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Horticulturae
Special Issues
Sustainable Management of the Mechanization of Works for Horticultural Crops
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Sustainable Management of the Mechanization of Works for Horticultural Crops

A special issue of Horticulturae (ISSN 2311-7524). This special issue belongs to the section "Protected Culture".

Deadline for manuscript submissions: 20 May 2026 | Viewed by 3379

Special Issue Editors

Prof. Dr. Ioan Ţenu

E-Mail Website
Guest Editor
Department of Pedotechnics, "Ion Ionescu de la Brad" University of Life Sciences from Iasi, 700490 Iasi, Romania
Interests: machinery and equipment for food industry; agricultural machinery; tribology with applications in machines and installations for agriculture and food
Special Issues, Collections and Topics in MDPI journals
Dr. Nicolae Valentin Vlăduţ

E-Mail Website
Guest Editor
Development for Machines and Installations Designed to Agriculture and Food Industry-INMA, National Institute of Research, Bucharest, Romania
Interests: agricultural machinery

Special Issue Information

Dear Colleagues,

Horticulture is launching a multidisciplinary Special Issue on "Sustainable Management of the Mechanization of Works for Horticultural Crops", inviting researchers, experts, and specialists from research institutions, universities, professional organizations, and enterprises to publish their scientific and original achievements. The aim is to generate a conducive framework and an academic community able to contribute to the increased performance of machinery systems used to carry out agricultural works under scientific conditions.

It is unanimously recognized that today's horticultural technological processes are energy-intensive and significantly damage the environment, leading to a search for technical solutions and alternative working methods which allow energy consumption optimization and a reduction in the negative impact on natural resources (water, soil, and air), at the same time diminishing the impact of climate change, which, in recent times, has affected the hydrological cycle’s intensity. This is why manufacturers of agricultural machinery are investing heavily in research and development to develop new products, equipped with hydraulic, electro-technical, electronic, and, more recently, computers and process software capable of implementing "Precision Farming", "Smart Farming", and "Agriculture 4.0", among others.

Prof. Dr. Ioan Ţenu
Dr. Nicolae Valentin Vlăduţ
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 250 words) can be sent to the Editorial Office for assessment.

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

  • mechanization of horticultural works
  • machinery
  • equipment sustainable management
  • operation
  • horticultural crops
  • precision agriculture
  • conservative and sustainable horticulture

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Published Papers (3 papers)

