Daftar Situs Slot Gacor Server Thailand Mudah Maxwin Hari Ini
CARITOGEL > Slot Thailand Situs Slot Gacor Gampang Menang Maxwin Terbaru Hari Ini
Slot gacor dan slot thailand hari ini terbukti mudah menang.
CARITOGEL merupakan situs slot online terbaru yang sedang populer di kalangan para pecinta judi online. Dikenal dengan koleksi permainan slot gacor dan mudah menang, CARITOGEL menjadi pilihan utama bagi para pemain yang mencari pengalaman berjudi yang seru dan menguntungkan.
Salah satu keunggulan utama dari CARITOGEL adalah koleksi permainan slot Thailand yang disediakan. Slot Thailand dikenal dengan desain yang menarik, fitur bonus yang menggiurkan, serta tingkat kemenangan yang tinggi. Para pemain dapat menikmati berbagai jenis permainan slot Thailand seperti Dragon's Luck, Thai Paradise, dan Thai Temple yang semua memiliki tingkat kemenangan yang tinggi dan bisa memberikan keuntungan besar bagi para pemain.
Selain itu, CARITOGEL juga menyediakan berbagai jenis permainan slot online dari provider terkemuka seperti Pragmatic Play, Spadegaming, dan Habanero. Dengan begitu, para pemain memiliki banyak pilihan permainan slot gacor yang bisa dimainkan sesuai dengan selera dan keberuntungan masing-masing.
Slot gacor hari ini adalah salah satu fitur unggulan dari CARITOGEL yang membuat para pemain semakin tertarik untuk bergabung. Dengan adanya slot gacor hari ini, para pemain memiliki kesempatan lebih besar untuk meraih kemenangan dan mendapatkan jackpot yang besar. Fitur ini sangat diminati oleh para pemain karena memberikan sensasi bermain yang lebih seru dan menarik.
Tak hanya itu, CARITOGEL juga menawarkan berbagai promosi dan bonus menarik yang bisa dinikmati oleh para pemain setiap harinya. Mulai dari bonus deposit, cashback, hingga bonus referral yang bisa meningkatkan peluang para pemain untuk mendapatkan keuntungan lebih besar. Dengan adanya promosi dan bonus yang menggiurkan ini, para pemain bisa merasa lebih diuntungkan dan semakin termotivasi untuk bermain di CARITOGEL.
Keamanan dan kenyamanan para pemain juga menjadi prioritas utama bagi CARITOGEL. Situs ini menggunakan sistem keamanan terbaik untuk melindungi data dan transaksi para pemain sehingga para pemain bisa bermain dengan tenang dan nyaman tanpa perlu khawatir akan kebocoran data pribadi. Selain itu, CARITOGEL juga menyediakan layanan customer service yang siap membantu para pemain selama 24 jam penuh setiap harinya. Para pemain bisa menghubungi customer service melalui live chat, telepon, atau email untuk mendapatkan bantuan dan informasi yang dibutuhkan.
Dengan berbagai keunggulan dan fitur unggulan yang ditawarkan, tidak heran jika CARITOGEL menjadi situs slot thailand online terbaik dan terpercaya saat ini. Para pemain bisa merasakan pengalaman berjudi yang seru dan menguntungkan di CARITOGEL tanpa perlu khawatir akan keamanan dan kenyamanan bermain. Jadi, tunggu apalagi? Segera bergabung dan rasakan sensasi bermain slot gacor dan mudah menang di CARITOGEL sekarang juga.
- Choosing a selection results in a full page refresh.
- Opens in a new window.
A Review and Progress of Research on Autonomous Drone in Agriculture, Delivering Items and Geographical Information Systems (GIS)
Ieee account.
- Change Username/Password
- Update Address
Purchase Details
- Payment Options
- Order History
- View Purchased Documents
Profile Information
- Communications Preferences
- Profession and Education
- Technical Interests
- US & Canada: +1 800 678 4333
- Worldwide: +1 732 981 0060
- Contact & Support
- About IEEE Xplore
- Accessibility
- Terms of Use
- Nondiscrimination Policy
- Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.
