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This research examines on the effectiveness of various Machine Learning and Deep Learning models for predicting the yield of paddy crop in East Godavari District, Andhra Pradesh, India. Dataset consist of 91 entries representing 91-day paddy crop growth cycle from 2000 to 2009, this study forecast yield for the years 2017 and 2018 using Machine Learning models such as Decision Tree, Random Forest, Gradient Boosting, AdaBoost, and Support Vector Machines (SVM) and Deep Learning models such as 1D Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, Multilayer Perceptron’s (MLP), Feedforward Neural Networks (FNN), and Gated Recurrent Units (GRU). Performance of the Model is evaluated by using R-square, Mean Square Error (MSE) and Mean Absolute Error (MAE).

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Harvesting Insights: Unrevealing Paddy Crop Yields Through Advanced Modeling Techniques

  • 会议论文

  • 发表日期：

    2025年

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  • Mounika Tummala,
  • Manimaran Aridoss,
  • Khadar Babu SK

摘要

This research examines on the effectiveness of various Machine Learning and Deep Learning models for predicting the yield of paddy crop in East Godavari District, Andhra Pradesh, India. Dataset consist of 91 entries representing 91-day paddy crop growth cycle from 2000 to 2009, this study forecast yield for the years 2017 and 2018 using Machine Learning models such as Decision Tree, Random Forest, Gradient Boosting, AdaBoost, and Support Vector Machines (SVM) and Deep Learning models such as 1D Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, Multilayer Perceptron’s (MLP), Feedforward Neural Networks (FNN), and Gated Recurrent Units (GRU). Performance of the Model is evaluated by using R-square, Mean Square Error (MSE) and Mean Absolute Error (MAE).