Precision Crop Farming Framework for Small-Scale Rainfed Agriculture Using UAV RGB High-Resolution Imagery
DOI:
https://doi.org/10.7160/aol.2025.170101How to Cite
Abstract
This paper presents a precision crop farming framework developed for small-scale rainfed agriculture using unmanned aerial vehicle (UAV) red, green, and blue (RGB) high-resolution imagery. The aim is to enhance farm management by providing precise spatial and temporal information in heterogeneous farming systems in Botswana's semi-arid regions. The precision crop farming framework integrates UAVs and Global Navigation Satellite System (GNSS) data, introducing new vegetation indices and employing machine learning algorithms for high-accuracy crop and land use analysis. The framework comprises four components: data collection, applications, data processing, and users. Methods included UAV data acquisition, global navigation satellite system geo-referencing, and machine learning classification. Results demonstrated high spatial resolution and classification accuracy, providing actionable insights into crop conditions, planting patterns, and farm variability. The precision crop farming framework is a tool for improving agricultural productivity and sustainability, providing a foundation for efficient, data-driven farm management practices.References
Agrillo, E., Filipponi, F., Salvati, R., Pezzarossa, A. and Casella, L. (2023) "Modeling approach for coastal dune habitat detection on coastal ecosystems combining very high‐resolution UAV imagery and field survey", Remote Sensing in Ecology and Conservation, Vol. 9, No. 1, pp. 251-267. E-ISSN 2056-3485, ISSN 2056-3485. DOI: https://doi.org/10.1002/rse2.308
Akstinas, V., Krisciunas, A., Šidlauskas, A., Čalnerytė, D., Meilutyte-Lukauskiene, D., Jakimavičius, D., Fyleris, T., Nazarenko, S. and Barauskas, R. (2022) "Determination of river hydromorphological features in low-land rivers from aerial imagery and direct measurements using machine learning algorithms", Water, Vol. 14, No. 24, p. 4114. ISSN 2073-4441. DOI: https://doi.org/10.3390/w14244114
Apata, T., Ogunleye, K., Agboola, O. and Ojo, T. (2021) "Heterogeneity of Agricultural Land Use Systems and Poverty in Sub-Saharan Africa: Relationship and Evidence from Rural Nigeria", AGRIS on-line Papers in Economics and Informatics, Vol. 13, No. 2, pp. 3-22. ISSN 1804-1930. DOI: https://doi.org/10.7160/aol.2021.130201
Azizi, A., Zhang, Z., Rui, Z., Li, Y., Igathinathane, C., Flores, J. P., Mathew, J., Pourreza, A., Han, X. and Zhang, M. (2024) "Comprehensive wheat lodging detection after initial lodging using UAV RGB images", Expert Systems with Applications, Vol. 238, No. 2, p. 121788. ISSN 0957-4174. DOI: https://doi.org/10.1016/j.eswa.2023.121788
Bai, L., Huang, X., Dashzebeg, G., Ariunaa, M., Yin, S., Bao, Y., Bao, G., Tong, S., Dorjsuren, A. and Enkhnasan, D. (2024) "Potential of unmanned aerial vehicle red-green-blue images for detecting needle pests: A case study with Erannis jacobsoni Djak (Lepidoptera, Geometridae)", Insects, Vol. 15, No. 3, p. 172. E-ISSN 2075-4450. DOI: https://doi.org/10.3390/insects15030172
Bao, W., Huang, C., Hu, G., Su, B. and Yang, X. (2024) "Detection of Fusarium head blight in wheat using UAV remote sensing based on parallel channel space attention", Computers and Electronics in Agriculture, Vol. 217, No. 16, p. 108630. ISSN 0168-1699. DOI: https://doi.org/10.1016/j.compag.2024.108630
Blizkovsky, P. and Emelin, R. (2020) "The impact of official development assistance on the productivity of agricultural production in Ghana, Cameroon and Mali", Agris on-line Papers in Economics and Informatics, Vol. 12, No. 2, pp. 29-39. ISSN 1804-1930. DOI: https://doi.org/10.7160/aol.2020.120203
Bolo, B., Mpoeleng, D. and Zlotnikova, I. (2019) "Development of methods acquiring real time very high resolution agricultural spatial information using unmanned aerial vehicle", Agris on-line Papers in Economics and Informatics, Vol. 11, No. 2, pp. 21-29. ISSN 1804-1930. DOI: https://doi.org/10.7160/aol.2019.110203
Bolo, B., Zlotnikova, I. and Mpoeleng, D. (2024) "A spatial data image processing model using unmanned aerial vehicles and RGB imagery for crop farming on small-scale subsistence farms", International Journal of Sustainable Agricultural Management and Informatics (Online First). E-ISSN 2054-5827. [Online]. Available: https://www.inderscience.com/info/ingeneral/forthcoming.php?jcode=ijsami [Accessed: Jan 13, 2025].