Applications of Drones in Smart Agriculture
- First Online: 09 March 2023
Cite this chapter
- Satya Prakash Kumar 9 ,
- A. Subeesh 9 ,
- Bikram Jyoti 9 &
- C. R. Mehta 9
Part of the book series: Advanced Technologies and Societal Change ((ATSC))
598 Accesses
The application of drones in agriculture has opened-up a new horizon to increase the agriculture outputs and real-time access to high-quality information. Crop monitoring has become a simple task with the emergence of drone-based data collection, replacing the traditional labor-intensive and time-consuming data collection. Drones can assist in precision agriculture by performing variety of agricultural tasks including soil health monitoring, seed planting, fertilizer application, crop stress management, irrigation schedule planning, weed management, crop yield management, and weather analysis. Drones with infrared, multispectral, and hyperspectral sensors can analyze crop health and soil conditions precisely and accurately. Spraying drones can help to reduce operator exposure while also improving the capacity to distribute chemicals in a timely and spatially resolved manner. Farmers can save time and water by recognizing areas that need a lot of water. At the same time, precision farming techniques can increase crop yield and quality. Drones can offer various solutions in many areas of agriculture and allied sciences such as yield monitoring, scouting and bird scaring in horticulture, crop health monitoring, forest diseases mapping, monitoring fish farm and livestock population, and mapping feed and fodder grasses in livestock management. The plethoras of drone-based applications in agriculture on spatiotemporal scales make it a promising futuristic technology to address the challenge of growing food insecurity.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
- Available as PDF
- Read on any device
- Instant download
- Own it forever
- Available as EPUB and PDF
- Compact, lightweight edition
- Dispatched in 3 to 5 business days
- Free shipping worldwide - see info
- Durable hardcover edition
Tax calculation will be finalised at checkout
Purchases are for personal use only
Institutional subscriptions
Aboutalebi, M., Allen, L.N., Torres-Rua, A.F., McKee, M., Coopmans, C.: Estimation of soil moisture at different soil levels using machine learning techniques and unmanned aerial vehicle (UAV) multispectral imagery. In: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping, vol. IV, no. 11008, pp. 216–226 (2019)
Google Scholar
Adão, T., Hruška, J., Pádua, L., Bessa, J., Peres, E., Morais, R., & Sousa, J. J. Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote sens. 9 (11), 1110 (2017)
Ahilan, T., Adityan, V.A., Kailash, S.: Efficient utilization of unmanned aerial vehicle (UAV) for fishing through surveillance for fishermen. Int. J. Aerosp. Mech. Eng. 9 (8), 1468–1471 (2015)
Anonymous: The Drone Rules 2021. Ministry of Civil Aviation, Government of India (2022)
Anonymous: FAO. The State of World Fisheries and Aquaculture 2020. Sustainability in Action, FAO, Rome, Italy (2020)
Awais, M., Li, W., Cheema, M.J.M., Hussain, S., AlGarni, T.S., Liu, C., Ali, A.: Remotely sensed identification of canopy characteristics using UAV-based imagery under unstable environmental conditions. Environ. Technol. Innov. 22 , 101465 (2021)
Article Google Scholar
Bhandari, A.K., Kumar, A., Singh, G.K.: Feature extraction using normalized difference vegetation index (NDVI): a case study of Jabalpur city. Procedia Technol. 6 , 612–621 (2012)
Campos, J., García-Ruíz, F., Gil, E.: Assessment of vineyard canopy characteristics from vigour maps obtained using UAV and satellite imagery. Sensors 21 (7), 2363 (2021)
Candiago, S., Remondino, F., De Giglio, M., Dubbini, M., Gattelli, M.: Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote Sensing 7 (4), 4026–4047 (2015)
Casa, R., Pascucci, S., Pignatti, S., Palombo, A., Nanni, U., Harfouche, A., Laura, L., Di Rocco, M., Fantozzi, P.: UAV-based hyperspectral imaging for weed discrimination in maize. Precision Agric. 19 , 24–35 (2019)
Chen, A., Orlov-Levin, V., Meron, M.: Applying high-resolution visible-channel aerial imaging of crop canopy to precision irrigation management. Agric. Water Manag. 216 , 196–205 (2019)
Das, S., Christopher, J., Apan, A., Choudhury, M.R., Chapman, S., Menzies, N.W., Dang, Y.P.: Evaluation of water status of wheat genotypes to aid prediction of yield on sodic soils using UAV-thermal imaging and machine learning. Agric. For. Meteorol. 307 , 108477 (2021)
