Farmers of the twentieth century are adopting the use of leading technical facilities in the field of agriculture to sustain the competitiveness of the global market economy. With the help of modern technology, they are trying to reduce production costs and improve crop yield with better product quality. A new era in which many traditional agricultural practices are replaced by cutting‐edge technologies like Precision Farming (PF), which entails applying the agronomic variables in the right place, at the right time, has been ushered in by technological advancements made in monitoring, supervision, management, and control systems. One of the methods that will enable quick and non‐destructive analysis of agricultural data will turn out to be an IoT integrated smart agriculture drone. This includes taking pictures to analyze crop behavior, finding out how much water the soil can contain, managing irrigation systems, etc. Numerous engineering disciplines, including aerodynamics, electronics, computer programming, and economics are integrated in the design, development, and implementation of drone‐based agricultural systems. In considering the above thought, a problem taken into consideration is for a grape field of approximately 10 acres located near Nashik (28 36'N, 77 12'E) in the state of Maharashtra to analyze the performance of IoT enabled agriculture drones for PF. Main objective is to spray the insecticide with an agriculture drone only on the detection of the green zone of the crop or canopy for effective spraying and hence to reduce the wastage and cost of spraying. Drone image sensor will capture the field images, atmospheric parameters like, temperature, humidity in the field and analyze it on the cloud platform for analyzing the crop health and related parameters. An agriculture drone of 16 L capacity is proposed for spraying 1 acre of land. It could take 7–10 minutes of flight time of the drone to spray the insecticide which can lead to reducing the labor cost by 65%, spraying time by 85% and amount of insecticide by 50%. Drone camera and spraying mechanism is to be synchronized to achieve the objective through IoT platform. For effective spraying from the bottom and the top of the leaf, synchronized UGV and UAV spraying is proposed. Further, time series analysis of the photographic images and video footage captured by the drone camera can be done for the prediction, modeling of crop yields and auditing them with computer vision capabilities of UAV, and developing sufficient datasets. These data sets can be used for machine learning algorithms for AI or DL and for future research related to crop disease forecasting, canopy cover, stress management, and be able to guide farmers for taking corrective actions in advance and evolve best practices for overall improvement in the yield.


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    Titel :

    IoT‐Enabled Unmanned Aerial Vehicle


    Untertitel :

    An Emerging Trend in Precision Farming


    Beteiligte:
    Mohanty, Sachi Nandan (Herausgeber:in) / Ravindra, J.V.R. (Herausgeber:in) / Surya Narayana, G. (Herausgeber:in) / Pattnaik, Chinmaya Ranjan (Herausgeber:in) / Mohamed Sirajudeen, Y. (Herausgeber:in) / Phade, Gayatri (Autor:in) / Kishore, A. T. (Autor:in) / Omkar, S. (Autor:in) / Suresh Kumar, M. (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    22.05.2023


    Format / Umfang :

    24 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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