Identifying Growth Patterns in Arid Zone Onion Crops (AlliumCepa) Using Digital Image Processing

Abstract

The agricultural sector is undergoing a revolution that requires sustainable solutions to the challenges that arise from traditional farming methods. To address these challenges, technical and sustainable support is needed to develop projects that improve crop performance. This study focuses on the onion crop and the challenges presented throughout its phenological cycle. Aerial monitoring using unmanned aerial vehicles (UAV) and digital image processing were used to identify patterns in the onion crop, including humid areas, weed growth, vegetation deficits, and decreased harvest performance. An algorithm was developed to identify the patterns that most affected crop growth, as the average local production reported was 40.166 ton/ha, but only 25.00 ton/ha was reached due to blight caused by constant humidity and limited sunlight. This resulted in the death of leaves and poor development of bulbs, with 50% of the production being of medium size. It is estimated that approximately 20% of the production was lost due to blight and unfavorable weather conditions.

Code and data availability

The article describes UAV imagery of an onion crop and custom image-processing algorithms, but contains no data availability statement, no public repository deposit, and no author-provided URL for the image dataset, code, or models. The only URLs present are citations to prior work (e.g., the Meyer & Neto reference), a

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