With the rapid increase in the world’s population, there is an ever-growing need for a sustainable food supply. Agriculture is one of the pillars for worldwide food provisioning, with fruits and vegetables being essential for a healthy diet. However, in the last few years the worldwide dispersion of virulent plant pests and diseases has caused significant decreases in the yield and quality of crops, in particular fruit, cereal and vegetables. Climate change and the intensification of global trade flows further accentuate the issue. Integrated Pest Management (IPM) is an approach to pest control that aims at maintaining pest insects at tolerable levels, keeping pest populations below an economic injury level. Under these circumstances, the early identification of pests and diseases becomes crucial. In this work, we present the first step towards a fully fledged, semantically enhanced decision support system for IPM. The ultimate goal is to build a complete agricultural knowledge base by gathering data from multiple, heterogeneous sources and to develop a system to assist farmers in decision making concerning the control of pests and diseases. The pest classifier framework has been evaluated in a simulated environment, obtaining an aggregated accuracy of 98.8%.

中文翻译：

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基于知识的农作物病虫害识别系统

随着世界人口的迅速增加，对可持续粮食供应的需求日益增长。农业是全球粮食供应的支柱之一，水果和蔬菜对于健康饮食至关重要。然而，在过去的几年中，世界范围内有毒的植物病虫害的扩散已导致农作物，特别是水果，谷物和蔬菜的产量和质量显着下降。气候变化和全球贸易流量的加剧进一步加剧了这一问题。病虫害综合防治（IPM）是一种有害生物控制方法，旨在将有害生物保持在可容忍的水平，使有害生物种群保持在经济损害水平以下。在这种情况下，病虫害的尽早发现变得至关重要。在这项工作中，我们提出了迈向IPM完整，语义增强的决策支持系统的第一步。最终目标是通过收集来自多个不同来源的数据来建立一个完整的农业知识库，并开发一个系统来帮助农民做出有关病虫害控制的决策。在模拟环境中对害虫分类器框架进行了评估，获得了总计98.8％的准确性。