Monitoring of tomato plant health through convolutional neural networks computer engineering

Abstract

The study conducted with the aim of providing an in-depth understanding of the state-of-the-art technologies, their strengths, limitations, and potential areas for improvement proposes a comprehensive exploration of methodologies for the early identification of tomato plant leaf diseases, emphasizing the integration of advanced image processing techniques, convolutional neural networks (CNNs) and open-source algorithms. The culmination of this survey contributes to the development of a dependable, secure, and precise framework tailored to the specificities of tomato plant diseases. The insights derived are poised to inform and guide future research endeavours, offering a holistic perspective on the advancements in early disease detection and predictive mechanisms within the realm of agricultural practices.

Plant phenotyping relevance

トマト葉の病徴を画像処理とCNNで早期検出する手法を対象としたレビューであり、植物病害状態の画像ベース表現型評価が中心です。

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