ArticleFrontiers in plant science2022
Detection and counting of pigment glands in cotton leaves using improved U-Net.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed, 2 citations in OpenAlex.
- TriAttnNet based deep learning model for automated cotton pest detection and disease classification.Scientific reports · 2026Article
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5 authors at 1 institution in 1 country.
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Abstract
Gossypol, as an important oil and raw material for feed, is mainly produced by cotton pigment gland, and has a wide range of applications in the fields of pharmaceutics, agriculture and industry. Accurate knowledge of the distribution of pigment gland in cotton leaves is important for estimating gossypol content. However, pigment glands are extremely small and densely distributed, manual counting is laborious and time-consuming, and difficult to count quickly and accurately. It is thus necessary to design a fast and accurate gland counting method. In this paper, the machine vision imaging technology is used to establish an image acquisition platform to obtain cotton leaf images, and a network structure is proposed based on deep learning, named as Interpolation-pooling net, to segment the pigment glands in the cotton leaf images. The network adopts the structure of first interpolation and then pooling, which is more conducive to the extraction of pigment gland features. The accuracy of segmentation of the model in cotton leaf image set is 96.7%, and the mIoU (Mean Intersection over Union), Recall, Precision and F1-score is 0.8181, 0.8004, 0.8004 and 0.8004 respectively. In addition, the number of pigment glands in cotton leaves of three different densities was measured. Compared with manual measurements, the square of the correlation coefficient (
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