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Horticulturae
Special Issues
Recent Advances in Horticultural Practices for Strawberries and Other Small...
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Recent Advances in Horticultural Practices for Strawberries and Other Small Fruit Crops

A special issue of Horticulturae (ISSN 2311-7524). This special issue belongs to the section "Fruit Production Systems".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 15197

Special Issue Editors

Prof. Dr. Yushan Qiao

E-Mail Website
Guest Editor
Laboratory of Fruit Crop Biotechnology, College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China
Interests: strawberries; Fragaria germplasm resources; molecular biology of fruit crops; protected cultivation of fruit crops; cultivation techniques; biofumigation; botanicals
Prof. Dr. Zhihong Zhang

E-Mail Website
Guest Editor
College of Horticulture, Shenyang Agricultural University, Shenyang 110866, Liaoning, China
Interests: strawberries; fruit quality and tolerance to stresses; breeding; cultivation techniques
Dr. Jiyu Zhang

E-Mail Website
Guest Editor
Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China
Interests: kiwifruit; genetic breeding; flower bud differentiation; flower and fruit management; fruit development and stress molecular biology

Special Issue Information

Dear Colleagues,

Strawberries and other small fruits, such as brambles (raspberries and blackberries), blueberries, currants, gooseberries, seaberries, mulberries, grapes and kiwifruit, etc., which are rich in nutrients and have high economic value, could be made by commercial or backyard cultivations. Small fruits are produced on small perennial woody or herbaceous plants. These fruits are small but play irreplaceable roles in people’s daily life. So, more and more horticulturalists are focusing on the research and development of horticulture practices of high-quality and laborsaving/mechanized production of small fruit crops.

This Special Issue aims to present new crop production/cultivation techniques, including all aspects of small fruit production, such as seedlings, soils, planting, pruning and training, flower bud formation, irrigation and drainage, manures and fertilizers, the control of pests, diseases and weeds, harvesting and marketing, and costs of production, etc. In particular, an artificial suitable plant production environment (protected cultivation and plant factory), biological agriculture (biological pesticides, biofertilizers, etc.), intelligent agriculture, and soil farming to maintain soil vitality were applied in horticultural production.

We invite horticulturalists to contribute both original research articles and reviews to this Special Issue and to share your results with the community of researchers, students, technicians, strawberries and small fruit growers.

Prof. Dr. Yushan Qiao
Prof. Dr. Zhihong Zhang
Dr. Jiyu Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Horticulturae is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • strawberries
  • small fruit crops
  • biological horticulture (excluding molecular breeding)
  • biocontrol
  • protected culture (including but are not limited soilless and hydroponic culture)
  • plant factory
  • sustainable/precision horticulture
  • advanced/integrated production systems
  • artificial intelligence and robotics

Published Papers (3 papers)

