The orchard management has improved by adequately utilizing the Remote Sensing (RS) and Geo-Information System (GIS). With the expansion of orchards in recent years, the sector has been facing a lack of skilled workers and specialists for optimal irrigation utilities, nutrient intake, canopy pruning, pest prevention, disease detection, and orchard quality management name a few. Sustainable orchard management will get a potential boost if the orchard database is well documented with standardized remote observation. Also, the observations recorded have optimal spectral, spatial, and temporal parameters to estimate overall changes in the orchard health. The Unmanned Aerial System (UAS)-based RS and GIS provide an approach that allows users to collect data efficiently and orderly. The following case study focuses on mapping a mango orchard by utilizing the derived product of the UAV remote sensing, an RGB ortho-imageries for analysis and generation of the orchard geo-management system.

    The geo analysis of the orchard is broadly subdivided into two major categories: spatial and spectral properties of the canopies and surroundings. First, the canopies’ precise position was determined. Next, the tree height is estimated using the shadow’s length, location, and time when the image was captured. From the classified image, individual canopies are labeled, and their top crown size is compared between manually drawn, semi-automatically generated, and field calculated values. It was observed that even though the area difference between these methods was only 1.08 m2 on average, the difference in perimeter was 8.2 m on average. The automated process can precisely map borders to the pixel level. Simultaneously, the manual method is limited to human perception of boundaries and will vary from user to user. The canopies’ spectral response provides insight and permits the interpretation of their physical properties like health, fruit maturity, and diseases. Furthermore, manual and semi-automatic generated canopies were compared concerning object-based averaged spectra. It was observed that the canopies’ histogram was bimodal in the green band in both cases. This can be attributed to the two-year cycle of Mangifera indica. Hence some trees were boring abundant fruits while other canopies bore lesser fruits. Further analysis by estimating canopies center, their accurate position is mapped which is very useful for logistics and management like planning for minimal distance to cover every tree for plucking fruits, visiting, or pruning, simulation of the spread of canopies, simulation of infectious disease, inter canopy gaps (between canopies or ground where sunlight illumination is available) to name a few. Finally, the orchard features such as ‘Canopy Positional Proximity Value’ (CPPV), a positional parameter of an individual tree concerning other trees in the orchard, are defined, which is further used to determine the ‘Orchard Compactness Factor’ (OCF) as an indicator of how densely the trees are packed in an orchard. OFC and CPPV provides information on orchard density as well as shape-size factor for the orchard geo-management. Hence, UAS-RS and GIS are potential tools that can mitigate many problems associated with orchard geo-management, which may further enhance the overall orchard productivity and sustainability.


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    Title :

    A UAS-Based Approach for Orchard Geo-Information Management System


    Additional title:

    Lecture Notes in Civil Engineering


    Contributors:

    Conference:

    International Conference on Unmanned Aerial System in Geomatics ; 2021 ; Roorkee, India April 02, 2021 - April 04, 2021



    Publication date :

    2023-03-16


    Size :

    15 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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