start-ver=1.4 cd-journal=joma no-vol=94 cd-vols= no-issue=1 article-no= start-page=64 end-page=72 dt-received= dt-revised= dt-accepted= dt-pub-year=2025 dt-pub=2025 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Development of an AI-based Image Analysis System to Calculate the Visit Duration of a Green Blow Fly on a Strawberry Flower en-subtitle= kn-subtitle= en-abstract= kn-abstract=Pollinator insects are required to pollinate flowers in the production of some fruits and vegetables, and strawberries fall into this category. However, the function of pollinators has not been clarified by quantitative metrics such as the duration of pollinator visits needed by flowers. Due to the long activity time of pollinators (approximately 10-h), it is not easy to observe the visitation characteristics manually. Therefore, we developed software for evaluating pollinator performance using two types of artificial intelligence (AI), YOLOv4, which is an object detection AI, and VGG16, which is an image classifier AI. In this study, we used Phaenicia sericata Meigen (green blow fly) as the strawberry pollinator. The software program can automatically estimate the visit duration of a fly on a flower from video clips. First, the position of the flower is identified using YOLO, and the identified location is cropped. Next, the cropped image is classified by VGG16 to determine if the fly is on the flower. Finally, the results are saved in CSV and HTML format. The program processed 10 h of video (collected from 07:00 h to 17:00 h) taken under actual growing conditions to estimate the visit durations of flies on flowers. The recognition accuracy was approximately 97%, with an average difference of 550 s. The software was run on a small computer board (the Jetson Nano), indicating that it can easily be used without a complicated AI configuration. This means that the software can be used immediately by distributing pre-configured disk images. When the software was run on the Jetson Nano, it took approximately 11 min to estimate one day of 2-h video. It is therefore clear that the visit duration of a fly on a flower can be estimated much faster than by manually checking videos. Furthermore, this system can estimate the visit durations of pollinators to other flowers by changing the YOLO and VGG16 model files. en-copyright= kn-copyright= en-aut-name=TaniguchiHiroki en-aut-sei=Taniguchi en-aut-mei=Hiroki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=TsukudaYuki en-aut-sei=Tsukuda en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=MotokiKo en-aut-sei=Motoki en-aut-mei=Ko kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=GotoTanjuro en-aut-sei=Goto en-aut-mei=Tanjuro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=YoshidaYuichi en-aut-sei=Yoshida en-aut-mei=Yuichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=YasubaKen-ichiro en-aut-sei=Yasuba en-aut-mei=Ken-ichiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= affil-num=1 en-affil=Graduate School of Environmental and Life Science, Okayama University kn-affil= affil-num=2 en-affil=School of Agriculture Okayama University kn-affil= affil-num=3 en-affil=Graduate School of Environmental and Life Science, Okayama University kn-affil= affil-num=4 en-affil=Graduate School of Environmental and Life Science, Okayama University kn-affil= affil-num=5 en-affil=Graduate School of Environmental and Life Science, Okayama University kn-affil= affil-num=6 en-affil=Graduate School of Environmental and Life Science, Okayama University kn-affil= en-keyword=deep learning kn-keyword=deep learning en-keyword=fly kn-keyword=fly en-keyword=microcomputer kn-keyword=microcomputer en-keyword=VGG16 kn-keyword=VGG16 en-keyword=YOLO kn-keyword=YOLO END start-ver=1.4 cd-journal=joma no-vol=93 cd-vols= no-issue=4 article-no= start-page=335 end-page=343 dt-received= dt-revised= dt-accepted= dt-pub-year=2024 dt-pub=2024 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Elucidation of Low-temperature Regulated Flavone Synthesis in Dahlia Variabilis and its Effects on Flower Color en-subtitle= kn-subtitle= en-abstract= kn-abstract=Dahlia (Dahlia variabilis) flower colors are diverse and are determined by the accumulation of flavonoids. Cultivars with dark red flowers accumulate more anthocyanins in their petals. Flower color changes such as color fading often occur in some cultivars. In this study, low minimum temperature regulated flower color fading and flavonoid synthesis in dahlia ‘Nessho’ were investigated. The pigment contents and expression levels of flavonoid biosynthesis genes were investigated in detail under several growing environments in which color fading occurs. Flavones accumulate more in color-faded orange flowers than in dark red ray florets. The expression analysis of the anthocyanin synthesis pathway genes indicated that the upregulation of flavone synthase (DvFNS) gene expression correlated with the high accumulation of flavones in color-faded petals. DvFNS expression was also detected in young leaves, and