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The present work proposes to evaluate, compare, and determine software alternatives that present good detection performance and low computational cost for the plant segmentation operation in computer vision systems. In practical aspects, it aims to enable low-cost and accessible hardware to be used efficiently in real-time embedded systems for detecting seedlings in the agricultural environment. The analyses carried out in the study show that the process of separating and classifying plant seedlings is complex and depends on the capture scene, which becomes a real challenge when exposed to unstable conditions of the external environment without the use of light control or more specific hardware. These restrictions are driven by functionality and market perspective, aimed at low-cost and access to technology, resulting in limitations in processing, hardware, operating practices, and consequently possible solutions. Despite the difficulties and precautions, the experiments showed the most promising solutions for separation, even in situations such as noise and lack of visibility.

IOT Automation with Segmentation Techniques for Detection of Plant Seedlings in Agriculture