely use desalinated seawater for irrigation. In this paper, farmers irrigating with desalinated seawater are characterized, since they contribute to aquifer sustainability. The study aimed to identify the variables which condition the use of this water resource, as well as the kinds of incentives that encourage this option. For this purpose, a survey was undertaken within a sample of 110 farmers. A cluster analysis and a binary logistic regression were employed. The results from the cluster analysis allowed the characterization of farmers who use desalinated seawater for irrigation. Furthermore, the regression model showed the variables that determine a more intensive use of this irrigation source, such as crop diversification, availability of different water resources and the conductivity of aquifer water available for irrigation. The incentives promoting the use of desalinated seawater for irrigation that most encourage farmers are the implementation of tax relief, price reductions and the obligation to install rainwater collection systems.

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Section: Methodsmentioning

confidence: 99%

Aquifer Sustainability and the Use of Desalinated Seawater for Greenhouse Irrigation in the Campo de Níjar, Southeast Spain

Aznar-Sánchez

Ureña

Velasco-Muñoz

et al. 2019

IJERPH

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“…Afrin et al 34 introduced an analysis technique to predict the yield of crops in Bangladesh by analyzing soil properties and climatic conditions. The most‐yielding and popular crops were considered to accurately predict the crop yield improvement rate through data‐mining techniques.…”

Section: Related Workmentioning

confidence: 99%

Hybrid bidirectional long short term memory with black widow optimization for crop yield prediction using data mining

Joseph

Ramasamy

2023

Concurrency and Computation

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SummaryCrop yield prediction is highly significant in the agricultural sector. It helps to understand the growth rate of major food crops and identify measures to improve the overall yield. The article proposes a hybrid strategy called bidirectional long short term memory with black widow optimization (Bi‐LSTM‐BWO) for predicting the annual yield produced with improved accuracy. Initially, data augmentation is performed for the collected dataset to increase the size of the dataset and to reduce the data scarcity problem. Then, the dataset is preprocessed to improve the data's quality and remove the noise and irrelevant information. The data is cleaned, transformed, and discretized in the preprocessing stage using various techniques. Then, the preprocessed data is clustered using an enhanced K‐means clustering technique. To enhance the clustering technique, the proposed technique utilized the rain optimization algorithm that automatically computes the initial centroids to improve the clustering outcome. Finally, the prediction process is performed using the proposed Bi‐LSTM‐BWO prediction scheme. The proposed prediction strategy efficiently predicts the annual yield with a high accuracy rate and minimizes loss. The proposed technique achieves a 99.18%, 99.81% and 99.01% accuracy rates for the summer, autumn and winter yield prediction, respectively.

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“…Soil variables are used for statistical analysis and scientific purposes. For predicting crop yield, a similar pace is chosen with similar soil qualities and meteorological circumstances [34].…”

Section: Challenges and Limitations In Predicting The Crop Yieldmentioning

confidence: 99%

A Comprehensive Review on Machine Learning Approaches for Yield Prediction Using Essential Soil Nutrients

Prabavathi¹,

Chelliah²

2022

ujar

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Agriculture is the backbone of India's economy, as it is the most important factor in the country's socio-economic development. Because of the rapid expansion in human population, the "Green Revolution" introduced high yield variety (HYV) seeds, which increased crop productivity but degraded crop and soil quality. This is due to the use of excessive amounts of chemical fertilizers in HYV seeds, as well as the irrigation system utilized to grow these seeds. This stunts the growth of the crops, resulting in financial and productivity losses. Because of field surveys, traditional ways to crop production prediction will take longer, and contemporary agriculture will face certain obstacles. As a result, a comprehensive review of various crop key factors such as climatic factors, soil nutrients, production factors, and environmental factors is conducted using a variety of machine learning approaches such as Support Vector Machine, bayes classifier, decision tree, random forest, linear regression and Extreme Learning Machines. The accuracy measures such as root mean square error, coefficient of determination and mean absolute error are used for comparing the performance of the system. Based on the findings of the reviews, an intelligent and robust machine learning technique provides the optimum option for achieving (i) soil fertility, (ii) crop prediction, and (iii) yield prediction. The importance of soil variables and the amount of nutrients available in the soil for growing crops has been found, according to an examination of 51 peer-reviewed studies, to create qualitative yield prediction. Furthermore, the investigations will yield recommendations for future fertilizer research.

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Analysis of Soil Properties and Climatic Data to Predict Crop Yields and Cluster Different Agricultural Regions of Bangladesh

Cited by 26 publications

References 2 publications

Aquifer Sustainability and the Use of Desalinated Seawater for Greenhouse Irrigation in the Campo de Níjar, Southeast Spain

Aquifer Sustainability and the Use of Desalinated Seawater for Greenhouse Irrigation in the Campo de Níjar, Southeast Spain

Hybrid bidirectional long short term memory with black widow optimization for crop yield prediction using data mining

A Comprehensive Review on Machine Learning Approaches for Yield Prediction Using Essential Soil Nutrients

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