利用递归神经网络对橄榄叶片病害进行图像分析和检测

Mohsin R. Kareem
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引用次数: 0

摘要

DL 的广泛应用提高了学术界对图像识别方法的兴趣,使自动图像分类和植物病害检测等应用成为可能。摩洛哥是世界上最大的橄榄生产国。植物健康可能会受到疾病的危害,从而影响其生长发育。影响橄榄叶片的许多疾病都特别针对作物的生长速度。这项研究的目的是创建深度 RNN,利用从各种渠道收集到的叶片图像(病害注释:孔雀眼落在橄榄树上、澳大利亚橄榄害虫、疾病和紊乱现场指南)来识别橄榄树的病害。因此,该技术是最佳的 RNN 模型,可用于进一步的应用中,以增强对橄榄叶和其他植物叶片的诊断测量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image Analysis and Detection of Olive Leaf Diseases Using Recurrent Neural Networks
The widespread adoption of DL has led to a rise in academic interest in image recognition approaches, enabling applications such as automated image classification and the detection of plant diseases. The world's largest producer of olives is Morocco. Plant health might be harmed by illnesses, which therefore affects its development. Numerous illnesses affecting olive leaves specifically target crop growth rate. The objective of this research is to create deep RNNs to identify olive plant illnesses using a collection of leaf images, collected from various sources (Disease note The peacock eye falls on olive trees, Field Guide to Olive Pests, Diseases and Disorders in Australia. Thus, this technique is the best RNN model and is employed in further applications to enhance diagnostic measurements regarding olive leaves and other plant leaves.
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