Etienne, A., Saraswat, D.: Machine learning approaches to automate weed detection by UAV based sensors. In: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping, vol. IV, no. 11008, pp. 202–215 (2019)
Falco, N., Wainwright, H.M., Dafflon, B., Ulrich, C., Soom, F., Peterson, J.E., Brown, J.B., Schaettle, K.B., Williamson, M., Cothren, J.D., Ham, R.G.: Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery. Sci. Rep. 11 (1), 1–17 (2021)
Feng, A., Zhou, J., Vories, E.D., Sudduth, K.A., Zhang, M.: Yield estimation in cotton using UAV-based multi-sensor imagery. Biosys. Eng. 193 , 101–114 (2020)
Furukawa, F., Maruyama, K., Saito, Y.K., Kaneko, M.: Corn height estimation using UAV for yield prediction and crop monitoring. In: Unmanned Aerial Vehicle: Applications in Agriculture and Environment, pp. 51–69. Springer, Cham (2020)
Chapter Google Scholar
Gómez-Gálvez, F.J., Pérez-Mohedano, D., de la Rosa-Navarro, R., Belaj, A.: High-throughput analysis of the canopy traits in the worldwide olive germplasm bank of Córdoba using very high-resolution imagery acquired from unmanned aerial vehicle (UAV). Sci. Hortic. 278 , 109851 (2021)
González-Jorge, H., Martínez-Sánchez, J., Bueno, M., Arias, A.P.: Unmanned aerial systems for civil applications: a review. Drones 1 , 2 (2017)
Góraj, M., Wróblewski, C., Ciężkowski, W., Jóźwiak, J., Chormański, J.: Free water table area monitoring on wetlands using satellite and UAV orthophotomaps-Kampinos National Park case study. Meteorol. Hydrol. Water Manage. Res. Oper. Appl. 7 (2019)
Han, L., Yang, G., Dai, H., Xu, B., Yang, H., Feng, H., Li, Z., Yang, X.: Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data. Plant Methods 15 (1), 1–19 (2019)
Hu, B., Zhou, Y., Jiang, Y., Ji, W., Fu, Z., Shao, S., Li, S., Huang, M., Zhou, L., Shi, Z.: Spatio-temporal variation and source changes of potentially toxic elements in soil on a typical plain of the Yangtze River Delta, China. J. Environ. Manage. 271 , 110943 (2020)
Hunter, J.E., III., Gannon, T.W., Richardson, R.J., Yelverton, F.H., Leon, R.G.: Integration of remote-weed mapping and an autonomous spraying unmanned aerial vehicle for site-specific weed management. Pest Manag. Sci. 76 (4), 1386–1392 (2020)
Iost Filho, F.H., Heldens, W.B., Kong, Z., de Lange, E.S.: Drones: innovative technology for use in precision pest management. J. Econ. Entomol. 113 , 1–25 (2020)
Jarman, M., Vesey, J., Febvre, P.: Unmanned Aerial Vehicles (UAVs) for UK Agriculture: Creating an Invisible Precision Farming Technology. White Paper (2016)
Jiménez-Brenes, F.M., Lopez-Granados, F., Torres-Sánchez, J., Peña, J.M., Ramírez, P., Castillejo-González, I.L., de Castro, A.I.: Automatic UAV-based detection of Cynodon dactylon for site-specific vineyard management. PLoS ONE 14 (6), 0218132 (2019)
Jorge, J., Vallbé, M., Soler, J.A.: Detection of irrigation inhomogeneities in an olive grove using the NDRE vegetation index obtained from UAV images. Eur. J. Remote Sens. 52 (1), 169–177 (2019)
Kalischuk, M., Paret, M.L., Freeman, J.H., Raj, D., da Silva, S., Eubanks, S., Wiggins, Z., Lollar, M., Marois, J.J., Mellinger, H.C., Das, J.: An improved crop scouting technique incorporating UAV-assisted multispectral crop imaging into conventional scouting practice for gummy stem blight in watermelon. Plant Dis. First Look (2019)
Li, B., Xu, X., Han, J., Zhang, L., Bian, C., Jin, L., Liu, J.: The estimation of crop emergence in potatoes by UAV RGB imagery. Plant Methods 15 (1), 1–13 (2019)
Liao, S., Lei, X., Xiao, Y.: The compound control method for pesticide spraying quadrotor UAVs. In: IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, pp.1022–1027. IEEE (2019)
Matese, A., Baraldi, R., Berton, A., Cesaraccio, C., Di Gennaro, S.F., Duce, P., Facini, O., Mameli, M.G., Piga, A., Zaldei, A.: Estimation of water stress in grapevines using proximal and remote sensing methods. Remote Sens. 10 , 114 (2018)
Milics, G.: Application of UAVs in precision agriculture. In: International Climate Protection, pp. 93–97. Springer (2019)
Mogili, U.R., Deepak, B.B.V.L.: Review on application of drone systems in precision agriculture. Procedia Comput. Sci. 133 , 502–509 (2018)
Mukherjee, A., Misra, S., Raghuwanshi, N.S.: A survey of unmanned aerial sensing solutions in precision agriculture. J. Netw. Comput. Appl. 148 , 102461 (2019)
Padua, L., Marques, P., Adão, T., Guimarães, N., Sousa, A., Peres, E., Sousa, J.J.: Vineyard variability analysis through UAV-based vigour maps to assess climate change impacts. Agronomy 9 (10), 581 (2019)
Radoglou-Grammatikis, P., Sarigiannidis, P., Lagkas, T., Moscholios, I.: A compilation of UAV applications for precision agriculture. Comput. Netw. 172 , 107148 (2020)
