Frontiers | Sustainable field-scale detection of walnut pests and diseases using deep transfer learning under changing-climatic conditions

for walnut disease detection the proposed model achieved training accuracy of 99.77%, validation accuracy of 98.68%, testing accuracy of 98.25%, with strong precision, recall, and f1-score of 98.67%. The proposed model can be integrated and deployed in real-world agricultural applications embedding AIoT for resource constraint devices for integrated pest and disease management (IPDM) in smart horticulture. This study also significantly contributes to advancing the UN’s Sustainable Development Goals (SDGs), notably SDG 1: No Poverty, SDG 2: Zero Hunger, SDG 3: Good Health and Well-being, SDG

本文转载自：Frontiers
原文链接：https://www.frontiersin.org/journals/horticulture/articles/10.3389/fhort.2026.1833509/full

本文由 XZ导师app 自动发布 | 发布时间：2026/10/24 17:10:21