Pollinators enhance the production of a superior strawberry – A global review and meta-analysis

Gudowska, Agnieszka; Cwajna, Aleksandra; Marjańska, Emilia; Moroń, Dawid

doi:10.1016/j.agee.2023.108815

Cited by 1 publication

(1 citation statement)

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“…Moreover, artificial pollination technologies have seen significant advancements, with the integration of machine learning and artificial intelligence (AI) playing a crucial role in the development of innovative solutions for future farming. Especially, AI and machine learning are being integrated into pollination technologies to develop autonomous pollina-tion systems [15,16]. Based on the above, researchers are developing various drone-based pollination technologies, such as the use of tiny drones equipped with artificial intelligence to autonomously navigate and carry pollen between plants [17,18].…”

Section: Introductionmentioning

confidence: 99%

Is the Artificial Pollination of Walnut Trees with Drones Able to Minimize the Presence of Xanthomonas arboricola pv. juglandis? A Review

Manthos,

Sotiropoulos,

Vagelas

2024

Applied Sciences

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Walnut (Juglans regia L.) is a monoecious species and although it exhibits self-compatibility, it presents incomplete overlap of pollen shed and female receptivity. Thus, cross-pollination is prerequisite for optimal fruit production. Cross-pollination can occur naturally by wind, insects, artificially, or by hand. Pollen has been recognized as one possible pathway for Xanthomonas arboricola pv. juglandis infection, a pathogenic bacterium responsible for walnut blight disease. Other than the well-known cultural and chemical control practices, artificial pollination technologies with the use of drones could be a successful tool for walnut blight disease management in orchards. Drones may carry pollen and release it over crops or mimic the actions of bees and other pollinators. Although this new pollination technology could be regarded as a promising tool, pollen germination and knowledge of pollen as a potential pathway for the dissemination of bacterial diseases remain crucial information for the development and production of aerial pollinator robots for walnut trees. Thus, our purpose was to describe a pollination model with fundamental components, including the identification of the “core” pollen microbiota, the use of drones for artificial pollination as a successful tool for managing walnut blight disease, specifying an appropriate flower pollination algorithm, design of an autonomous precision pollination robot, and minimizing the average errors of flower pollination algorithm parameters through machine learning and meta-heuristic algorithms.

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