Ramos, A.P.M., Osco, L.P., Furuya, D.E.G., Gonçalves, W.N., Santana, D.C., Teodoro, L.P.R., da Silva Junior, C.A., Capristo-Silva, G.F., Li, J., Baio, F.H.R., Junior, J.M.: A random forest ranking approach to predict yield in maize with uav-based vegetation spectral indices. Comput. Electron. Agric. 178 , 105791 (2020)
Ronchetti, G., Mayer, A., Facchi, A., Ortuani, B., Sona, G.: Crop row detection through UAV surveys to optimize on-farm irrigation management. Remote Sens. 12 (12), 1967 (2020)
Sankey, T., Donager, J., McVay, J., Sankey, J.B.: UAV LIDAR and hyperspectral fusion for forest monitoring in the southwestern USA. Remote Sens. Environ. 195 , 30–43 (2017)
Shivers, S.W., Roberts, D.A., McFadden, J.P.: Using paired thermal and hyperspectral aerial imagery to quantify land surface temperature variability and assess crop stress within California orchards. Remote Sens. Environ. 222 , 215–231 (2019)
Smigaj, M., Gaulton, R., Suárez, J.C., Barr, S.L.: Canopy temperature from an unmanned aerial vehicle as an indicator of tree stress associated with red band needle blight severity. For. Ecol. Manage. 433 , 699–708 (2019)
Stephan, F., Reinsperger, N., Grünthal, M., Paulicke, D., Jahn, P.: Human drone interaction in delivery of medical supplies: a scoping review of experimental studies. PLoS ONE 17 (4), 0267664 (2022)
Su, J., Coombes, M., Liu, C., Zhu, Y., Song, X., Fang, S., Guo, L., Chen, W.H.: Machine learning-based crop drought mapping system by UAV remote sensing RGB imagery. Un. Syst. 8 (1), 71–83 (2020)
Toriyama, K.: Development of precision agriculture and ICT application thereof to manage spatial variability of crop growth. Soil Sci. Plant Nutr. 66 , 811–819 (2020)
Torresan, C., Berton, A., Carotenuto, F., Di Gennaro, S.F., Gioli, B., Matese, A., Miglietta, F., Vagnoli, C., Zaldei, A., Wallace, L.: Forestry applications of UAVs in Europe: a review. Int. J. Remote Sens. 38 (8–10), 2427–2447 (2017)
Ubina, N.A., Cheng, S.C.: A review of unmanned system technologies with its application to aquaculture farm monitoring and management. Drones 6 (1), 12 (2022)
Van der Merwe, D., Burchfield, D.R., Witt, T.D., Price, K.P., Sharda, A.: Drones in agriculture. In: Sparks, D.L. (ed.) Advances in Agronomy, pp. 1–30. Academic Press (2020)
Wallace, L., Lucieer, A., Watson, C., Turner, D.: Development of a UAV-LiDAR system with application to forest inventory. Remote Sens. 4 , 1519–1543 (2012)
Wang, G., Lan, Y., Qi, H., Chen, P., Hewitt, A., Han, Y.: Field evaluation of an unmanned aerial vehicle (UAV) sprayer: effect of spray volume on deposition and the control of pests and disease in wheat. Pest Manag. Sci. 75 (6), 1546–1555 (2019)
Wang, L., Lan, Y., Yue, X., Ling, K., Cen, Z., Cheng, Z., Liu, Y., Wang, J.: Vision-based adaptive variable rate spraying approach for unmanned aerial vehicles. Int. J. Agric. Biol. Eng. 12 (3), 18–26 (2019)
Wen, S., Zhang, Q., Yin, X., Lan, Y., Zhang, J., Ge, Y.: Design of plant protection UAV variable spray system based on neural networks. Sensors 19 (5), 1112 (2019)
Wu, Z., Ni, M., Hu, Z., Wang, J., Li, Q., Wu, G.: Mapping invasive plant with UAV-derived 3D mesh model in mountain area—a case study in Shenzhen Coast, China. Int. J. Appl. Earth Obs. Geoinf. 77 , 129–139 (2019)
Yang, S., Yu, W., Yang, L., Du, B., Chen, S., Sun, W., Jiang, H., Xie, M., Tang, J.: Occurrence and fate of steroid estrogens in a Chinese typical concentrated dairy farm and slurry irrigated soil. J. Agric. Food Chem. 69 (1), 67–77 (2020)
Yao, D., Cheng, L., Wu, Q., Zhang, G., Wu, B., He, Y.: Assessment and prediction of fishery water quality using electrochemical sensor array carried by UAV. In: 2019 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN), pp.1–4 (2019)
Zhang, J., Xie, T., Yang, C., Song, H., Jiang, Z., Zhou, G., Zhang, D., Feng, H., Xie, J.: Segmenting purple rapeseed leaves in the field from UAV RGB imagery using deep learning as an auxiliary means for nitrogen stress detection. Remote Sens. 12 (9), 1403 (2020)
Zheng, H., Cheng, T., Zhou, M., Li, D., Yao, X., Tian, Y., Cao, W., Zhu, Y.: Improved estimation of rice aboveground biomass combining textural and spectral analysis of UAV imagery. Precision Agric. 20 (3), 611–629 (2019)
Zhou, X., Zheng, H.B., Xu, X.Q., He, J.Y., Ge, X.K., Yao, X., Cheng, T., Zhu, Y., Cao, W.X., Tian, Y.C.: Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery. ISPRS J. Photogramm. Remote. Sens. 130 , 246–255 (2017)
Download references
Author information
Authors and affiliations.