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Research

26 pages, 3362 KB  
Article
UAS-Based Spectral and Phenological Modeling for Sustainable Mechanization and Nutrient Management in Horticultural Crops
by Alexis Suero, Emmanuel Torres-Quezada, Lorena López, Mark Reiter, Andre Biscaia and Fernando Fuentes-Peñailillo
Horticulturae 2025, 11(12), 1451; https://doi.org/10.3390/horticulturae11121451 - 30 Nov 2025
Viewed by 204
Abstract
Potatoes are an economically important crop in Virginia, USA, where growers must balance planting dates, nitrogen (N) management, and variable crop prices. Early planting exposes crops to low temperatures that limit growth, whereas late planting increases pest pressure and nutrient inefficiency. This study [...] Read more.
Potatoes are an economically important crop in Virginia, USA, where growers must balance planting dates, nitrogen (N) management, and variable crop prices. Early planting exposes crops to low temperatures that limit growth, whereas late planting increases pest pressure and nutrient inefficiency. This study evaluated the effects of planting dates, N rates, and application timing on potato growth, yield, and pest incidence. We also assessed whether soil physicochemical properties could predict the presence of wireworms and plant-parasitic nematodes (PPNs) using complementary on-farm samples collected across Eastern Virginia between March and July 2023. Three planting dates (early-March, late-March, and early-April) were combined with five N rates (0, 146, 180, 213, and 247 kg N·ha−1) under early- and late-application regimes. We collected data on plant emergence, flowering time, soil nitrate, biomass, tuber yield, pest damage, and UAS-derived metrics. Results showed that late-March planting with 180 kg N·ha−1 achieved the highest gross profit while maintaining competitive yields (25.06 Mg·ha−1), representing 24% and 6% improvements over traditional practices, respectively. Early-April planting produced the largest tubers, with a mean tuber weight 19% higher than the other planting dates. The Normalized Difference Red Edge Index (NDRE) was strongly correlated with N content in plant tissue (R2 = 0.81; r ≈ 0.90), and UAS-derived plant area accurately predicted tuber yield 4–6 weeks before harvest (R2 = 0.75). Wireworm damage was significantly higher in early-March plantings due to delayed insecticide application, while soil nitrate concentration and percent H saturation were identified as key predictors of wireworm presence. Although less effectively modeled due to limited sample size, PPN occurrence was influenced by potassium saturation and soil pH. Aligning planting dates and nitrogen applications with crop phenology, using growing degree days (GDD), enhanced nitrogen management, and yield prediction. Full article
(This article belongs to the Special Issue Sustainable Management of the Mechanization of Works for Horticultural Crops)
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14 pages, 1735 KB  
Article
Maturity Classification of Blueberry Fruit Using YOLO and Vision Transformer for Agricultural Assistance
by Ikuma Esaki, Satoshi Noma, Takuya Ban, Rebeka Sultana and Ikuko Shimizu
Horticulturae 2025, 11(10), 1272; https://doi.org/10.3390/horticulturae11101272 - 21 Oct 2025
Cited by 1 | Viewed by 947
Abstract
This paper proposes a method for classifying the maturity levels of blueberry fruit from camera images as part of a cultivation support system. Following the five-stage maturity classification, the proposed approach first detects individual blueberry regions in an image and subsequently classifies each [...] Read more.
This paper proposes a method for classifying the maturity levels of blueberry fruit from camera images as part of a cultivation support system. Following the five-stage maturity classification, the proposed approach first detects individual blueberry regions in an image and subsequently classifies each region into one of the five levels. The method leverages a Transformer-based model to extract features from local fruit regions that include contextual background, enabling the learning of spatial relationships both within and beyond the fruit boundaries. A dedicated dataset was constructed by capturing images of blueberry fruits alongside a color chart representing maturity levels. Experimental evaluations involving multiple deep learning models under three training–testing configurations demonstrate the effectiveness of the proposed method, achieving an average classification accuracy of 93.7%. Full article
(This article belongs to the Special Issue Sustainable Management of the Mechanization of Works for Horticultural Crops)
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14 pages, 6235 KB  
Article
Physical Ripening Indices Improve the Assessment of Mechanical Harvesting Time for Olive Cultivars Resistant to Xylella fastidiosa subsp. pauca
by Simone Pietro Garofalo, Francesco Maldera, Francesco Nicolì, Gaetano Alessandro Vivaldi and Salvatore Camposeo
Horticulturae 2024, 10(10), 1108; https://doi.org/10.3390/horticulturae10101108 - 18 Oct 2024
Cited by 2 | Viewed by 1568
Abstract
Xylella fastidiosa subsp. pauca (Xfp) is a significant threat to Mediterranean agriculture, particularly impacting olive trees in southern Italy, causing Olive Quick Decline Syndrome. Resistant olive cultivars, such as ‘Leccino’ and ‘Fs-17’, have been identified as alternatives to restore the oliviculture [...] Read more.
Xylella fastidiosa subsp. pauca (Xfp) is a significant threat to Mediterranean agriculture, particularly impacting olive trees in southern Italy, causing Olive Quick Decline Syndrome. Resistant olive cultivars, such as ‘Leccino’ and ‘Fs-17’, have been identified as alternatives to restore the oliviculture within the infected areas. ‘Frantoio’ and ‘Cipressino’ are included in ongoing studies on genetic resistance to Xfp. The mechanization of olive harvesting is essential for reducing production costs in the olive oil sector. Two systems, trunk shakers and over-the-row machines, are used depending on the tree density and canopy structure, with super-high-density systems offering advantages in terms of cost and efficiency. This study investigates the feasibility of using simple and non-destructive indices to assess the optimal mechanical harvesting time. Different physical ripening indices, including detachment force, fresh weight, pigmentation, and firmness, were measured on four olive cultivars (‘Fs-17’, ‘Leccino’, ‘Frantoio’, ‘Cipressino’) in southern Italy over two years. The study found that the pigmentation index had a strong relationship with the detachment index, particularly for ‘Fs-17’, and ‘Leccino’, providing a reliable non-destructive measure for optimal harvesting time. The results indicate that the optimal harvesting times for mechanical harvesting are early September for ‘Cipressino’, early October for ‘Fs-17’, and mid-October for ‘Frantoio’ and ‘Leccino’. Full article
(This article belongs to the Special Issue Sustainable Management of the Mechanization of Works for Horticultural Crops)
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