the expression level was higher in winter than in summer. Seasonal changes in DvFNS expression in young leaves significantly correlated with color fading in petals. The change in DvFNS expression in young unexpanded leaves of relatively high-sensitive plants was significantly higher than that of low-sensitive plants before and after treatment under inductive conditions. In conclusion, low-temperature-inducible changes in the flavonoid accumulation in petals was suggested to reflect a change in DvFNS expression occurring in the meristem prior to flower bud formation. This temporal DvFNS expression in young unexpanded leaves of ‘Nessho’ dahlia could be an insight for the selection and breeding of non-color fading plants. en-copyright= kn-copyright= en-aut-name=K. MuthamiaEdna en-aut-sei=K. Muthamia en-aut-mei=Edna kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=NaitoKoji en-aut-sei=Naito en-aut-mei=Koji kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=OkadaHiromasa en-aut-sei=Okada en-aut-mei=Hiromasa kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=KarasawaYukino en-aut-sei=Karasawa en-aut-mei=Yukino kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=KikumuraTokuyu en-aut-sei=Kikumura en-aut-mei=Tokuyu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=NaraTakuya en-aut-sei=Nara en-aut-mei=Takuya kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=HamauzuYasunori en-aut-sei=Hamauzu en-aut-mei=Yasunori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=MotokiKo en-aut-sei=Motoki en-aut-mei=Ko kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=YasubaKen-ichiro en-aut-sei=Yasuba en-aut-mei=Ken-ichiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= en-aut-name=YoshidaYuichi en-aut-sei=Yoshida en-aut-mei=Yuichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=10 ORCID= en-aut-name=KitamuraYoshikuni en-aut-sei=Kitamura en-aut-mei=Yoshikuni kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=11 ORCID= en-aut-name=GotoTanjuro en-aut-sei=Goto en-aut-mei=Tanjuro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=12 ORCID= affil-num=1 en-affil=Graduate School of Environmental and Life Science, Okayama University kn-affil= affil-num=2 en-affil=Graduate School of Environmental and Life Science, Okayama University kn-affil= affil-num=3 en-affil=Faculty of Agriculture, Shinshu University kn-affil= affil-num=4 en-affil=Faculty of Agriculture, Shinshu University kn-affil= affil-num=5 en-affil=Faculty of Agriculture, Shinshu University kn-affil= affil-num=6 en-affil=Faculty of Agriculture, Shinshu University kn-affil= affil-num=7 en-affil=Faculty of Agriculture, Shinshu University kn-affil= affil-num=8 en-affil=Graduate School of Environmental and Life Science, Okayama University kn-affil= affil-num=9 en-affil=Graduate School of Environmental and Life Science, Okayama University kn-affil= affil-num=10 en-affil=Graduate School of Environmental and Life Science, Okayama University kn-affil= affil-num=11 en-affil=Graduate School of Environmental and Life Science, Okayama University kn-affil= affil-num=12 en-affil=Graduate School of Environmental and Life Science, Okayama University kn-affil= en-keyword=anthocyanin kn-keyword=anthocyanin en-keyword=dahlia kn-keyword=dahlia en-keyword=flavone synthase kn-keyword=flavone synthase en-keyword=seasonal color fading kn-keyword=seasonal color fading en-keyword=young unexpanded leaves kn-keyword=young unexpanded leaves END start-ver=1.4 cd-journal=joma no-vol=91 cd-vols= no-issue=3 article-no= start-page= end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2022 dt-pub=2022 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Deep Learning Predicts Rapid Over-softening and Shelf Life in Persimmon Fruits en-subtitle= kn-subtitle= en-abstract= kn-abstract=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. en-copyright= kn-copyright= en-aut-name=SuzukiMaria en-aut-sei=Suzuki en-aut-mei=Maria kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=MasudaKanae en-aut-sei=Masuda en-aut-mei=Kanae kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=AsakumaHideaki en-aut-sei=Asakuma en-aut-mei=Hideaki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=TakeshitaKouki en-aut-sei=Takeshita en-aut-mei=Kouki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=BabaKohei en-aut-sei=Baba en-aut-mei=Kohei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=KuboYasutaka en-aut-sei=Kubo en-aut-mei=Yasutaka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=UshijimaKoichiro en-aut-sei=Ushijima en-aut-mei=Koichiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=UchidaSeiichi en-aut-sei=Uchida en-aut-mei=Seiichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=AkagiTakashi en-aut-sei=Akagi en-aut-mei=Takashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= affil-num=1 en-affil=Graduate School of Environmental and Life Science, Okayama University kn-affil= affil-num=2 en-affil=Graduate School of Environmental and Life Science, Okayama University kn-affil= affil-num=3 en-affil=Fukuoka