ICAR-Central Institute of Agricultural Engineering, Bhopal, India
Satya Prakash Kumar, A. Subeesh, Bikram Jyoti & C. R. Mehta
You can also search for this author in PubMed Google Scholar
Corresponding author
Correspondence to Satya Prakash Kumar .
Editor information
Editors and affiliations.
Department of Agricultural Biology, University of Jaffna, Jaffna, Sri Lanka
Kandiah Pakeerathan
Rights and permissions
Reprints and permissions
Copyright information
© 2023 Centre for Science and Technology of the Non-aligned and Other Developing Countries
About this chapter
Kumar, S.P., Subeesh, A., Jyoti, B., Mehta, C.R. (2023). Applications of Drones in Smart Agriculture. In: Pakeerathan, K. (eds) Smart Agriculture for Developing Nations. Advanced Technologies and Societal Change. Springer, Singapore. https://doi.org/10.1007/978-981-19-8738-0_3
Download citation
DOI : https://doi.org/10.1007/978-981-19-8738-0_3
Published : 09 March 2023
Publisher Name : Springer, Singapore
Print ISBN : 978-981-19-8737-3
Online ISBN : 978-981-19-8738-0
eBook Packages : Computer Science Computer Science (R0)
Share this chapter
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative
- Publish with us
Policies and ethics
- Find a journal
- Track your research
Powerful Role of Drones in Agriculture
While aiming to produce enough food and remain sustainable, agriculture is facing significant changes. In the new agricultural era, farmers are able to use various high-tech sensing devices based on GPS, variable rate application, steering systems and remote sensing, as well as farm management software . The introduction and the use of modern and precise farm technologies brings revolutionary changes into farming. In other words, modern farm technology revolutionizes the way in which farmers work.
By using precise technology, farmers are able to optimize both farm productivity and profitability based on real-time field information thus protecting the environment , which can be a turning point to success.
“Drones can monitor any type of crop during its growing season, in any area.”
What Are Drones?
Unmanned aerial vehicles (UAV), commonly named drones, are small aerial platforms weighing up to 20 kg (50 lbs). Due to their size, they cannot be boarded by a human body (yet). Drones can be operated in two ways; directly, in which a human has complete control of the vehicle by wireless remote; and autonomously, in which the vehicle is able to control itself and follow a route based on the data from GPS or other sensors.
There are many different kinds of unmanned aerial vehicles and can be categorized into the following groups:
- Fixed wing ; very simple vehicle to control. It has some form of a non-movable wing and a propeller that facilitates forward movement. Due to its construction, it must always be moving relative to the air around it to stay aloft. Hence, it’s operation can be greatly affected by the wind. Another limiting factor is that larger drones require some kind of runway area that can be used for deployment and retrieval, while smaller ones can be hand launched and retrieved by landing on a soft surface.
- Rotary wing ; the most common drone type. It looks like a small helicopter since it has multiple rotors (typically 4-8). Due to its rotary system, the drone has the ability to hover and can be vertically deployed and retrieved. The rotary wing vehicle has some advantages over the other types. It is small and easily transportable and less liable to mechanical failure . The main disadvantages, though, are its limitation in cargo it can carry, as well as its battery life, which is limited to allow only 15 minutes or less of flight.
- Tethered vehicle is a common drone tethered to a wire to eliminate the need for a remote controller. Drone movement is therefore confined according to the tether. Moreover, tethered drones have many different variations. They can range from a standard drone moved according to the tether to a drone tethered with a microfilament wire with an installed power system for unlimited flight.
- Lighter-than-air (LTA) vehicles include blimps and other typical helium-filled crafts tethered to some kind of wire. Their main disadvantages are the difficulty in transporting because of their size, and the fact that they cannot tolerate even moderate wind speeds. Therefore, LTA drones are used less in farming.
Crop Data Delivered from the Air with Unmanned Aerial Vehicle (UAV)
Drones are small and light aerial vehicles which may fly at extremely high altitudes and carry various navigation systems or recording devices such as RGB cameras, infrared cameras, and other sensors . Due to their ability to deploy various sensors and capture high-resolution and low-cost images of crop conditions, drones are very useful in farming.
Initially used for chemical spraying, today drones are a great tool for capturing aerial imagery with platform mounted cameras and sensors. Images can range from simple visible-light photographs to multi-spectral imagery that can be used to assess different aspects of plant health, weeds, and assets.
