The use of drone technology in horticulture is a revolutionary development that will have a big impact on crop management, especially for plants classified as plantations, spices, medicinal and aromatic crops. Drones with multispectral sensors, thermal imaging capabilities and high-resolution cameras provide for precise data collection and monitoring, facilitating improved management techniques i.e., 3 D mapping, crop monitoring, crop surveillance, crop health assessment etc. Drones provide data of crop health assessment, temperature and horticultural plots' topography making and yield forecasts for plantation crops like tea and coffee, maximizing resource utilization and boosting output. Drones help in the early detection of pests and diseases in spice crops, which enables prompt interventions and lower crop losses. In addition to collecting data, drones can be utilized for targeted applications of fertilizer and pesticides. By reducing the amount of chemicals needed and sure they are only use, when necessary, this method reduces costs and its negative effects on the environment.
Drones, Horticulture, Coconut, Turmeric, Ginger, Lemon grass
Abesinghe, A. M. S. K., Amaratunga, K. S. P., Ekanayake, E. M. A. C., Kavindi, M. A. R., & Mohott, A. J. (2020). Developing a selective tea plucking mechanism using image processing for a drone-based tea harvesting machine. Global Journal of Science, 8(11), 526–533.
Atun, R., Ucar, E., & Gürsoy, O. (2020). Investigation of salt stress in rosemary (Rosmarinus officinalis L.) with the remote sensing technique. Türkiye Tarımsal Araştırmalar Dergisi, 7(10), 19–59.
Bahuguna, S., Anchal, S., Guleria, D., Devi, M., Meenakshi, Kumar, D., Kumar, R., Murthy, P. V. S., & Kumar, A. (2022). Unmanned Aerial Vehicle-Based Multispectral Remote Sensing for Commercially Important Aromatic Crops in India for Its Efficient Monitoring and Management. Journal of the Indian Society of Remote Sensing, 50, 397–407.
Bura, R. O., Apriyani, S. W., Ariwibawa, K., & Adharian, E. (2018). UAV application for oil palm harvest prediction. Journal of Physics: Conference Series, 1130, 011-20.
Todorov, K. (2021, August 10). Drone Technology to Spray Vineyards Gains Popularity. Wine Business.net. https://www.winebusiness.com/news/
Dunning, H. (2017, September 1). Drones that detect early plant disease could save crops. Imperial News. https://www.imperial.ac.uk/news/178983/drones-that-detect-early-plant-disease/
Enrique, A. O., Manuel, P. R., Jorge, M. G., & João, V. (2020). A Cloud-Based Environment for Generating Yield Estimation Maps From Apple Orchards Using UAV Imagery and a Deep Learning Technique. Frontiers in Plant Science, 11. https://doi.org/10.3389/fpls.2020.01086
FAO. (2018). E-agriculture in action – Drones for Agriculture.
http://www.fao.org/documents/card/en/c/I8494EN
Handique, B. K., Goswami, C., Gupta, C., Pandit, S., Gogo, S., Jadi, R., Jena, P., Borah, G., & Raju, P. L. N. (2020). Hierarchical classification for assessment of horticultural crops in mixed cropping pattern using UAV borne multi spectral sensor. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 53(3), 216–270.
Hoyos-Villegas, V., & Fritschi, F. B. (2013). Relationships among vegetation indices derived from aerial photographs and soybean growth and yield. Crop Science, 53(6), 2631–2642.
Kedari, S., Lohagaonkar, P., Nimbokar, M., Palve, G., & Yevale, P. (2016). Quadcopter-a smarter way of pesticide spraying. Imperial Journal of Interdisciplinary Research, 2(6), 055–087.
Koppe, W., Gnyp, M. L., & Hennig, S. D. (2012). Multi temporal hyperspectral and radar remote sensing for estimating winter wheat biomass in the North China Plain. Photogrammetrie Fernerkundung-Geoinformation, 2(3), 281–298.
Kumar, L., Schmidt, K. S., Dury, S., & Skidmore, A. K. (2001). Imaging spectrometry and vegetation science. In Imaging Spectrometry - Basic Principles and Prospective Application, Remote Sensing and Digital Image Processing (Vol. 4, pp. 111–155).
Liu, X., Ghazali, K. H., Han, F., Mohamed, I. I., Zhao, Y., & Ji, Y. (2020). Oil Palm Tree Detection and Counting in Aerial Images Based on Faster R-CNN. In A. N. K. Nasir et al. (Eds.), InECCE2019. Lecture Notes in Electrical Engineering (Vol. 632). Springer. https://doi.org/10.1007/978-981-15-2317-5_40
Mansur, M. A., Mukhtar, R. B., & Al-Doksi, J. (2014). The usefulness of unmanned airborne vehicle (UAV) imagery for automated palm oil tree counting. Research Journal, 1, 12–20.
Pathak, H., Kumar, A. K., Mohapatra, S. D., & Gaikwad, B. B. (2020). Use of Drones in Agriculture: Potentials, Problems and Policy Needs. ICAR-NIASM, 12(2), 12–23.
Singh, S., Pandey, P., Khan, M. S., & Semwal, M. (2021, December 3–4). Multi-temporal High Resolution Unmanned Aerial Vehicle (UAV) Multispectral Imaging for Menthol Mint Crop Monitoring [Paper presentation]. 6th International Conference for Convergence in Technology, Maharashtra, India.
Singhal, G., Bansod, B., Mathew, L., Goswami, J., Choudhury, B. U., & Raju, P. L. N. (2019). Estimation of Leaf Chlorophyll Concentration in Turmeric (Curcuma longa) Using High Resolution Unmanned Aerial Vehicle Imagery Based on Kernel Ridge Regression. Journal of the Indian Society of Remote Sensing, 47, 1111–1122.
Sylvester, G. (2018). E-Horticulture in action: drones for Horticulture. Food and Agriculture Organization of the United Nations and International Telecommunication Union.
Tellaeche, A., Burgos Artizzu, X. P., Pajares, G., Ribeiro, A., & Fernández-Quintanilla, C. (2008). A new vision based approach to differential spraying in precision Horticulture. Computers and Electronics in Agriculture, 60(2), 144–155.
Yang, C., Everitt, J. H., & Bradford, J. M. (2008). Yield estimation from hyperspectral imagery using spectral angle mapper (SAM). Transactions of the ASABE, 51(2), 729–737.
Yigezu, M. G., Woldeyohannis, M. M., & Tonja, A. L. (2021). Early Ginger Disease Detection Using Deep Learning Approach. In Advances of Science and Technology: 9th EAI International Conference, ICAST.
Zailani Khuzaimah, N. M. N., Adam, S. N., Kalantar, B., Emeka, O. J., & Ueda, N. (n.d.). Application and Potential of Drone Technology in Oil Palm Plantation: Potential and Limitations. Journal of Sensors, 2(22), 1–18.
Zhang, Q., Hu, M., Zhou, Y., Wan, B., Jiang, L., Zhang, Q., & Wang, D. (2022). Effects of UAV-LiDAR and Photogrammetric Point Density on Tea Plucking Area Identification. Remote Sensing, 14(6), 1505. https://doi.org/10.3390/rs14061505