Estimation of wheat and paddy yield in the parts of Indian Punjab by integrating climate and Landsat satellite data using machine learning algorithms

No abstract is available for this paper.

• Publication year

  2025

• Venue

  Spatial Information Research

• Publication date

  2025-11-04

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1007/s41324-025-00647-8
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Machine Learning (AutoML)-Driven Wheat Yield Prediction for European Varieties: Enhanced Accuracy Using Multispectral UAV Data

  2025cited by this paper
• Soil Science-Informed Machine Learning

  2024cited by this paper
• Progress in Research on Deep Learning-Based Crop Yield Prediction

  2024cited by this paper
• Predicting China's Maize Yield Using Multi-Source Datasets and Machine Learning Algorithms

  2024influential reference
• Machine Learning-Based Crop Yield Prediction in South India: Performance Analysis of Various Models

  2024influential reference
• Crop Yield Prediction using Effective Deep Learning and Dimensionality Reduction Approaches for Indian Regional Crops

  2024cited by this paper
• Predicting the crop yield in agriculture using gradient boosting algorithm in comparison of naive bayes algorithm

  2024cited by this paper
• Optimizing Crop Yield Estimation through Geospatial Technology: A Comparative Analysis of a Semi-Physical Model, Crop Simulation, and Machine Learning Algorithms

  2024cited by this paper
• Rice Yield Estimation Using Multi-Temporal Remote Sensing Data and Machine Learning: A Case Study of Jiangsu, China

  2024influential reference
• Evaluation of MODIS, Landsat 8 and Sentinel-2 Data for Accurate Crop Yield Predictions: A Case Study Using STARFM NDVI in Bavaria, Germany

  2023cited by this paper
• Estimation of Winter Wheat Yield Using Multiple Temporal Vegetation Indices Derived from UAV-Based Multispectral and Hyperspectral Imagery

  2023cited by this paper
• Soil and satellite remote sensing variables importance using machine learning to predict cotton yield

  2023cited by this paper
• Prediction of crop yield using satellite vegetation indices combined with Machine Learning Approaches

  2023cited by this paper
• A data-driven crop model for maize yield prediction

  2023cited by this paper
• Out-of-year corn yield prediction at field-scale using Sentinel-2 satellite imagery and machine learning methods

  2023cited by this paper
• Rice (Oryza sativa L.) Growth Modeling Based on Growth Degree Day (GDD) and Artificial Intelligence Algorithms

  2022influential reference
• Exploring the superiority of solar-induced chlorophyll fluorescence data in predicting wheat yield using machine learning and deep learning methods

  2022cited by this paper
• Explainable Machine Learning Approach Quantified the Long-Term (1981–2015) Impact of Climate and Soil Properties on Yields of Major Agricultural Crops Across CONUS

  2022cited by this paper
• High-Resolution Mapping of Paddy Rice Extent and Growth Stages across Peninsular Malaysia Using a Fusion of Sentinel-1 and 2 Time Series Data in Google Earth Engine

  2022cited by this paper
• Deep Learning Based Wheat Crop Yield Prediction Model in Punjab Region of North India

  2021cited by this paper
• Crop yield forecasting and associated optimum lead time analysis based on multi-source environmental data across China

  2021cited by this paper
• A deep learning framework under attention mechanism for wheat yield estimation using remotely sensed indices in the Guanzhong Plain, PR China

  2021cited by this paper
• Assimilation of Remote Sensing Data into Crop Growth Model for Yield Estimation: A Case Study from India

  2021cited by this paper
• Direct and indirect impacts of climate change on wheat yield in the Indo-Gangetic plain in India

  2020cited by this paper
• Crop yield prediction using machine learning: A systematic literature review

  2020cited by this paper
• Field-level crop yield mapping with Landsat using a hierarchical data assimilation approach

  2019cited by this paper
• Gradient and Newton Boosting for Classification and Regression

  2018cited by this paper
• Remote sensing based change analysis of rice environments in Odisha, India.

  2015cited by this paper
• Climate variation explains a third of global crop yield variability

  2015cited by this paper
• Forecasting wheat yield in Punjab state of India by combining crop simulation model WOFOST and remotely sensed inputs

  2013cited by this paper
• Remote sensing of rice crop areas

  2013cited by this paper
• On the use of cross-validation for time series predictor evaluation

  2012cited by this paper
• Support vector machines for classification and regression.

  2010cited by this paper
• Crop yield response to economic, site and climatic variables

  2010cited by this paper
• Random Forests

  2001cited by this paper

• No citing papers are available for this paper.