Drones collect raw data and translate it with algorithms into useful information . Therefore, they can be used for various applications in farming, such as the monitoring of the following parameters:
- Crop health ; damage made by pests, color change due to pest infection
- Vegetation indices ; leaf area, anomaly detection, treatment efficacy, phenology, yield
- Plant height ; plant height and density
- Plant scouting ; plant size, plot statistics, stand number, compromised plots, planter skips
- Water needs ; water-stressed parts of the field/orchard in need of watering
- Soil analysis ; nutrient availability for plant nutrient management
To summarize, drones help farmers optimize the use of inputs such as seeds, fertilizers, water, and pesticides more efficiently. This allows timely protection of crops from pests, saves time for crop scouting, reduces overall cost in farm production, and secures high yield and quality crops .
“Drones help farmers optimize the use of inputs such as seeds, fertilizers, water, and pesticides more efficiently.”
From Raw Data to Useful Information
Flying over the field, the drone takes high-resolution pictures with a camera or sensor. Based on a measured parameter, these images are captured in different bands from visible (color), near-infrared to infrared spectrum. The collected images are raw data which requires further interpretation. Immediately after capturing the image, the images are directly sent to the cloud/software where different prescription maps are created depending on the operation the farmer wants to perform on the field. The maps can then be uploaded to the specific farm equipment which will adjust the number of inputs (seeds, fertilizers, pesticides) that would need to be applied to the field accordingly.
“Drones capture images of crop conditions and create Normalized Differential Vegetation Index (NDVI) maps.”
In the era of precision farming, drones are acting as an essential technology that will take farming to a completely new level . They are a cost-effective way to collect data about various crop conditions in a relatively short period of time. Drones have also shown great potential in the ability to provide sustainable farming, improve yield, and increase overall farm profitability.
Technology in farming is constantly evolving. Collecting accurate and reliable georeferenced data based on GPS coordinates and automated steering systems, along with the use of remote sensing (drones), is an essential part of precision farming which can optimize both farm productivity and profitability.
Although there are some risks and limitations, precision farming and related technology have great potential in dealing with the challenges of a modern farm production and at the same time protecting natural resources.
Text sources: UKY || GRDC || Tethered Drones
Image sources: Unmanned Aerial Online || Kapshop || sourceable.net || Unmanned Aerial Online
- Enterprise Farms
- Cooperatives
- Food & Beverages
- Input Manufacturers
- Agronomic Advisory
- Farm Insights
- Farm Enterprise
- Farm Advisory
- Agriculture Supply Chain
- Traceability
- Partner Program
- ROI Calculator
- Farm Digitalization Score
- Case Studies
- Book a Meeting
- Privacy Policy
- Terms of Use
- BOOK A MEETING
In a few minutes, please check your email inbox where we have sent you a copy of the ebook.
*Please check your spam or promotions folder in case the email doesn’t arrive.
In a few minutes, please check your email inbox where we have sent you a copy of the report.
In a few minutes, please check your email inbox where we have sent a link to the webinar.
In a few minutes, please check your email inbox for further instructions.
We will get back to you as soon as possible.
Full Name *
Please leave this field empty. Subscribe to AGRIVI newsletter.
By clicking SUBMIT you agree to our Privacy Policy and Terms of Use. We are committed to your privacy. AGRIVI uses the provided information to contact you about products, news and other information. You may unsubscribe at any time.
"Advertisement"
Essay On the Role of drone technology in Agriculture
Hello My Dear Friend, In this post “ Essay On the Role of drone technology in Agriculture “, We will be going to read about the Role of drone technology in Agriculture as an Essay in detail. So…
Let’s Start…
Introduction
Technology improvements have revolutionized several industries throughout the years, including agriculture. Drone technology is one significant innovation that has recently gained recognition.
Drones, also known as unmanned aerial vehicles (UAVs), are self-flying devices that can be remotely controlled or designed to function independently.
Drones have emerged as a game-changing instrument in agriculture, revolutionizing farming practices and enhancing efficiency.
This paper investigates the role of drone technology in agriculture, emphasizing its advantages, applications, and possible impact on the industry.
Benefits of Drone Technology in Agriculture
1. precision farming:.
Precision farming entails the use of technology to collect data and make informed judgments about farming practices.
Drones outfitted with a variety of sensors and cameras can collect high-resolution imagery as well as real-time data on crops, soil conditions, and weather patterns.
This data enables farmers to monitor their fields, predict possible difficulties such as pests or illnesses, and optimize fertilizer and irrigation use.
2. Improved Crop Monitoring:
Farmers used to have to manually inspect their fields to monitor crop health, which was a time-consuming and labor-intensive operation.
Drones with thermal or multispectral cameras may record detailed photographs of crops, detecting minute variations in plant health that the naked eye may miss.
This allows for the early diagnosis of illnesses, nutrient deficits, and irrigation issues, allowing for timely intervention and preventing crop losses.
