Deep Learning Predicts Rapid Over-softening and Shelf Life in Persimmon Fruits
デジタルデータあり（Japan Link Center）
すぐに読む
CiNii Research
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- 資料種別
- 記事
- 著者・編者
- Maria SuzukiKanae MasudaHideaki AsakumaKouki TakeshitaKohei BabaYasutaka KuboKoichiro UshijimaSeiichi UchidaTakashi Akagi
- 著者標目
- タイトル（掲載誌）
- The horticulture journal
- 巻号年月日等（掲載誌）
- 91(3):2022.7
- 掲載巻
- 91
- 掲載号
- 3
- 掲載ページ
- 408-415
- 掲載年月日（W3CDTF）
- 2022-07
- ISSN（掲載誌）
- 2189-0102
- ISSN-L（掲載誌）
- 2189-0102
- 出版事項（掲載誌）
- [Kyoto] : Japanese Society for Horticultural Science
- 出版地（国名コード）
- JP
- 本文の言語コード
- eng
- NDLC
- 対象利用者
- 一般
- 所蔵機関
- 国立国会図書館
- 請求記号
- Z78-A456
- 連携機関・データベース
- 国立国会図書館 : 国立国会図書館雑誌記事索引
- 書誌ID（NDLBibID）
- 032269816
- 整理区分コード
- 632
- 要約等
- <p>In contrast to the progress in the research on physiological disorders relating to shelf life in fruit crops, it has been difficult to non-destructively predict their occurrence. Recent high-tech instruments have gradually enabled non-destructive predictions for various disorders in some crops, while there are still issues in terms of efficiency and costs. Here, we propose application of a deep neural network (or simply deep learning) to simple RGB images to predict a severe fruit disorder in persimmon, rapid over-softening. With 1,080 RGB images of ‘Soshu’ persimmon fruits, three convolutional neural networks (CNN) were examined to predict rapid over-softened fruits with a binary classification and the date to fruit softening. All of the examined CNN models worked successfully for binary classification of the rapid over-softened fruits and the controls with > 80% accuracy using multiple criteria. Furthermore, the prediction values (or confidence) in the binary classification were correlated to the date to fruit softening. Although the features for classification by deep learning have been thought to be in a black box by conventional standards, recent feature visualization methods (or “explainable” deep learning) has allowed identification of the relevant regions in the original images. We applied Grad-CAM, Guided backpropagation, and layer-wise relevance propagation (LRP), to find early symptoms for CNNs classification of rapid over-softened fruits. The focus on the relevant regions tended to be on color unevenness on the surface of the fruit, especially in the peripheral regions. These results suggest that deep learning frameworks could potentially provide new insights into early physiological symptoms of which researchers are unaware.</p>
- DOI
- 10.2503/hortj.utd-323
- オンライン閲覧公開範囲
- インターネット公開
- 関連情報（URI）
- 参照
- Impact of gibberellic acid and prohydrojasmon treatments on fruit characteristics, carotenoid biosynthetic gene expression, and electrical properties in satsuma mandarin fruitIdentification of lineage‐specific <i>cis</i>–<i>trans</i> regulatory networks related to kiwifruit ripening initiationTranscriptomic Interpretation on Explainable AI-Guided Intuition Uncovers Premonitory Reactions of Disordering Fate in Persimmon FruitCollaboration with AI in Horticultural ScienceDevelopment of an AI-based Image Analysis System to Calculate the Visit Duration of a Green Blow Fly on a Strawberry Flower
- 参照
- Cultivar discrimination of litchi fruit images using deep learningExpression of genes encoding xyloglucan endotransglycosylase/hydrolase in ‘Saijo’ persimmon fruit during softening after deastringency treatmentHypoxia‐responsive <i><scp>ERF</scp>s</i> involved in postdeastringency softening of persimmon fruitExplaining Convolutional Neural Networks using Softmax Gradient Layer-wise Relevance PropagationProanthocyanidin biosynthesis of persimmon (Diospyros kaki Thunb.) fruitNon-destructive tests on the prediction of apple fruit flesh firmness and soluble solids content on tree and in shelf lifeThree-dimensional optical coherence tomography image denoising through multi-input fully-convolutional networksOlive-Fruit Variety Classification by Means of Image Processing and Convolutional Neural NetworksExplainable Deep Learning Reproduces a ‘Professional Eye’ on the Diagnosis of Internal Disorders in Persimmon FruitDeep Residual Learning for Image RecognitionDeep Learning for Plant Stress Phenotyping: Trends and Future PerspectivesInfluence of Time and Concentration of 1-MCP Application on the Shelf Life of Pear `La France' FruitRelationship between a Reduced Aroma Production and Lipid Metabolism of Apples after Long-term Controlled-atmosphere StorageMonitoring storage shelf life of tomato using electronic nose techniqueDeep learning image segmentation and extraction of blueberry fruit traits associated with harvestability and yieldDown-regulation of POLYGALACTURONASE1 alters firmness, tensile strength and water loss in apple (Malus x domestica) fruitDeepFruits: A Fruit Detection System Using Deep Neural NetworksRethinking the Inception Architecture for Computer VisionApplication of Deep Learning Architectures for Accurate and Rapid Detection of Internal Mechanical Damage of Blueberry Using Hyperspectral Transmittance DataOn Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance PropagationWater stress-induced ethylene in the calyx triggers autocatalytic ethylene production and fruit softening in ‘Tonewase’ persimmon grown in a heated plastic-houseOptimization of edible coating composition to retard strawberry fruit senescenceInheritance and effect on ripening of antisense polygalacturonase genes in transgenic tomatoesReduced chilling injury and delayed fruit ripening in tomatoes with modified atmosphere and humidity packagingDeep learningDeep Neural Networks Based Recognition of Plant Diseases by Leaf Image ClassificationXception: Deep Learning with Depthwise Separable Convolutionsカキ'西条'におけるストレス誘導エチレン生合成と果実軟化1-MCP処理による脱渋処理したカキ‘蓮台寺’果実の軟化防止カキ‘太秋’における収穫後の早期軟化果発生に及ぼすフジコナカイガラムシの影響音響振動法によるカキ‘早秋’の果肉評価と果肉硬度保持技術の開発カキ‘平核無’果実の軟化とエチレン生成および呼吸の関係Noninvasive Diagnosis of Seedless Fruit Using Deep Learning in PersimmonDeepFruits: A Fruit Detection System Using Deep Neural Networks
- 連携機関・データベース
- 国立情報学研究所 : CiNii Research
- 提供元機関・データベース
- Japan Link Center学術機関リポジトリデータベース雑誌記事索引データベースCrossref科学研究費助成事業データベースCrossrefCrossrefCrossrefCrossrefCrossref
- 書誌ID（NDLBibID）
- 032269816