Bouguettaya, A., Zarzour, H., Kechida, A. and Taberkit, A. (2022) "A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images", Cluster Computing, Vol. 26, No. 5, pp. 1297-1317. E-ISSN 1573-7543, ISSN 1386-7857. DOI: https://doi.org/10.1007/s10586-022-03627-x
Campbell, B. M., Thornton, P., Zougmore, R., Van Asten, P. J. A. and Lipper, L. (2014) "Sustainable intensification: What is its role in climate smart agriculture?", Current Opinion in Environmental Sustainability, Vol. 8, pp. 39-43. E-ISSN 1877-3443. DOI: https://doi.org/10.1016/j.cosust.2014.07.002
Castellano, G., De Marinis, P. and Vessio, G. (2023) "Weed mapping in multispectral drone imagery using lightweight vision transformers", Neurocomputing, Vol. 562, p. 126914. E-ISSN 1872-8286. DOI: https://doi.org/10.1016/j.neucom.2023.126914
Cresswell, J. W. and Cresswell, J. D. (2022) "Research Design: Qualitative, Quantitative, and Mixed Methods Approaches", 6th ed., Newcastle upon Tyne, the UK: Sage. ISBN 978-1-4129-6556-9.
Cucho-Padin, G., Loayza, H., Palacios, S., Balcazar, M., Carbajal, M. and Quiroz, R. (2020) "Development of low-cost remote sensing tools and methods for supporting smallholder agriculture", Applied Geomatics, Vol. 12, pp. 247–263. E-ISSN 1866-928X, ISSN 1866-9298. DOI: https://doi.org/10.1007/s12518-019-00292-5
Digital Agriculture Laborator (2023) "RGB Camera", University of California, Davis. [Online]. Available: https://digitalag.ucdavis.edu [Accessed: March 20, 2025].
Du, M., Li, H. and Roshanianfard, A. (2022) "Design and experimental study on an innovative UAV-LiDAR topographic mapping system for precision land levelling", Drones, Vol. 6, No. 12, p. 403. ISSN 2504-446X. DOI: https://doi.org/10.3390/drones6120403
FAO (2023) "Africa – Regional Overview of Food Security and Nutrition 2023: Statistics and trends", Accra, Ghana: FAO. [Online]. Available: https://openknowledge.fao.org/items/0db03746-74e1-4b78-9508-70b9f661859c [Accessed: Feb. 15, 2025].
Feng, G., Wang, C., Wang, A., Gao, Y., Zhou, Y., Huang, S. and Luo, B. "Segmentation of wheat lodging areas from UAV imagery using an ultra-lightweight network", Agriculture, Vol. 14, No. 2, p.244. ISSN 2077-0472. DOI: https://doi.org/10.3390/agriculture14020244
Ferro, M. V., Sørensen, C. G., and Catania, P. (2024) "Comparison of different computer vision methods for vineyard canopy detection using UAV multispectral images", Computers and Electronics in Agriculture, Vol. 225, p. 109277. ISSN 0168-1699. DOI: https://doi.org/10.1016/j.compag.2024.109277
Finger, R., Swinton, S., El Benni, N. and Walter, A. (2019) "Precision farming at the nexus of agricultural production and the environment", Annual Review of Resource Economics, Vol. 11, No. 1, pp. 313-335. E-ISSN 1941-1359, ISSN 1941-1340. DOI: https://doi.org/10.1146/annurev-resource-100518-093929
Furaste Danilevicz, M., Lujan, R., Batley, J., Bayer, P., Bennamoun, M., Edwards, D. and Ashworth, M. (2023) "Segmentation of sandplain lupin weeds from morphologically similar narrow-leafed lupins in the field", Remote Sensing, Vol. 15, No. 7, p. 1817. E-2072-4292. DOI: https://doi.org/10.3390/rs15071817
Gao, H., Yu, Y., Huang, X., Song, L., Li, L., Li, L. and Zhang, L. (2023a) "Enhancing the localization accuracy of UAV images under GNSS denial conditions", Sensors, Vol. 23, No. 24, p. 9751. E-ISSN 1424-8220. DOI: https://doi.org/10.3390/s23249751