3. Crop Spraying:
Crop spraying is another important application of drones in agriculture. Drones-carrying sprayers may precisely apply pesticides, herbicides, or fertilizers to crops, minimizing waste and impact on the environment.
Drones can travel fields more efficiently than traditional methods, such as tractor-mounted sprayers, reaching difficult-to-access regions while minimizing soil compaction.
4. Livestock Monitoring:
Drones outfitted with cameras and sensors can also be used to keep an eye on livestock. They can survey wide tracts of grazing pasture, detect abnormal animal behavior, and locate injured or missing animals.
This type of monitoring enables farmers to treat health issues quickly, enhance pasture management, and maintain the overall well-being of their cattle.
5. Time and Cost Savings:
Drones can considerably cut the time and expense associated with certain farming activities.
Farmers may discover problem areas more rapidly with rapid data collection and analysis, leading to tailored treatments and lowering the need for extensive treatment.
Furthermore, the usage of drones eliminates the need for manual labor in operations like field inspection and crop spraying, saving time and resources.
Applications of Drone Technology in Agriculture
1. field mapping:.
Drones outfitted with GPS technology and mapping software may provide precise, high-resolution maps of fields.
These maps contain useful information about field boundaries, soil changes, and topography, allowing farmers to better plan planting methods, optimize resource allocation, and adopt precision agriculture techniques.
2. Crop Health Assessment:
Drones can take comprehensive imagery of crops, allowing farmers to analyze crop health and identify stress causes.
Drones equipped with multispectral cameras can detect fluctuations in plant reflectance, revealing early indicators of disease or nutrient deficits.
Farmers can take prompt corrective adjustments by analyzing this data, decreasing crop losses, and increasing yields.
3. Irrigation Management:
Thermal cameras on drones may detect changes in plant temperature, suggesting water stress.
This data assists farmers in identifying parts of the field that require irrigation adjustments, resulting in more efficient water use and better irrigation management.
4. Weed Detection and Management:
Drones can help with weed detection and management by recording high-resolution photos and utilizing image processing algorithms to identify and map weed-infested areas.
This enables farmers to target specific weed management regions, lowering reliance on chemicals and minimizing environmental effects.
5. Yield Estimation: By analyzing crop data collected
Farmers can more accurately estimate crop yields using drones. This data is useful for crop planning, harvest timing, and market forecasting, allowing for better decision-making and resource management.
Potential Impact and Future Outlook
Drone technology integration in agriculture has enormous potential to alter the business.
Drones can help farmers enhance production, reduce environmental impact, and increase profitability by enabling accurate and data-driven farming practices.
Furthermore, the deployment of drones can improve safety by limiting human exposure to potentially dangerous jobs such as chemical spraying or checking inaccessible locations.
Further advances in drone technology, such as extended flight time, improved sensors, and enhanced data analytics, will open up new opportunities for agriculture in the future.
Integration with other technologies such as artificial intelligence and machine learning can improve drone capabilities, allowing them to make more precise and autonomous decisions based on real-time data.
Drone technology’s importance in agriculture cannot be overstated. Drones have shown the ability to revolutionize farming practices ranging from precision farming and crop monitoring to crop spraying and livestock management.
Drones provide farmers with vital insights by collecting massive volumes of data, allowing them to make educated decisions and optimize their operations.
Drones are poised to play an increasingly important role in defining the future of agriculture, leading to more sustainable and efficient farming practices as technology advances.
Finally, Thanks For Reading “ Essay On the Role of drone technology in Agriculture “.
If you have any questions related to “ Essay On the Role of drone technology in Agriculture “, So, please comment below.
Walt Nauta Biography, Career, Wife, Net Worth & More
Novak Djokovic Biography, Age, Career, Net Worth & More
Leave a Comment Cancel reply
Save my name, email, and website in this browser for the next time I comment.
IMAGES
VIDEO
COMMENTS
Importance of Drone in Agriculture. 127. Drone technology is not merely a technological novelty; it. represents a paradigm shift in t he way we approach a gricult ure. The. ability of drones to ...
Monitoring Plant Health. Another important role that drones play in agriculture comes in the form of plant monitoring. Some drones are equipped with specialized imaging tools known as Normalized Difference Vegetation Index (NDVI). These make use of detailed color information to display the health of plants. With this knowledge, the farmer can ...
Drone is frequently utilized in farms to help the farmers as a part. of "Precision Agriculture" to modernize farming in dev eloped. countries. Within a few years, drones will become mor e ...
Manually, it is not possible to assess the overall condition of crops in large agricultural farms. Drone-based mapping of field crops enables farmers to have a close eye on the entire crop area at once, which can help the farmers to find out which particular area in the field requires special attention (Cancela et al. 2019; Small 1973).Drones inspect the field with infrared cameras mounted on ...
