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<p>Advanced attention mechanisms considerably improve crop classification using time-series and frequency information. Self-attention reigns supreme at identifying detailed temporal patterns and pointing out key features within remote sensing data. Tanh-activated self-attention achieved the highest accuracy (88.89%), over multiplicative attention (85.67%), soft attention (82.98%) and global attention (82.12%). Focusing on key time-based and frequency-based patterns, these techniques helped in improving the model’s ability to accurately differentiate between crop types. The study introduces a new method for accurate crop classification; this method uses vegetation indices along with attention mechanisms. Agricultural monitoring faces challenges. These include temporal changes, complex spectral data and variable ecological conditions. This method incorporates vegetation index data and attention-based deep learning to address them. This framework investigates the interplay of vegetation indices along with attention mechanisms across multiple ecological conditions as well as plant growth stages, using advanced techniques such as data resampling, feature engineering and machine learning.</p>

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Deep learning techniques for crop classification in complex agricultural landscapes

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月12日

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  • Megha Sharma,
  • Anil Kumar,
  • Supriya Muthuraman,
  • Parth Chaturvedi

摘要

Advanced attention mechanisms considerably improve crop classification using time-series and frequency information. Self-attention reigns supreme at identifying detailed temporal patterns and pointing out key features within remote sensing data. Tanh-activated self-attention achieved the highest accuracy (88.89%), over multiplicative attention (85.67%), soft attention (82.98%) and global attention (82.12%). Focusing on key time-based and frequency-based patterns, these techniques helped in improving the model’s ability to accurately differentiate between crop types. The study introduces a new method for accurate crop classification; this method uses vegetation indices along with attention mechanisms. Agricultural monitoring faces challenges. These include temporal changes, complex spectral data and variable ecological conditions. This method incorporates vegetation index data and attention-based deep learning to address them. This framework investigates the interplay of vegetation indices along with attention mechanisms across multiple ecological conditions as well as plant growth stages, using advanced techniques such as data resampling, feature engineering and machine learning.