Gao, Y., Zhao, T., Zheng, Z. and Liu, D. (2023b) "A cotton leaf water potential prediction model based on particle swarm optimisation of the LS-SVM model", Agronomy, Vol 13, No. 12, p. 2929. ISSN 2073-4395. DOI: https://doi.org/10.3390/agronomy13122929
García-López, S., Vélez-Nicolás, M., Martínez-López, J., Sánchez Bellón, Á., Pacheco-Orellana, M. J., Ruiz-Ortiz, V., Muñoz-Pérez, J. and Barbero, L. (2022) "Using UAV photogrammetry and automated sensors to assess aquifer recharge from a coastal wetland", Remote Sensing, Vol. 14, No. 24, p. 6185. E-2072-4292. DOI: https://doi.org/10.3390/rs14246185
Gerardo, R. and de Lima, I. P. (2023) "Applying RGB-based vegetation indices obtained from UAS imagery for monitoring the rice crop at the field scale: A case study in Portugal", Agriculture, Vol. 13, No. 10, p. 1916. ISSN 2077-0472. DOI: https://doi.org/10.3390/agriculture13101916
Gkillas, A., Kosmopoulos, D. and Berberidis, K. (2022) "Cost-efficient coupled learning methods for recovering near-infrared information from RGB signals: application in precision agriculture", Computers and Electronics in Agriculture, Vol. 209, p.107833. ISSN 0168-1699. DOI: https://doi.org/10.1016/j.compag.2023.107833
Guo, Y., Zhang, X., Chen, S., Wang, H., Senthilnath, J., Cammarano, D. and Fu, Y. (2022) "Integrated UAV-based multi-source data for predicting maize grain yield using machine learning approaches", Remote Sensing, Vol. 14, No. 24, p. 6290. E-ISSN-2072-4292. DOI: https://doi.org/10.3390/rs14246290
Hassani, K., Gholizadeh, H., Taghvaeian, S., Natalie, V., Carpenter, J. and Jacob, J. (2023) "Application of UAS-based remote sensing in estimating winter wheat phenotypic traits and yield during the growing season", Journal of Photogrammetry Remote Sensing and Geoinformation Science, Vol. 91, No. 1, pp. 77-90. E-ISSN 2512-2819, ISSN 2512-2789. DOI: https://doi.org/10.1007/s41064-022-00229-5
Haumont, J., Cool, S., Lootens, P., Beek, J., Raymaekers, D., Ampe, E., Cuypere, T., Bes, O., Bodyn, J. and Saeys, W. (2022) "Multispectral UAV-based monitoring of leek dry-biomass and nitrogen uptake across multiple sites and growing seasons", Remote Sensing, Vol 14, No. 24, p. 6211. E-ISSN 2072-4292. DOI: https://doi.org/10.3390/rs14246211
Heard, J., Scoular, C., Duckworth, D., Ramalingam, D. and Teo, I. (2020) "Critical Thinking: Definition and Structure", Melbourne: Australian Council for Educational Research. ISBN 9781742865874.
Hütt, C., Bolten, A., Hüging, H. and Bareth, G. (2022) "UAV LiDAR metrics for monitoring crop height, biomass and nitrogen uptake: A case study on a winter wheat field trial", Journal of Photogrammetry Remote Sensing and Geoinformation Science, Vol. 91, No. 2, pp. 65-76. E-ISSN 2512-2819, ISSN 2512-2789. DOI: https://doi.org/10.1007/s41064-022-00228-6
IUSS Working Group (2015) "World Reference Base for Soil Resources 2014", update 2015: International soil classification system for naming soils and creating legends for soil maps. World Soil Resources Reports No. 106, Rome: FAO. [Online]. Available: https://openknowledge.fao.org/server/api/core/bitstreams/bcdecec7-f45f-4dc5-beb1-97022d29fab4/content [Accessed: Feb. 5, 2025].