As a multidisciplinary and multipurpose technology in agriculture, drones have been investigated from various perspectives. For example, scholars have examined drone applications in agriculture (Kulbacki et al., 2018, Mogili and Deepak, 2018), their contribution to precision agriculture (Puri et al., 2017, Tsouros et al., 2019), their complementarity with other cutting-edge technologies (Al ...
Taking Drones Further. The potential for UAVs in the improvement of sustainable agriculture is huge. Already the agriculture drone market is predicted to be worth US$32.4 billion - an indication that the industry is beginning to recognize the benefits over more traditional methods, such as ground mapping.
The use of drones has become widespread in agriculture, and it is associated with unique opportunities and challenges. The most common role of drones in agriculture is as a remote sensing platform to assess and monitor crops, but emerging agricultural applications include precision distribution of agricultural chemicals and biological control agents, livestock health monitoring, and remote ...
for farmers. Overall, drones have the potential to revolutionize farming practices, leading to increased efficiency, productivity, and sustainability in agriculture. Index Terms—Drone, Agriculture, UAV I. INTRODUCTION The use of drones in agriculture has gained significant attention in recent years due to their potential to revolutionize
Applications of Drones in Agriculture and Allied Sectors . Application of Drones in Agriculture . The agricultural sector is under pressure to adopt new technology and processes to maximize agricultural output, driven by a global need to feed an expanding population on a limited cultivable area [24].
The development of technology in drones supports the farmers to test the soil fertility. It provides effective and efficient results. Agricultural drones (AD) are a recent wonder that has been introduced into farming areas. These are small and fly above the cropland from a distance of 100-400 feet. It is able to capture and analyze images ...
Efficacy of Drone T echnology in Agriculture: A review. ASHUTOSH UPADHYAYA, PAWAN JEET*, PREM KUMAR SUNDARAM, ANIL KUMAR SINGH, KIRTI SAURABH AND MANMOHAN DEO. ABSTRACT. INTRODUCTION. 189. Drones ...
agricultural innovation Drone technology could help farmers around the world monitor their crops, fend off pests, improve land tenure, and more. But to realise its full potential, regulatory regimes are necessary, while keeping citizens' safety and privacy rights secure. The international drone market has grown considerably in the past
Technology has the role of helping to address the challenges for such rational use of resources. More specifically, new advancements in agronomy R&D have the ability to improve the management of renewable biological resources and open new markets in food, energy, and bio-based products [].In the last few decades, Earth observation from space enabled many people (policy makers, technicians ...
fertilizer spraying, irrigation and land mapping, and drone technology are used (Kalantar et al. 2017) (Fig. 1.1). 2 Application Areas of Drone in agriculture Drone technology in agricultural sectors has been playing an important role the past few years, the benets of which are becoming more apparent to farmers. In many
Precision agriculture is an important part of drone research projects today. Agriculture needs commercial drones since the industry took off: and sophisticated analytics and software combine with evolved drone solutions to allow for breakthroughs. For future farming, drones are an essential tool in precision agriculture, as they allow farmers to monitor crop and livestock conditions by air ...
Drones can provide numerous solutions to horticulture domain like crop characterization and counting, crop growth analysis, yield estimation, etc. (Fig. 3.3 ). Some of the key applications of drones in horticulture are as follows: Fig. 3.3. Use of drone for orchards' yield estimation. Full size image.
To summarize, drones help farmers optimize the use of inputs such as seeds, fertilizers, water, and pesticides more efficiently. This allows timely protection of crops from pests, saves time for crop scouting, reduces overall cost in farm production, and secures high yield and quality crops. "Drones help farmers optimize the use of inputs ...
Nagaland, India. An innovative drones technology in agric ulture: A. review. Amit Kumar, Sajeel Ahamad, Maneesh Kumar, Chhail Bihari, Satvaan. Singh, Vibhu Pandey, Khursheed Alam, Shalini Singh ...
Drones technologies saves the excess use of water, pesticides, and herbicides, maintains the fertility of the soil, also helps in the efficient use of man power and elevate the productivity and improve the quality. The objective of this. paper is to review the usage of Drones in agriculture applications.
Across Europe, the use of drones in agriculture varies widely, influenced by differences. in regulatory frameworks, farming practices, and technological ado ption. Countries like. France, the ...
Drones, also known as unmanned aerial vehicles (UAVs), are self-flying devices that can be remotely controlled or designed to function independently. Drones have emerged as a game-changing instrument in agriculture, revolutionizing farming practices and enhancing efficiency. This paper investigates the role of drone technology in agriculture ...
In this article, an effort was made to synthesize available information on multiple uses of drones in Agriculture. Keywords- Labour scarcity, ICT-driven tools, technology, UAVs Discover the world ...
DRONE TECHNOLOGY IN AGRICULTURE F OR SURVEILLANCE AND INSPECTION Section A-Research paper. 1254. Eur. Chem. Bull. 2023,12 (Special Issue 12), 1253-1263. 1. Introduction. Drone use in agriculture ...