• Journal of Biosystems Engineering
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• v.32 no.4
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• pp.256-262
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• 2007

Image processing systems have been used to measure the plant parameters such as size, shape and structure of plants. There are yet some limited applications for evaluating plant colors due to illumination conditions. This study was focused to present adaptive methods to analyze plant leaf color regardless of illumination conditions. Color patches attached on the calibration bars were selected to represent leaf colors of lettuces and to test a possibility of health monitoring of lettuces. Repeatability of assigning leaf colors to color patches was investigated by two-tailed t-test for paired comparison. It resulted that there were no differences of assignment histogram between two images of one lettuce that were acquired at different light conditions. It supported that use of the calibration bars proposed for leaf color analysis provided color constancy, which was one of the most important issues in a video color analysis. A health discrimination equation was developed to classify lettuces into one of two classes, SOUND group and POOR group, using the machine vision. The classification accuracy of the developed health discrimination equation was 80.8%, compared to farmers' decision. This study could provide a feasible method to develop a standard color chart for evaluating leaf colors of plants and plant health monitoring system using the machine vision.

• Journal of Internet of Things and Convergence
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• v.10 no.4
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• pp.43-53
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• 2024

Tomatoes are rich in nutrients like lycopene, β-carotene, and vitamin C. However, they often suffer from biological and environmental stressors, resulting in significant yield losses. Traditional manual plant health assessments are error-prone and inefficient for large-scale production. To address this need, we collected a comprehensive dataset covering the entire life span of tomato plants, annotated across 5 health states from 1 to 5. Our study introduces an Attention-Enhanced DS-ResNet architecture with Channel-wise attention and Grouped convolution, refined with new training techniques. Our model achieved an overall accuracy of 80.2% using 5-fold cross-validation, showcasing its robustness in precisely classifying the health states of tomato plants.

• Journal of Plant Biotechnology
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• v.49 no.2
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• pp.139-144
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• 2022

The genus Prunus (family: Rosaceae) consists of over 400 plant species and exhibits vast biodiversity worldwide. Given the wide distribution of this genus, its taxonomic classification is important. Anatomical characteristics are conserved and stable and can therefore be used as an important tool for the taxonomic characterization of plants. Therefore, this study aimed to assess and document the anatomical characteristics of the leaf, stem, and seed of P. africana using micrographs and photographs for possible use in the identification, quality control, and phylogenetic analysis of the species. The anatomical sections of a young stem revealed a cortex consisting of isodiametric parenchyma cells, druse crystals, primary vascular bundles, and pith. The mature stem bark majorly consisted of the rhytidome, with the periderm densely arranged in multiple layers; a cluster of stone cells; and sclerenchyma. The leaf sections were hypostomatic, with stomata sizes ranging from 18.90-(22.34)-26.90 × 15.41-(18.40)-21.22 ㎛. The leaf sections showed the presence of characteristic druse crystals, vascular bundles, and mesophyll layers. The pericarp contained the epicarp, mesocarp, and endocarp, with their thickness being approximately 350-400, 300-350, and 30-50 ㎛, respectively. In addition, it contained a seed testa with a thickness of approximately 50-60 ㎛. The morphological and anatomical characteristics observed in P. africana leaves, stems, and seeds in this study could serve as useful data for the taxonomic identification of this species.