Agriculture and Forestry Research Center kn-affil= affil-num=4 en-affil=Department of Advanced Information Technology, Kyushu University kn-affil= affil-num=5 en-affil=Department of Advanced Information Technology, Kyushu University kn-affil= affil-num=6 en-affil=Graduate School of Environmental and Life Science, Okayama University kn-affil= affil-num=7 en-affil=Graduate School of Environmental and Life Science, Okayama University kn-affil= affil-num=8 en-affil=Department of Advanced Information Technology, Kyushu University kn-affil= affil-num=9 en-affil=Graduate School of Environmental and Life Science, Okayama University kn-affil= END start-ver=1.4 cd-journal=joma no-vol=91 cd-vols= no-issue=1 article-no= start-page=58 end-page=67 dt-received= dt-revised= dt-accepted= dt-pub-year=2022 dt-pub=20220122 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Fasciation in Strawberry Floral Organs and Possible Implications for Floral Transition en-subtitle= kn-subtitle= en-abstract= kn-abstract=Fasciation in strawberry is characterized by an enlarged and flattened receptacle, clustering of flowers, and altered inflorescence architecture. However, the developmental process of fasciated flowers remains obscure. In this study, the fasciation incidence and developmental process in the primary fruit and inflorescence architecture were evaluated and compared for the non-susceptible cultivars, ‘Nyoho’ and ‘Sagahonoka’ and one of the most susceptible cultivars, ‘Ai-Berry’. The severity and frequency of flower and inflorescence fasciation was clearly greater in the vigorously growing large plants of ‘Ai-Berry’ compared to small plants and large plants of the other two cultivars. In ‘Ai-Berry’, the deformation of the large shoot apical meristem (SAM) into an oval shape was the initial symptom observed before and during floral transition. Such oval-shaped SAMs often differentiated two or more leaf primordia almost at the same time, which then developed into divided multiple vegetative SAMs before floral transition and linearly-fasciated SAMs during floral transition, respectively. The development of fasciation symptoms was observed after downregulation of FaTFL1. Although inflorescence or receptacle fasciation could be controlled when early and rapid floral induction was achieved by intermittent low-temperature treatment, severe fasciation was observed in late-flowered plants which were either not responsive or not subjected to this treatment. These results indicate that fasciation of floral organs may be triggered and develop during floral transition and that temperature fluctuations around boundary values between floral inhibition to induction may cause a half-finished or slowly processed floral transition and finally result in severe fasciation in vigorously growing ‘Ai-Berry’ plants. en-copyright= kn-copyright= en-aut-name=Thi CamNguyen en-aut-sei=Thi Cam en-aut-mei=Nguyen kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=SunagawaNaomichi en-aut-sei=Sunagawa en-aut-mei=Naomichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=SesumiMiho en-aut-sei=Sesumi en-aut-mei=Miho kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=KitamuraYoshikuni en-aut-sei=Kitamura en-aut-mei=Yoshikuni kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=TanakaYoshiyuki en-aut-sei=Tanaka en-aut-mei=Yoshiyuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=GotoTanjuro en-aut-sei=Goto en-aut-mei=Tanjuro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=YasubaKen-ichiro en-aut-sei=Yasuba en-aut-mei=Ken-ichiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=YoshidaYuichi en-aut-sei=Yoshida en-aut-mei=Yuichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= affil-num=1 en-affil=Graduate School of Environmental and Life Sciences, Okayama University kn-affil= affil-num=2 en-affil=Graduate School of Environmental and Life Sciences, Okayama University kn-affil= affil-num=3 en-affil=Graduate School of Environmental and Life Sciences, Okayama University kn-affil= affil-num=4 en-affil=Graduate School of Environmental and Life Sciences, Okayama University kn-affil= affil-num=5 en-affil=Graduate School of Agriculture, Kyoto University kn-affil= affil-num=6 en-affil=Graduate School of Environmental and Life Sciences, Okayama University kn-affil= affil-num=7 en-affil=Graduate School of Environmental and Life Sciences, Okayama University kn-affil= affil-num=8 en-affil=Graduate School of Environmental and Life Sciences, Okayama University kn-affil= END start-ver=1.4 cd-journal=joma no-vol=88 cd-vols= no-issue=1 article-no= start-page=1 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2019 dt-pub=20190131 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Publication of the first special issue of The Horticulture Journal Preface en-subtitle= kn-subtitle= en-abstract= kn-abstract= en-copyright= kn-copyright= en-aut-name=YoshidaYuichi en-aut-sei=Yoshida en-aut-mei=Yuichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= affil-num=1 en-affil=Okayama University kn-affil= END