Segmentation of FHB infection in wheat ear using super-resolution of UAV image and reception enrichment gate network

Abstract

Fusarium head blight (FHB) is one of the most serious wheat diseases and mainly infects the ear, affecting the yield and quality of wheat worldwide. Segmentation of FHB infection in wheat ear based on unmanned aerial vehicle (UAV) images is feasible and significant in ensuring timely control measures and maintaining food security. The high flight altitude of UAV allows for rapid image acquisition but results in blurred textures and details, and the variability of field environment leads to missed and false segmentation. To address these problems, we first executed the super-resolution (SR) of high-altitude UAV images, and then FHB infection was segmented using a deep gate network. Specifically, an SR network called hierarchical context aggregation network (HCAN) was developed to generate clear textures and detailed characteristics of wheat efficiently through the successive fusion of various contexts. HCAN was superior to the current state-of-the-art methods with a peak signal-to-noise ratio of 29.056 dB and a structural similarity index of 0.9142. Meanwhile, a reception enrichment gate network (REGN) was applied to segment FHB infection in wheat ear through the integration of dual-gate mechanism and multi-scale convolution. REGN gained superior results to those of other segmentation networks with a mean intersection over union of 77.93 %, mean pixel accuracy of 87.43 %, and mean Dice coefficient of 87.06 %. Indistinct edges, missed segmentation, and false segmentation were dramatically alleviated in high-density, overlapping, shaded and overexposed wheat because local and neighboring gate operations enhanced the representation and reception field, and multi-scale convolution could enrich the reception diversity. In sum, the proposed approach provided a reliable, efficient, and accurate determination of FHB infection in wheat on the basis of UAV images and could be extended to the analysis of other diseases or crops.

Plant phenotyping relevance

UAV画像からコムギ穂のFHB感染状態を抽出する超解像・セグメンテーション手法を開発し、性能評価しており、植物病害表現型の取得方法が中心である。

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