An efficient algorithm for predicting crop using historical data and pattern matching technique

[], Anjana; K, Aishwarya Kedlaya; Sana, Aysha; Bhat, B Apoorva; Kumar, Sharath; Bhat, Nagaraj

doi:10.1016/j.gltp.2021.08.060

Cited by 20 publications

(6 citation statements)

References 7 publications

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“…This is useful for gaining a more comprehensive understanding of rice production in Indonesia. The selection of features that influence rice production results needs to be developed further by adding other features that can influence rice production results, such as soil type (van Klompenburg et al, 2020), soil moisture (Anjana et al, 2021), wind data, irrigation data, and pollution data (Cedric et al, 2022). This was done to see a deeper correlation regarding the factors that influence rice production results.…”

Section: Fig 5 Prediction Resultsmentioning

confidence: 99%

“…This is useful for seeing the effect of the number k on the prediction accuracy results. This research uses R2, MAE, and MSE as evaluation matrices for machine learning algorithm test results; other matrices can be added to measure the accuracy of predictions by calculating the prediction rate and false prediction rate (Anjana et al, 2021).…”

Section: Fig 5 Prediction Resultsmentioning

confidence: 99%

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A Comparative Analysis of Machine Learning Algorithms for Predicting Paddy Production

Aditya,

Munthe,

Sihombing

2024

SinkrOn

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For countries with large populations, such as Indonesia, food security is a very important issue. The majority of Indonesia's population depends on rice as their main food, and paddy is one of the most widely cultivated food commodities. The very good and accurate national paddy production prediction results really support decisions regarding national paddy production targets for the coming period. Therefore, to ensure supply and price stability, paddy availability must be predicted. Many studies have used machine learning to predict crop yields. By learning important patterns and relationships from input data, machine learning can combine the advantages of other methods to make better predictions of paddy yields. The aim of this research is to conduct a comparative analysis between three machine learning algorithms, namely, random forest, decision tree, and k-nearest neighbors, in predicting paddy production. To determine which algorithm is the best, a model evaluation is carried out using the coefficient of determination (R2-score), mean absolute error (MAE), and mean squared error (MSE). This research goes through methodological stages, starting from collecting datasets, data preprocessing, training and testing split datasets, applying algorithms, and evaluating the model. From this research, results were obtained for the random forest algorithm with an R2-score of 82.38%, MAE of 261726.20, and MSE of 2.19495E+11. For the decision tree, the R2-score was 79.62%, MAE was 323257.99, and MSE was 2.49304E+11. Meanwhile, k-nearest neighbors obtained an R2-score of 76.25%, MAE of 318433.42, and MSE of 2.90577E+11. The results of this research show that the random forest algorithm is the best for predicting paddy production because it obtains a larger R2-score as well as smaller MAE and MSE results.

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Section: Fig 5 Prediction Resultsmentioning

confidence: 99%

Section: Fig 5 Prediction Resultsmentioning

confidence: 99%

A Comparative Analysis of Machine Learning Algorithms for Predicting Paddy Production

Aditya,

Munthe,

Sihombing

2024

SinkrOn

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“…A crop recommends by the suggested system [18] aids farmers in choosing the best crop for the season and sowing area by using Pattern Matching. The farmers will benefit as a result since their net profit will increase.…”

Section: Other Techniquesmentioning

confidence: 99%

A Comparative Analysis of Machine Learning Models for Crop Recommendation in India

Reddy,

Neerugatti

2023

RIA

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Agriculture serves as the mainstay of India's economy, bearing a vital responsibility in nourishing an expanding populace. The thriving of this sector is contingent upon numerous variables, among which the choice of the optimal crop plays a pivotal role. The advent of Machine Learning (ML) has engendered a transformative impact on the agricultural sector by facilitating the prediction of suitable crops, contingent on soil attributes. This study undertakes the examination of diverse ML algorithms, encompassing Decision Tree, Linear Regression, Naï ve Bayes, Random Forest, Extreme Gradient Boosting (XGBoost), and Support Vector Machine, to assess their efficacy in recommending optimal crops based on soil parameters. The parameters under consideration include Phosphorus, Nitrogen, Potassium, Electrical Conductivity, pH, Organic Carbon, Boron, Iron, Zinc, Copper, Manganese, and Sulphur. The crop recommendations are focused on Rice, Cotton, and Jowar for the Kurnool district of Andhra Pradesh, India. Among the assessed models, it was observed that the XGBoost model surpassed others in terms of accuracy in determining the most suitable crop for the given soil parameters. The experimental findings substantiate the precision of the model in forecasting the apt crop, thereby underscoring the immense potential of ML in the agricultural domain. This investigation signifies a considerable stride towards optimal crop recommendation, thereby increasing the potential for enhanced yield and profitability. Through the incorporation of technological innovation, agriculture can be rendered more efficient, cost-effective, and sustainable, thus laying the groundwork for a promising future.

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“…The suggested system [10] helps farmers choose the best crops based on the sowing season and region. Farmers will gain from it as well because it would boost their net profit.…”

Section: Literature Surveymentioning

confidence: 99%

A cost-effective farmer support system for better yield prediction and resource management

Kumar¹,

Prajwal²

2022

sajet

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Agriculture is a significant part of India's economy. Agriculture with technology can lead to groundbreaking improvements. Machine learning plays a significant role here, to help predict and formulate essential results. The main focus of this project is to predict and suggest suitable crops to sow in certain soil composition and climatic parameters based on existing historical data collected over years and later subjecting it to Supervised Learning methods and Multi - Class Classification algorithms. So, we propose to create a user-friendly smartphone application that can provide a list of top crops that can be produced in a specific type of soil, and the accuracy of our system can be confirmed using a government website called "Soil Health Card," which collects data on soil composition and its related data.

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An efficient algorithm for predicting crop using historical data and pattern matching technique

Cited by 20 publications

References 7 publications

A Comparative Analysis of Machine Learning Algorithms for Predicting Paddy Production

A Comparative Analysis of Machine Learning Algorithms for Predicting Paddy Production

A Comparative Analysis of Machine Learning Models for Crop Recommendation in India

A cost-effective farmer support system for better yield prediction and resource management

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