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Automated Estimation of Crop Yield Using Artificial Intelligence and Remote Sensing Technologies.

Qazi Mudassar Ilyas1, Muneer Ahmad2, Abid Mehmood3

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Summary

This study introduces a novel fuzzy hybrid ensemble method for remote crop classification and yield estimation using satellite imagery. The approach significantly improves accuracy over traditional methods, supporting precision agriculture and food security initiatives.

Keywords:
data analysisdata augmentationdeep learningfeature extractionprecision agriculturesensory images

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Remote Sensing

Background:

  • Traditional crop monitoring methods face challenges due to environmental variability and geographical diversity.
  • Accurate crop classification and yield estimation are crucial for national food security and economic stability.
  • Precision agriculture offers advanced solutions for overcoming limitations in conventional crop management.

Purpose of the Study:

  • To develop and evaluate a fuzzy hybrid ensemble method for enhanced remote crop type classification and yield estimation.
  • To leverage remote sensing data for more effective agricultural monitoring.
  • To improve the accuracy and efficiency of crop assessment compared to existing techniques.

Main Methods:

  • A fuzzy hybrid ensemble classification and estimation method was proposed, utilizing remote sensory data.
  • Image enhancement techniques included fuzzy neighborhood spatial filtering, scaling, flipping, shearing, and zooming.
  • A bagging strategy was employed to identify optimal weights for candidate classifiers, and imagery datasets were augmented for unbiased classification.
  • Publicly available datasets from the Food and Agriculture Organization (FAO) and Word Bank DataBank were used for yield estimation.

Main Results:

  • The ensemble method demonstrated superior performance, outperforming individual classifiers by an average of 13% and 24% for crop type classification.
  • Gradient boosting outperformed other regressors (multivariate regressor, random forest, decision tree) with a lower mean square error for yield estimation (2017-2021).
  • The proposed architecture supports embedded devices with lightweight algorithms like MobilenetV2, reducing processing time and overhead.

Conclusions:

  • The fuzzy hybrid ensemble method offers a significant advancement in remote crop classification and yield estimation.
  • This approach enhances the capabilities of precision agriculture, contributing to more robust food security strategies.
  • The method's adaptability to embedded devices makes it a scalable and efficient tool for modern agriculture.