Open cotton boll detection using LiDAR point clouds and RGB images from unmanned aerial systems: Scalability and Operational Maintainability

Scalability includes not only throughput but also maintenance burden, observability, update procedures, and the ability to recover from operational failure. This structured evidence review evaluates "Open cotton boll detection using LiDAR point clouds and RGB images from unmanned aerial systems" alongside nine author-disjoint, topically matched publications in multimodal precision agriculture. It compares construct definitions, evaluation choices, operating assumptions, and reported limitations instead of treating bibliographic similarity as empirical equivalence. Viewed through scalability and operational maintainability, the map separates claims supported by the available record from questions that still require full-text extraction, replication, or new experiments. The synthesis is interpretive rather than meta-analytic and therefore does not present a pooled effect estimate or a new causal result. The resulting agenda measures load behavior, observability, update risk, staffing burden, and recovery under production-scale failures.

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2. Qianxia, L., Zhongfa, Z., Lai, W., Guangyuan, A., & Yuzhu, Q. (2025). Research on the Scale Effects of Uav Flight Height And Crop Single Plant Mapping for Precision Agriculture. . https://doi.org/10.2139/ssrn.5334293 DOI
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5. Futerman, S.-I., Laor, Y., Eshel, G., Aharon, S., & Cohen, Y. (2026). Expanding the services of cereal/legume cover crop mixtures: From UAV-RGB species-dominance identification to precision-based pre-plant nitrogen decisions. Precision Agriculture, 27(3). https://doi.org/10.1007/s11119-026-10359-0 DOI
6. Bolo, B., Zlotnikova, I., & Mpoeleng, D. (2025). Precision Crop Farming Framework for Small-Scale Rainfed Agriculture Using UAV RGB High-Resolution Imagery. Agris on-line Papers in Economics and Informatics, 17(1), 3-19. https://doi.org/10.7160/aol.2025.170101 DOI
7. Mateen, A. (2019). WEED DETECTION IN WHEAT CROP USING UAV for PRECISION AGRICULTURE. Pakistan Journal of Agricultural Sciences, 56(03), 775-784. https://doi.org/10.21162/pakjas/19.8036 DOI
8. Tang, W., Liu, M., Zhao, R., Liu, G., Sun, H., Guo, C., Liu, Y., An, L., & Li, M. (2025). UAV-borne RGB image and LiDAR fusion for reconstruction of 3D simulated hyperspectral data for crop growth parameter estimation. Computers and Electronics in Agriculture, 239, 111058. https://doi.org/10.1016/j.compag.2025.111058 DOI
9. Li, W. (2023). Unmanned Aerial Vehicle (UAV) in Precision Agriculture to Identify the Crop Water Shortage by Using Multi-Spectral Sensor. Open Access Journal of Agricultural Research, 8(2), 1-4. https://doi.org/10.23880/oajar-16000303 DOI
10. Saltos-Alcivar, W., Delgado-Marcillo, C., Zamora-Ledezma, E., Rivas, C.-A., & Pacheco Gil, H.-A. (2026). UAV-Borne RGB Imagery and Machine Learning for Estimating Soil Properties and Crop Physiological Traits in Peanut (Arachis hypogaea): A Low-Cost Precision Agriculture Approach. AgriEngineering, 8(5), 177. https://doi.org/10.3390/agriengineering8050177 DOI