Avo-AirDB: An avocado UAV Database for agricultural image segmentation and classification

Amraoui, Khalid El; Lghoul, Mouataz; Ezzaki, Ayoub; Masmoudi, Lhoussaine; Hadri, Majid; Elbelrhiti, Hicham; Simo, Aziz Abdou

doi:10.1016/j.dib.2022.108738

Cited by 7 publications

(1 citation statement)

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“…The journal "Data in Brief" features seven relevant articles [11][12][13][14][15][16][17], including one that pertains to UAV images of a cotton field [11], another that focuses on UAV data for avocado classification [12], two that present UAV images obtained over a vineyard [13,14], and one that presents plant and soil data for forage crops [15]. Additionally, there is an article that showcases UAV RGB images of soybean crops [16] and another that features hyperspectral imagery of potato cultivation [17].…”

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A Multispectral UAV Imagery Dataset of Wheat, Soybean and Barley Crops in East Kazakhstan

Maulit

Nugumanova

Apayev

et al. 2023

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This study introduces a dataset of crop imagery captured during the 2022 growing season in the Eastern Kazakhstan region. The images were acquired using a multispectral camera mounted on an unmanned aerial vehicle (DJI Phantom 4). The agricultural land, encompassing 27 hectares and cultivated with wheat, barley, and soybean, was subjected to five aerial multispectral photography sessions throughout the growing season. This facilitated thorough monitoring of the most important phenological stages of crop development in the experimental design, which consisted of 27 plots, each covering one hectare. The collected imagery underwent enhancement and expansion, integrating a sixth band that embodies the normalized difference vegetation index (NDVI) values in conjunction with the original five multispectral bands (Blue, Green, Red, Red Edge, and Near Infrared Red). This amplification enables a more effective evaluation of vegetation health and growth, rendering the enriched dataset a valuable resource for the progression and validation of crop monitoring and yield prediction models, as well as for the exploration of precision agriculture methodologies.

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