Riki Ridwana, Muhammad Kamal, Sanjiwana Arjasakusuma
The global conversation on sustainable development highlights the problems of food (in)security, considering rapid population growth, climate change, land degradation, and escalating competition for natural resources. Ecologically sound agricultural management and well-informed policymaking depend on accurate and trustworthy food crop maps. Remote sensing has become essential for mapping food crops, and machine learning (ML) and deep learning algorithms are becoming increasingly important. This systematic literature review examines the effectiveness of ML algorithms and deep learning architectures in remote sensing-based food crop mapping. This review follows the PRISMA guidelines and covers peer-reviewed studies published between 2017 and March 2026. A comprehensive search across five electronic databases (Google Scholar, SpringerLink, ScienceDirect, Scopus, and Wiley) identified 1,589 records, of which 80 met the inclusion criteria after screening, eligibility assessment, and quality appraisal. The extracted variables included algorithm type, crop species, sensor modality, spatial platform (satellite or Unmanned Aerial Vehicle), accuracy metrics, and validation strategy. According to the analysis, U-Net, the value-guided explanation model, and the one-dimensional convolutional neural network have the highest average overall accuracy for mapping food crops. However, crop performance varied depending on crop type, sensor characteristics, and agroecological conditions. These findings provide an organized framework for future research on food crop management and monitoring. The study found that 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. Stakeholders and policymakers can develop data-driven strategies to optimize land use, minimize environmental risks, and enhance global food sustainability using these algorithms. © 2026 by the authors.
Doctoral Program in Geographical Science, Faculty of Geography, Universitas Gadjah Mada, Sekip Utara, Bulaksumur, Yogyakarta, 55281, Indonesia; Department of Geographic Information Science, Faculty of Geography, Universitas Gadjah Mada, Sekip Utara, Bulaksumur, Yogyakarta, 55281, Indonesia; Mapping Survey and Geographic Information Study Program, Faculty of Social Sciences Education, Universitas Pendidikan Indonesia, Jalan Doktor Setiabudi Isola, Bandung, 40154, Indonesia
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