Effectiveness of Machine and Deep Learning Algorithms in Remote Sensing for Food Crop Mapping: A Systematic Literature Review
DOI:
https://doi.org/10.23917/forgeo.12235Keywords:
Effectiveness, Machine Learning, Deep Learning, Food Crop Mapping, Remote SensingAbstract
The problems of food (in)security, considering rapid population growth, climate change, land degra-dation, and escalating competition for natural resources, are highlighted in the global conversation on sustainable development. Ecologically sound agricultural management and well-informed policy-making depend on accurate and trustworthy maps of food crops. For mapping food crops, remote sensing has become essential, and machine and deep learning algorithms are becoming more and more important. A systematic literature review explores the extent to which these algorithms have been applied in food crop mapping. A comprehensive search across five electronic databases yielded 406 relevant studies, of which 50 articles were chosen after applying inclusion and exclusion criteria. According to the analysis, the best algorithms for mapping food crops are U-Net, value-guided expla-nation model (SGEM), and one-dimensional convolutional neural network (Conv1D). These find-ings provide an organized framework for future research on food crop management and monitoring. Food security and sustainable farming methods depend on accurate and reliable food crop maps, which can be improved by applying state of the art deep learning methods, the study found. By using these algorithms, stakeholders and policymakers can develop data-driven strategies to optimize land use, minimize environmental risks, and enhance global food sustainability.
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