August 4, 2025Remote SensingOpen Access

Assessing Model Trade-Offs in Agricultural Remote Sensing: A Review of Machine Learning and Deep Learning Approaches Using Almond Crop Mapping

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DL models achieve high accuracy rates of 97.3% to 97.5%, while ML models follow closely with 96.6% to 96.8%.
Traditional ML models demonstrate robust performance with lower computational demands compared to DL methods.
Comprehensive evaluation covers diverse agricultural contexts and model performance metrics from 2008 to 2022.
Findings emphasize the importance of balancing model accuracy and efficiency in agricultural applications.

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Comparative analysis of ML and DL models for land cover classification in agriculture, highlighting trade-offs and implications.

Rahaman et al. (2025) studied this question.

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