Killeen, P., Kiringa, I., Yeap, T. and Branco, P. (2024) "Corn grain yield prediction using UAV-based high spatiotemporal resolution imagery, machine learning, and spatial cross-validation", Remote Sensing, Vol. 16, No. 4, p. 683. E-ISSN 2072-4292. DOI: https://doi.org/10.3390/rs16040683
Kodl, G., Streeter, R., Cutler, N. and Bolch, T. (2024) "Arctic tundra shrubification can obscure increasing levels of soil erosion in NDVI assessments of land cover derived from satellite imagery", Remote Sensing of Environment, Vol. 301, No. 1, p. 113935. ISSN 0034-4257. DOI: https://doi.org/10.1016/j.rse.2023.113935
Kumar, S., Kumar, A. and Lee, D.-G. (2022) "Semantic segmentation of UAV images based on transformer framework with context information", Mathematics, Vol. 10, No. 24, p. 4735. ISSN 2227-7390. DOI: https://doi.org/10.3390/math10244735
Lee, C.-J., Yang, M.-D., Tseng, H.-H., Hsu, Y.-C., Sung, Y. and Chen, W.-L. (2023) "Single-plant broccoli growth monitoring using deep learning with UAV imagery", Computers and Electronics in Agriculture, Vol. 207, No. 6, p. 107739. ISSN 0168-1699. DOI: https://doi.org/10.1016/j.compag.2023.107739
Li, J., Li, Q., Yu, C., He, Y., Qi, L., Shi, W. and Zhang, W. (2022) "A model for identifying soybean growth periods based on multi-source sensors and improved convolutional neural network", Agronomy, Vol. 12, No. 12, p. 2991. ISSN 2073-4395. DOI: https://doi.org/10.3390/agronomy12122991
Lin, H., Chen, Z., Qiang, Z., Su-Kit, T., Liu, L. and Pau, G. (2023) "Automated counting of tobacco plants using multispectral UAV data", Agronomy, Vol. 13, No. 12, p. 2861. ISSN 2073-4395. DOI: https://doi.org/10.3390/agronomy13122861
Liu, Y., Su, J., Yheng, Y., Liu, D., Song, Z., Fang, Z, Zang, P. and Su, B. (2024) "GLDCNet: a novel convolutional neural network for grapevine leafroll disease recognition using UAV-based imagery", Computers and Electronics in Agriculture, Vol. 218, p.108668. ISSN 0168-1699. DOI: https://doi.org/10.1016/j.compag.2024.108668
Ma, H., Li, X., Ji, J., Cui, H., Shi, Y., Li, N. and Yang, C. (2023a) "Monitoring indicators for comprehensive growth of summer maize based on UAV remote sensing", Agronomy, Vol. 13, No. 12, p. 2888. ISSN 2073-4395. DOI: https://doi.org/10.3390/agronomy13122888
Ma, J., Liu, B., Ji, L., Zhu, Z., Wu, Y. and Jiao, W. (2023b) "Field-scale yield prediction of winter wheat under different irrigation regimes based on dynamic fusion of multimodal UAV imagery", International Journal of Applied Earth Observation and Geoinformation, Vol. 118, p. 103292. ISSN 1569-8432. DOI: https://doi.org/10.1016/j.jag.2023.103292
Ma, L., Huang, X., Hai, Q., Gang, B., Tong, S., Bao, Y., Dashzebeg, G., Nanzad, T., Dorjsuren, A., Enkhnasan, D., and Ariunaa, M. (2022a) "Model-based identification of Larix sibirica Ledeb. damage caused by Erannis jacobsoni Djak. based on UAV multispectral features and machine learning", Forests, Vol. 13, No. 12, p. 2104. E-ISSN 1999-4907. DOI: https://doi.org/10.3390/f13122104
Ma, X., Chen, P. and Jin, X. (2022b) "Predicting wheat leaf nitrogen content by combining deep multitask learning and a mechanistic model using UAV hyperspectral images", Remote Sensing, Vol. 14, No. 24, p. 6334. E-ISSN 2072-4292. DOI: https://doi.org/10.3390/rs14246334
