外文摘要：CONTEXT: Recently, smart agriculture has become an essential part of modern agriculture approaches from tillage, via plant seeding and grow support to their collection. With modern technologies, farmers can use substances like pesticides, herbicides, or fertilizers at precise dosages or to identify places on a field with specific production rates. OBJECTIVE: The main objective of this study is to introduce a novel and a unique aerial image dataset of various fields acquired by UAV containing crops/weeds in the early phenophases captured in two different resolutions (2 mm and 7 mm per pixel). Secondly, the best super -resolution technique for high -resolution images, substitution with lower resolution is explored. METHODS: For data acquisition, we employed DJI Matrice 600 equipped with a full -frame Sony Alpha A7R IV285 image sensor. Data were captured at flight heights of 26 and 95 m from 4 different fields in Central Europe. In addition, we proposed a methodology focused on the selection of an appropriate super -resolution method to enhance low -resolution aerial images to obtain better accuracy of crop/weed detection. As a baseline crop/weed detector for super -resolution effect evaluation, YOLOv5 architecture was used. Next, we explored the performance of several super -resolution models (U -Net++, ESRGAN, SwinIR), and fine-tuned the best -performed one. RESULTS AND CONCLUSIONS: We present the new dataset named SPAGRI-AI: a novel unique dataset of aerial images for super -resolution experiments in smart precision agriculture. The dataset contains 27,638 aerial images (1024 x 1024 px) and additionally, it contains a subset of 2014 labeled images with 45,548 bounding boxes
外文关键词：convolutional neural networks;Smart agriculture;deep-learning;Image super-resolution;Crop and weed
作者：Kovac, Daniel;Jonak, Martin;Mucha, Jan;Jezek, Stepan;Cziria, Kornel
作者单位：Brno Univ Technol;Skymaps Sro
期刊名称：AGRICULTURAL SYSTEMS
期刊影响因子：0.0
出版年份：2024
出版刊次：216
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台