An Ensemble Algorithm for Crop Yield Prediction

Keerthana, M. Krishna; Meghana, K.; Pravallika, Siginamsetty; Kavitha, M.

doi:10.1109/icicv50876.2021.9388479

Cited by 46 publications

(12 citation statements)

References 9 publications

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“…Keerthana Mummaleti et al [30] analyzed the usage and implementation of ensemble techniques in predicting the crop type from location parameters, by retrieving 7 features from various databases with 28,252 instances. The paper concluded that an ensemble of decision tree regression and Ada boost regression gave the best accuracy, giving a recommendation of which crop should be cultivated in the region based on weather conditions.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning-Based Crop Yield Prediction in South India: Performance Analysis of Various Models

Nikhil,

Pandiyan,

Raja

et al. 2024

Computers

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Agriculture is one of the most important activities that produces crop and food that is crucial for the sustenance of a human being. In the present day, agricultural products and crops are not only used for local demand, but globalization has allowed us to export produce to other countries and import from other countries. India is an agricultural nation and depends a lot on its agricultural activities. Prediction of crop production and yield is a necessary activity that allows farmers to estimate storage, optimize resources, increase efficiency and decrease costs. However, farmers usually predict crops based on the region, soil, weather conditions and the crop itself based on experience and estimates which may not be very accurate especially with the constantly changing and unpredictable climactic conditions of the present day. To solve this problem, we aim to predict the production and yield of various crops such as rice, sorghum, cotton, sugarcane and rabi using Machine Learning (ML) models. We train these models with the weather, soil and crop data to predict future crop production and yields of these crops. We have compiled a dataset of attributes that impact crop production and yield from specific states in India and performed a comprehensive study of the performance of various ML Regression Models in predicting crop production and yield. The results indicated that the Extra Trees Regressor achieved the highest performance among the models examined. It attained a R-Squared score of 0.9615 and showed lowest Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) of 21.06 and 33.99. Following closely behind are the Random Forest Regressor and LGBM Regressor, achieving R-Squared scores of 0.9437 and 0.9398 respectively. Moreover, additional analysis revealed that tree-based models, showing a R-Squared score of 0.9353, demonstrate better performance compared to linear and neighbors-based models, which achieved R-Squared scores of 0.8568 and 0.9002 respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Machine Learning-Based Crop Yield Prediction in South India: Performance Analysis of Various Models

Nikhil,

Pandiyan,

Raja

et al. 2024

Computers

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“…Agricultural Application [18,26] Decision Tree Crop Yield Prediction, Disease Detection, Soil Assessment [18][19][20] Random Forest Crop Yield Prediction, Disease Detection, Soil Assessment [18,27] Extreme Gradient Boosting Crop Yield Prediction, Soil Assessment [18,20] Naive Bayes Crop Yield Prediction, Disease Detection [18,21] K-Nearest Neighbors Crop Yield Prediction, Disease Detection [28] Ensemble Traditional ML Models Crop Yield Prediction [26] Multi-Linear Regressor Crop Yield Prediction [29] RNN Crop Yield Prediction [29] LSTM Crop Yield Prediction [29] Support Vector Regression Crop Yield Prediction [23,24,30,31] CNN Crop Yield Prediction, Disease Detection [30] GNN Crop Yield Prediction [30] U-Net Crop Yield Prediction [23,25,32] ANN Crop Yield Prediction, Disease Detection [25] DBSCAN Crop Yield Prediction [23,25] Support Vector Machine Crop Yield Prediction, Disease Detection, Smart Farming [33] Vision Transformers Disease Detection [22] VGG-RNN Hybrid Soil Assessment [23,24] MLP Soil Assessment…”

Section: Techniquementioning

confidence: 99%

A Review of Machine Learning Techniques in Agroclimatic Studies

Tamayo-Vera,

Wang,

Mesbah

2024

Agriculture

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The interplay of machine learning (ML) and deep learning (DL) within the agroclimatic domain is pivotal for addressing the multifaceted challenges posed by climate change on agriculture. This paper embarks on a systematic review to dissect the current utilization of ML and DL in agricultural research, with a pronounced emphasis on agroclimatic impacts and adaptation strategies. Our investigation reveals a dominant reliance on conventional ML models and uncovers a critical gap in the documentation of methodologies. This constrains the replicability, scalability, and adaptability of these technologies in agroclimatic research. In response to these challenges, we advocate for a strategic pivot toward Automated Machine Learning (AutoML) frameworks. AutoML not only simplifies and standardizes the model development process but also democratizes ML expertise, thereby catalyzing the advancement in agroclimatic research. The incorporation of AutoML stands to significantly enhance research scalability, adaptability, and overall performance, ushering in a new era of innovation in agricultural practices tailored to mitigate and adapt to climate change. This paper underscores the untapped potential of AutoML in revolutionizing agroclimatic research, propelling forward the development of sustainable and efficient agricultural solutions that are responsive to the evolving climate dynamics.

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“…The paper aims to demonstrate the feasibility and accuracy of using SVM for predicting rice crop yields, which has implications for optimizing agricultural practices and food security in India. Authors [17] introduce an ensemble algorithm for predicting crop yields. The authors propose a method that combines multiple predictive models or algorithms to create a more accurate and reliable prediction for crop yields.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Crop Yield Prediction Framework Using Hybrid Machine Learning Model

Manjunath,

Palayyan

2023

RIA

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Given India's vast expanse and dense population, the prediction of agricultural yields is crucial for ensuring food security. The task, however, is complex due to the influence of a multitude of factors, such as agricultural practices, environmental conditions, and technological advancements. Existing machine learning (ML) models face difficulties due to the quality and variability of data, model overfitting, intricate model structures, insufficient feature engineering, and temporal dependencies. Therefore, a robust and efficient model that addresses these challenges is imperative. In this study, an investigation was conducted using five prevalent ML algorithms -Random Forest (RF), XGBoost, Decision Tree (DT), Support Vector Machine (SVM), and Linear Regression (LR)on a crop prediction dataset sourced from Kaggle. Algorithms that exhibited the highest coefficient of determination (R² ) were selected to construct a hybrid model for aggregate prediction. Results demonstrated that the proposed hybrid model, encompassing DT, XGBoost, and RF, surpassed individual classifiers in terms of R² score and outperformed the existing models, achieving an accuracy of 98.6%. This provides a robust and efficient framework for crop yield predictions. Consequently, a user-friendly tool, 'Crop Yield Predictor', was developed, rendering the model accessible and practical for on-ground applications in agriculture. This tool effectively translates complex data and algorithms into actionable insights, bridging the gap between advanced machine learning techniques and practical agricultural applications.

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An Ensemble Algorithm for Crop Yield Prediction

Cited by 46 publications

References 9 publications

Machine Learning-Based Crop Yield Prediction in South India: Performance Analysis of Various Models

Machine Learning-Based Crop Yield Prediction in South India: Performance Analysis of Various Models

A Review of Machine Learning Techniques in Agroclimatic Studies

An Efficient Crop Yield Prediction Framework Using Hybrid Machine Learning Model

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