McCarthy, C., Nyoni, Y., Kachamba, D., Banda, L., Moyo, B., Chisambi, C., Banfill, J. and Hoshino, B. (2023) "Can drones help smallholder farmers improve agriculture efficiencies and reduce food insecurity in Sub-Saharan Africa? Local perceptions from Malawi", Agriculture, Vol. 13, No. 5, p. 1075. ISSN 2077-0472. DOI: https://doi.org/10.3390/agriculture13051075
Mndela, Y., Ndou, N. and Nyamugama, A. (2023) "Irrigation scheduling for small-scale crops based on crop water content patterns derived from UAV multispectral imagery", Sustainability, Vol. 15, No. 15, p.12034. ISSN 2071-1050. DOI: https://doi.org/10.3390/su151512034
Mohidem, N. A., Che'Ya, N., Juraimi, A., Fazlil Ilahi, W. F., Mohd Roslim, M. H., Sulaiman, N., Saberioon, M. and Mohd Noor, N. (2021) "How can unmanned aerial vehicles be used for detecting weeds in agricultural fields?", Agriculture, Vol. 11, No. 10, p. 1004. ISSN 2077-0472. DOI: https://doi.org/10.3390/agriculture11101004
Muksimova, S., Mardieva, S. and Cho, Y.-I. (2022) "Deep encoder–decoder network-based wildfire segmentation using drone images in real-time", Remote Sensing, Vol. 14, No. 24, p. 6302. E-ISSN 2072-4292. DOI: https://doi.org/10.3390/rs14246302
Nduku, L., Munghemezulu, C., Mashaba-Munghemezulu, Z., Kalumba, A. M., Chirima, G., Masiza, W. and Villiers, C. (2023) "Global research trends for unmanned aerial vehicle remote sensing application in wheat crop monitoring", Geomatics, Vol. 3, No. 1, pp. 115-136. ISSN 2673-7418. DOI: https://doi.org/10.3390/geomatics3010006
Paul, R. and Elder, L. (2006) "Critical Thinking: Learn the Tools the Best Thinkers Use", Concise ed. Old Bridge, NJ: Pearson Prentice Hall. ISBN 9780131703476.
Peng, J., Nieto, H., Andersen, M., Sørensen, K., Larsen, R., Morel, J., Parsons, D., Zhou, Z. and Manevski, K. (2023) "Accurate estimates of land surface energy fluxes and irrigation requirements from UAV-based thermal and multispectral sensors", Journal of Photogrammetry and Remote Sensing, Vol. 198, No. 1, pp. 238-254. ISSN 0924-2716. DOI: https://doi.org/10.1016/j.isprsjprs.2023.03.009
Qu, H., Zheng, C., Ji, H., Barai, K. and Zhang, Y.-J. (2024) "A fast and efficient approach to estimate wild blueberry yield using machine learning with drone photography: flight altitude, sampling method, and model effects", Computers and Electronics in Agriculture, Vol. 216, p.108543. ISSN 0168-1699. DOI: https://doi.org/10.1016/j.compag.2023.108543
Ramachandran, R., Bajón Fernández, Y., Truckell, I., Constantino, C., Casselden, R., Leinster, P. and Rivas-Casado, M. (2023) "Accuracy assessment of surveying strategies for the characterization of microtopographic features that influence surface water flooding", Remote Sensing, Vol. 15, No. 7, p. 1912. E-ISSN 2072-4292. DOI: https://doi.org/10.3390/rs15071912
Rouse, J. W., Haas, R. H., Schell, J. A. and Deering, D. W. (1974). "Monitoring vegetation systems in the Great Plains with ERTS", Proceedings of the Third Earth Resources Technology Satellite-1 Symposium, Vol. 1, pp. 309-317. NASA SP-351.
