Crop Yield Prediction using XG Boost Algorithm

,; Ravi*, Rohit; Baranidharan, Dr. B.; ,

doi:10.35940/ijrte.d9547.018520

Cited by 8 publications

(3 citation statements)

References 2 publications

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“…Additionally, GB construct highly efficient, more accurate, high quality ML models in a lesser amount of time. Ravi and Baranidharan [ 56 ] and Cai et al [ 57 ] state that GB is faster compared to all ML algorithms. Cai et al [ 57 ] presented an approach based on GB algorithm to identify a black box model of greenhouse temperature with environment and control data.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Crop Stress Detection in Greenhouses

Elvanidi

Katsoulas

2022

Plants

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Greenhouse climate control systems are usually based on greenhouse microclimate settings to exert any control. However, to save energy, water and nutrients, additional parameters related to crop performance and physiology will have to be considered. In addition, detecting crop stress before it is clearly visible by naked eye is an advantage that could aid in microclimate control. In this study, a Machine Learning (ML) model which takes into account microclimate and crop physiological data to detect different types of crop stress was developed and tested. For this purpose, a multi-sensor platform was used to record tomato plant physiological characteristics under different fertigation and air temperature conditions. The innovation of the current model lies in the integration of photosynthesis rate (Ps) values estimated by means of remote sensing using a photochemical reflectance index (PRI). Through this process, the time-series Ps data were combined with crop leaf temperature and microclimate data by means of the ML model. Two different algorithms were evaluated: Gradient Boosting (GB) and MultiLayer perceptron (MLP). Two runs with different structures took place for each algorithm. In RUN 1, there were more feature inputs than the outputs to build a model with high predictive accuracy. However, in order to simplify the process and develop a user-friendly approach, a second, different run was carried out. Thus, in RUN 2, the inputs were fewer than the outputs, and that is why the performance of the model in this case was lower than in the case of RUN 1. Particularly, MLP showed 91% and 83% accuracy in the training sample, and 89% and 82% in testing sample, for RUNs 1 and 2, respectively. GB showed 100% accuracy in the training sample for both runs, and 91% and 83% in testing sample in RUN 1 and RUN 2, respectively. To improve the accuracy of RUN 2, a larger database is required. Both models, however, could easily be incorporated into existing greenhouse climate monitoring and control systems, replacing human experience in detecting greenhouse crop stress conditions.

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Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Crop Stress Detection in Greenhouses

Elvanidi

Katsoulas

2022

Plants

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“…To estimate the crop's yield, Rohit Ravi et al (3) created a model utilizing the XG Boost algorithm. Predicting agricultural yields using data on cultivated area, rainfall, and maximum and lowest temperatures is the primary goal of this study.…”

Section: Fig 1 Example Landsat and Sentinel Imagementioning

confidence: 99%

Jowar and Wheat Yield Prediction using a Wavelet based Fusion of Landsat and Sentinel Data with Meteorological Parameters

Linkesh,

Ghorpade,

Prasad

2024

IJST

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Objectives: The objective of this study is to improve the accuracy of crop yield prediction models, specifically focusing on wheat and jowar crops in Maharashtra during the Rabi season, by integrating Landsat and Sentinel satellite data with meteorological parameters. Methods: The study utilizes Landsat 8 and Sentinel satellite datasets covering Maharashtra State. Atmospheric correction is applied to extract surface properties, followed by wavelet-based fusion to combine the images. Normalized Difference Vegetation Index (NDVI) is calculated and combined with meteorological parameters using ensemble learning techniques, including Random Forest and Ada-Boost algorithms. Comparative analysis is conducted against existing models, considering parameters such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Findings: Significant findings reveal that the proposed methodology outperforms existing models, achieving lower MAE, MSE, and RMSE values for wheat and jowar yield predictions. Additionally, our research highlights the superiority of wheat production over jowar in the Rabi season, based on comprehensive analysis of crop yield predictions. Novelty: This study introduces a novel approach that integrates multiple data sources and employs ensemble learning techniques to enhance crop yield prediction accuracy. By combining Landsat and Sentinel satellite data with meteorological parameters, our methodology provides a more comprehensive understanding of crop growth dynamics, leading to more reliable predictions compared to existing methods. Keywords: Satellite imagery, Machine learning, Normalized Difference Vegetation Index, Fusion, Ensemble learning

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“…The performance of the XGBoost model in estimating winter wheat yield, as evidenced in this study, suggests a moderate level of predictive ability, albeit with some discrepancies between predicted and actual values. Rohit et al [41] also employed the XGBoost algorithm for yield estimation and highlighted the high accuracy in yield prediction. However, these studies cannot be directly compared due to different environmental conditions.…”

Section: Estimating Winter Wheat Yieldmentioning

confidence: 99%

Analyzing Winter Wheat (Triticum aestivum) Growth Pattern Using High Spatial Resolution Images: A Case Study at Lakehead University Agriculture Research Station, Thunder Bay, Canada

Fuentes,

Heenkenda,

Sahota

et al. 2024

Crops

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Remote sensing technology currently facilitates the monitoring of crop development, enabling detailed analysis and monitoring throughout the crop’s growing stages. This research analyzed the winter wheat growth dynamics of experimental plots at the Lakehead University Agricultural Research Station, Thunder Bay, Canada using high spatial and temporal resolution remote sensing images. The spectral signatures for five growing stages were prepared. NIR reflectance increased during the growing stages and decreased at the senescence, indicating healthy vegetation. The space–time cube provided valuable insight into how canopy height changed over time. The effect of nitrogen treatments on wheat did not directly influence the plant count (spring/autumn), and height and volume at maturity. However, the green and dry weights were different at several treatments. Winter wheat yield was predicted using the XGBoost algorithm, and moderate results were obtained. The study explored different techniques for analyzing winter wheat growth dynamics and identified their usefulness in smart agriculture.

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Crop Yield Prediction using XG Boost Algorithm

Cited by 8 publications

References 2 publications

Machine Learning-Based Crop Stress Detection in Greenhouses

Machine Learning-Based Crop Stress Detection in Greenhouses

Jowar and Wheat Yield Prediction using a Wavelet based Fusion of Landsat and Sentinel Data with Meteorological Parameters

Analyzing Winter Wheat (Triticum aestivum) Growth Pattern Using High Spatial Resolution Images: A Case Study at Lakehead University Agriculture Research Station, Thunder Bay, Canada

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