Abstract：
Over the last few years, several studies have appeared that employ Artificial Intelligence (AI) techniques to improve sustainable development in the agricultural sector. Specifically, these intelligent techniques provide mechanisms and procedures to facilitate decision-making in the agri-food industry. One of the application areas has been the automatic detection of plant diseases. These techniques, mainly based on deep learning models, allow for analysing and classifying plants to determine possible diseases facilitating early detection and thus preventing the propagation of the disease. In this way, this paper proposes an Edge-AI device that incorporates the necessary hardware and software components for automatically detecting plant diseases from a set of images of a plant leaf. In this way, the main goal of this work is to design an autonomous device that allows the detection of possible diseases that can detect potential diseases in plants. This will be achieved by capturing multiple images of the leaves and implementing data fusion techniques to enhance the classification process and improve its robustness. Several tests have been carried out to determine that the use of this device significantly increases the robustness of the classification responses to possible plant diseases.
摘要：
在过去的几年里，一些研究已经出现，采用人工智能（AI）技术来改善农业部门的可持续发展。具体来说,这些智能技术提供了促进农业食品工业决策的机制和程序。应用领域之一是植物病害的自动检测。这些技术,主要基于深度学习模型，允许对植物进行分析和分类，以确定可能的疾病，从而促进早期发现，从而防止疾病的传播。这样,本文提出了一种Edge-AI设备，该设备包含必要的硬件和软件组件，用于从一组植物叶片图像中自动检测植物病害。这样,这项工作的主要目标是设计一种自主设备，允许检测可能的疾病，可以检测植物中的潜在疾病。这将通过捕获叶子的多个图像并实施数据融合技术来实现，以增强分类过程并提高其鲁棒性。已经进行了若干测试以确定该装置的使用显著增加了对可能的植物病害的分类响应的鲁棒性。