Sandino, J., Breen, B., Doshi, A., Randall, K., Barthelemy, J., Robinson, S. and Gonzalez, L. (2023) "A green fingerprint of Antarctica: drones, hyperspectral imaging, and machine learning for moss and lichen classification", Remote Sensing, Vol. 15, No. 24, p. 5658. E-ISSN 2072-4292. DOI: https://doi.org/10.3390/rs15245658
Sileshi, G., Kihara, J., Tamene, L., Vanlauwe, B., Phiri, E., Jama, B. and de Moura, E. G. (2022) "Unravelling causes of poor crop response to applied N and P fertilizers on African soils", Experimental Agriculture, Vol. 58, No. e7, pp. 1-17. E-ISSN 1469-4441, ISSN 0014-4797. DOI: https://doi.org/10.1017/S0014479722000412
Soares da Silva, A., Vieira, B., Bezerra, T., Martins, G. and Siquieroli, A. (2022) "Early detection of coffee leaf rust caused by Hemileia vastatrix using multispectral images", Agronomy, Vol. 12, No. 12, p. 2911. ISSN 2073-4395. DOI: https://doi.org/10.3390/agronomy12122911
Sulemane, S., Carvalho, J., Pedro, D. F., Moutinho, F. and Correia, S. (2022) "Vineyard gap detection by convolutional neural networks fed by multi-spectral images", Algorithms, Vol. 15, No. 12, p. 440. ISSN 1999-4893. DOI: https://doi.org/10.3390/a15120440
Tian, F., Ransom, C., Zhou, J., Wilson, B. and Sudduth, K. (2024) "Assessing the impact of soil and field conditions on cotton crop emergence using UAV-based imagery", Computers and Electronics in Agriculture, Vol. 218, p.108738. ISSN 0168-1699. DOI: https://doi.org/10.1016/j.compag.2024.108738
Tscharntke, T., Clough, Y., Wanger, T., Jackson, L., Kormann née Motzke, I., Perfecto, I., Vandermeer, J. and Whitbread, A. (2012) "Global food security, biodiversity conservation and the future of agricultural intensification", Biological Conservation, Vol. 151, No. 1, p. 53-59. ISSN 0006-3207. DOI: https://doi.org/10.1016/j.biocon.2012.01.068
Wang, W., Tang, J., Zhang, N., Xu, X., Zhang, A. and Wang, Y. (2022) "Automated detection method to extract Pedicularis based on UAV images", Drones, Vol. 6, No. 12, p. 399. ISSN 2504-446X. DOI: https://doi.org/10.3390/drones6120399
Wieme, J., Leroux, S., Cool, S., Beek, J., Pieters, J. and Maes, W. (2024) "Ultra-high-resolution UAV imaging and supervised deep learning for accurate detection of Alternaria solani in potato fields", Frontiers in Plant Science, Vol. 15, p.1206998. E-ISSN 1664-462X. DOI: https://doi.org/10.3389/fpls.2024.1206998
Wolfert, S., Ge, L., Verdouw, C. and Bogaardt, M.-J. (2017) "Big data in smart farming – A review" Agricultural Systems, Vol. 153, pp. 69-80. ISSN 0308-521X. DOI: https://doi.org/10.1016/j.agsy.2017.01.023
Xie, Y., Wu, W., Shi, J., Yu, T., Sun, X. and Li, X. (2024) "Multispectral UAV-based monitoring of Cassytha filiformis invasion in Xisha Islands", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 17, pp. 829-841. ISSN 1939-1404. DOI: https://doi.org/10.1109/JSTARS.2023.3330768
Xin, Y., Wang, R., Wang, X., Wang, X., Xiao, Z. and Lin, J. (2022) "High-resolution terrain reconstruction of slot canyon using backpack mobile laser scanning and UAV photogrammetry", Drones, Vol. 6, No. 12, p. 429. ISSN 2504-446X. DOI: https://doi.org/10.3390/drones6120429
Yağ, I. and Altan, A. (2022) "Artificial intelligence-based robust hybrid algorithm design and implementation for real-time detection of plant diseases in agricultural environments", Biology, Vol. 11, No. 2, p. 1732. ISSN 2079-7737. DOI: https://doi.org/10.3390/biology11121732
Yin, H., Huang, W., Li, F., Yang, H., Li, Y., Hu, Y. and Yu, K. (2022) "Multi-temporal UAV imaging-based mapping of chlorophyll content in potato crop", Journal of Photogrammetry Remote Sensing and Geoinformation Science, Vol. 91, No. 9, pp. 91-106. E-ISSN 2512-2819. ISSN 2512-2789. DOI: https://doi.org/10.1007/s41064-022-00218-8
Yin, R. K. (2018) "Case Study Research and Applications: Design and Methods", 6th ed. Thousand Oaks, CA: Sage. ISBN 9781506336169.
Zhang, L., Song, X., Zhang, H., Wang, A., Zhu, Y., Zhu, X., Chen, L. and Zhu, Q. (2024) "Estimating winter wheat plant nitrogen content by combining spectral and texture features based on a low-cost UAV RGB system throughout the growing season", Agriculture, Vol. 14, No. 3, p.456. ISSN 2077-0472. DOI: https://doi.org/10.3390/agriculture14030456
Zheng, H., Ji, W., Wang, W., Lu, J., Li, D., Guo, C., Yao, X., Tian, Y., Cao, W., Zhu, Y. and Cheng, T. (2022) "Transferability of models for predicting rice grain yield from unmanned aerial vehicle (UAV) multispectral imagery across years, cultivars and sensors", Drones, Vol. 6, No. 12, p. 423. ISSN 2504-446X. DOI: https://doi.org/10.3390/drones6120423
