The field of remote sensing and machine learning is rapidly advancing, with a focus on improving accuracy and efficiency in agricultural applications. Recent research has explored the use of transfer learning, meta-learning, and ensemble methods to enhance crop classification, disease detection, and yield estimation. These approaches have shown promising results, with some studies achieving high accuracy rates and improved computational efficiency. The integration of multi-scale geospatial information and the use of auxiliary labels are also being investigated to improve model performance. Noteworthy papers in this area include: A Decade of Wheat Mapping for Lebanon, which introduces an improved pipeline for winter wheat segmentation and presents a case study on a decade-long analysis of wheat mapping in Lebanon. Enhancing Cocoa Pod Disease Classification via Transfer Learning and Ensemble Methods, which presents an ensemble-based approach for cocoa pod disease classification and achieves a test accuracy of 100% using Bagging. Transfer Learning via Auxiliary Labels with Application to Cold-Hardiness Prediction, which introduces a new transfer-learning framework that allows farmers to leverage phenological data to produce more accurate cold-hardiness predictions.
Advances in Remote Sensing and Machine Learning for Agricultural Applications
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Banana Ripeness Level Classification using a Simple CNN Model Trained with Real and Synthetic Datasets
Application of machine learning models to predict the relationship between air pollution, ecosystem degradation, and health disparities and lung cancer in Vietnam
A comprehensive review of remote sensing in wetland classification and mapping
Meta-learning For Few-Shot Time Series Crop Type Classification: A Benchmark On The EuroCropsML Dataset
A Decade of Wheat Mapping for Lebanon
Mamba-Based Ensemble learning for White Blood Cell Classification
Geographical Context Matters: Bridging Fine and Coarse Spatial Information to Enhance Continental Land Cover Mapping
Can Moran Eigenvectors Improve Machine Learning of Spatial Data? Insights from Synthetic Data Validation
Enhancing Cocoa Pod Disease Classification via Transfer Learning and Ensemble Methods: Toward Robust Predictive Modeling
Transfer Learning via Auxiliary Labels with Application to Cold-Hardiness Prediction