Effective Feature Extraction Using Residual Attention and Local Context Aware Classifier for Crop Yield Prediction

Crop yield forecasting plays a key role in agricultural management and planning which is highly essential for food security and production in regional to global scales. However, a prediction of crop yield is considered a challenging task due to the difficulty in extracting spatial context and local semantic features, and difficulty in handling spatiotemporal relations. In order to address these issues, a comprehensive feature extraction is developed along with an effective deep-learning classifier. In this paper, the Residual Attention and Local Context Aware Classifier (RALCAC) is developed for obtaining appropriate features from the remote sensing crop yield images. The developed RALCAC helps to obtain the spatial context using Residual Attention (RA) module and local semantic information that are beneficial in understanding the detailed depiction of the crop. Further, the Convolutional Long Short Term Memory (ConvLSTM) is used to obtain the prediction of crop yield using the comprehensive features from the RALCAC. The RALCAC is analysed by means of Root Mean Squared Error (RMSE) and coefficient of determination. The existing research such as DeepYield, SSTNN and 3DCNN are used to compare the RALCAC method. The RMSE of RALCAC for the MODIS dataset is 3.257, and it is lesser when compared to the DeepYield.

@article{Nagaraju2024,
  title     = {Effective Feature Extraction Using Residual Attention and Local Context Aware Classifier for Crop Yield Prediction},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {7},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Vinaykumar Vajjanakurike Nagaraju and Ananda Babu Jayachandra and Balaji Prabhu Baluvaneralu Veeranna and Ravi Prakash Madenur Lingaraju},
  doi       = {10.14569/IJACSA.2024.0150774},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150774}
}