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Research

17 pages, 9155 KiB  
Article
Physiological Disorder Diagnosis of Plant Leaves Based on Full-Spectrum Hyperspectral Images with Convolutional Neural Network
by Myongkyoon Yang
Horticulturae 2022, 8(9), 854; https://doi.org/10.3390/horticulturae8090854 - 19 Sep 2022
Cited by 4 | Viewed by 2283
Abstract
The prediction and early detection of physiological disorders based on the nutritional conditions and stress of plants are extremely vital for the growth and production of crops. High-throughput phenotyping is an effective nondestructive method to understand this, and numerous studies are being conducted [...] Read more.
The prediction and early detection of physiological disorders based on the nutritional conditions and stress of plants are extremely vital for the growth and production of crops. High-throughput phenotyping is an effective nondestructive method to understand this, and numerous studies are being conducted with the development of convergence technology. This study analyzes physiological disorders in plant leaves using hyperspectral images and deep learning algorithms. Data on seven classes for various physiological disorders, including normal, prediction, and the appearance of symptom, were obtained for strawberries subjected to artificial treatment. The acquired hyperspectral images were used as input for a convolutional neural network algorithm without spectroscopic preprocessing. To determine the optimal model, several hyperparameter tuning and optimizer selection processes were performed. The Adam optimizer exhibited the best performance with an F1 score of ≥0.95. Moreover, the RMSProp optimizer exhibited slightly similar performance, confirming the potential for performance improvement. Thus, the novel possibility of utilizing hyperspectral images and deep learning algorithms for nondestructive and accurate analysis of the physiological disorders of plants was shown. Full article
(This article belongs to the Special Issue Recent Advances in Horticultural Practices for Strawberries and Other Small Fruit Crops)
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17 pages, 70548 KiB  
Article
SwinGD: A Robust Grape Bunch Detection Model Based on Swin Transformer in Complex Vineyard Environment
by Jinhai Wang, Zongyin Zhang, Lufeng Luo, Wenbo Zhu, Jianwen Chen and Wei Wang
Horticulturae 2021, 7(11), 492; https://doi.org/10.3390/horticulturae7110492 - 12 Nov 2021
Cited by 33 | Viewed by 4707
Abstract
Accurate recognition of fruits in the orchard is an important step for robot picking in the natural environment, since many CNN models have a low recognition rate when dealing with irregularly shaped and very dense fruits, such as a grape bunch. It is [...] Read more.
Accurate recognition of fruits in the orchard is an important step for robot picking in the natural environment, since many CNN models have a low recognition rate when dealing with irregularly shaped and very dense fruits, such as a grape bunch. It is a new trend to use a transformer structure and apply it to a computer vision domain for image processing. This paper provides Swin Transformer and DETR models to achieve grape bunch detection. Additionally, they are compared with traditional CNN models, such as Faster-RCNN, SSD, and YOLO. In addition, the optimal number of stages for a Swin Transformer through experiments is selected. Furthermore, the latest YOLOX model is also used to make a comparison with the Swin Transformer, and the experimental results show that YOLOX has higher accuracy and better detection effect. The above models are trained under red grape datasets collected under natural light. In addition, the dataset is expanded through image data augmentation to achieve a better training effect. After 200 epochs of training, SwinGD obtained an exciting mAP value of 94% when IoU = 0.5. In case of overexposure, overdarkness, and occlusion, SwinGD can recognize more accurately and robustly compared with other models. At the same time, SwinGD still has a better effect when dealing with dense grape bunches. Furthermore, 100 pictures of grapes containing 655 grape bunches are downloaded from Baidu pictures to detect the effect. The Swin Transformer has an accuracy of 91.5%. In order to verify the universality of SwinGD, we conducted a test under green grape images. The experimental results show that SwinGD has a good effect in practical application. The success of SwinGD provides a new solution for precision harvesting in agriculture. Full article
(This article belongs to the Special Issue Recent Advances in Horticultural Practices for Strawberries and Other Small Fruit Crops)
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14 pages, 1121 KiB  
Article
The Effect of Organic, Inorganic Fertilizers and Their Combinations on Fruit Quality Parameters in Strawberry
by Neslihan Kilic, Aysegul Burgut, Muhammet Ali Gündesli, Gozde Nogay, Sezai Ercisli, Nesibe Ebru Kafkas, Halina Ekiert, Hosam O. Elansary and Agnieszka Szopa
Horticulturae 2021, 7(10), 354; https://doi.org/10.3390/horticulturae7100354 - 02 Oct 2021
Cited by 26 | Viewed by 6281
Abstract
Strawberry (Fragaria × ananassa Duch.) is widely grown and highly appreciated by consumers around the world for its delicious, soft, and highly nutritious fruits. Turkey is one of the most important strawberry producers in the world. Strawberry cultivation in Turkey typically involves [...] Read more.
Strawberry (Fragaria × ananassa Duch.) is widely grown and highly appreciated by consumers around the world for its delicious, soft, and highly nutritious fruits. Turkey is one of the most important strawberry producers in the world. Strawberry cultivation in Turkey typically involves the use of chemical fertilizers and more recently organic and organic + chemical fertilizers have been started to use in commercial production to produce healthier fruits. Therefore, in this study, we investigated the effect of organic, chemical, and organic + chemical fertilizer treatments in strawberry (cvs. ‘Albion’, ‘San Andreas’ and ‘Monterey’) fruit quality parameters including fruit color (L*, a*, b*, C and h°) parameters, soluble solids content, total acidity, fruit firmness, vitamin C, specific sugars and organic acids. Results showed that in particular fruit color parameters, soluble solid content (SSC), total acidity, fruit firmness, and vitamin C (L-Ascorbic acid) in fruits of three strawberry cultivars were significantly affected by different fertilizer applications (p < 0.05). Compared with conventional chemical fertilizer treatment, the organic fertilizer treatment produced fruit with significantly higher contents of SSC and glucose but decreased fruit firmness and vitamin C. Organic fertilizer also gave more intense colored strawberry fruits with high Chroma values (47,948 in organic fertilizer application and 39,644 and 39,931 in organic + chemical fertilizer and chemical fertilizer, respectively). Citric acid was identified to be the predominant organic acid in strawberry fruits but treatments were found insignificant on citric acid content. Full article
(This article belongs to the Special Issue Recent Advances in Horticultural Practices for Strawberries and Other Small Fruit Crops)
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