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Horticulturae
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Pest Diagnosis and Control Strategies for Fruit and Vegetable Plants—Second...
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Pest Diagnosis and Control Strategies for Fruit and Vegetable Plants—Second Edition

A special issue of Horticulturae (ISSN 2311-7524). This special issue belongs to the section "Insect Pest Management".

Deadline for manuscript submissions: 25 December 2026 | Viewed by 3432

Special Issue Editors

Dr. Maja Čačija

E-Mail Website
Guest Editor
Department for Agricultural Zoology, Faculty of Agriculture, University of Zagreb, Svetosimunska 25, 10000 Zagreb, Croatia
Interests: wireworm fauna and population dynamics; molecular methods in entomology; biological pest control; soil fauna; insect rearing
Special Issues, Collections and Topics in MDPI journals
Dr. Ivan Juran

E-Mail Website
Guest Editor
Department for Agricultural Zoology, Faculty of Agriculture, University of Zagreb, Svetosimunska 25, 10000 Zagreb, Croatia
Interests: wireworm fauna and population dynamics; molecular methods in entomology; biological pest control; soil fauna; insect rearing
Special Issues, Collections and Topics in MDPI journals
Dr. Carmelo Peter Bonsignore

E-Mail Website
Guest Editor
Laboratorio di Entomologia ed Ecologia Applicata, Dipartimento PAU, Università Mediterranea di Reggio Calabria, 89124 Reggio Calabria, Italy
Interests: insect ecology; pest management; forest entomology; biological control; tritrophic interactions
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the tremendous success of the first edition of the Special Issue “Pest Diagnosis and Control Strategies for Fruit and Vegetable Plants”(https://www.mdpi.com/journal/horticulturae/special_issues/C32FM3V8NO ), a second edition is being launched.

Fruit and vegetable production is constantly threatened by insect pests, diseases and weeds, as well as new invasive species of harmful organisms. Any of these threats can restrict market access and food supply chains, and this has an impact on natural and agro ecosystems. The basis for protecting crops from pests today is Integrated Pest Management (IPM), which involves proper and timely pest diagnosis and pest-adapted control. Accurate and rapid pest detection and identification is the first step towards the use of effective control strategies, which can help growers avoid costly mistakes that lead to economic losses. Nowadays, various diagnostic tools ("decision support systems") are being developed that allow for fast and efficient identification of harmful organisms so that an accurate and timely choice of a specific pest control strategy can be implemented.

The purpose of this Special Issue, “Pest Diagnosis and Control Strategies for Fruit and Vegetable Plants—Second Edition”, is to present innovative studies, approaches, tools, and techniques that can be successfully used in pest diagnosis or as efficient control measures in fruit and vegetable production. These also include innovative articles on molecular and geometric morphometrics diagnostic tools, the implementation of artificial intelligence in pest detection, and any control strategy that can control pests in an environmentally friendly and cost-efficient manner.

Dr. Maja Čačija
Dr. Ivan Juran
Dr. Carmelo Peter Bonsignore
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

  • integrated pest management
  • diagnostic tools (decission support systems)
  • pest monitoring and detection
  • control measures

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

26 pages, 964 KB  
Article
Environment-Guided Multimodal Pest Detection and Risk Assessment in Fruit and Vegetable Production Systems
by Jiapeng Sun, Yucheng Peng, Zhimeng Zhang, Wenrui Xu, Boyuan Xi, Yuanying Zhang and Yihong Song
Horticulturae 2026, 12(4), 486; https://doi.org/10.3390/horticulturae12040486 - 16 Apr 2026
Viewed by 921
Abstract
Aimed at the practical challenge that pest occurrence in fruit and vegetable horticultural production exhibits strong environmental dependency, pronounced stage characteristics, and high sensitivity to control decision-making, a multimodal pest recognition and occurrence risk joint modeling method is proposed to address the limitation [...] Read more.
Aimed at the practical challenge that pest occurrence in fruit and vegetable horticultural production exhibits strong environmental dependency, pronounced stage characteristics, and high sensitivity to control decision-making, a multimodal pest recognition and occurrence risk joint modeling method is proposed to address the limitation that conventional intelligent plant protection systems focus primarily on pest identification while lacking risk discrimination capability. Within a unified network framework, pest visual information and environmental temporal data are integrated through the construction of an environment-guided representation learning mechanism, a recognition–risk joint optimization strategy, and a risk-aware decision representation modeling structure. In this manner, pest category recognition and occurrence risk evaluation are conducted simultaneously, thereby providing direct decision support for precision prevention and control in fruit and vegetable production. Systematic experimental evaluation is conducted based on multi-crop and multi-year field data collected from Wuyuan County, Bayannur City, Inner Mongolia. Overall comparative results demonstrate that an identification accuracy of 0.947, a precision of 0.936, and a recall of 0.924 are achieved on the test set, all of which significantly outperform mainstream visual detection models such as YOLOv8, DETR, and Mask R-CNN. In terms of detection performance, mAP@50 and mAP@75 reach 0.962 and 0.821, respectively, indicating stable localization and discrimination capability under complex backgrounds and dense small-target conditions. For the occurrence risk discrimination task, a risk accuracy of 0.887 is obtained, representing an improvement of approximately 4.5 percentage points compared with the simple multimodal feature concatenation method. Cross-crop, cross-site, and cross-year generalization experiments further show that risk accuracy remains above 0.84 with stable recognition performance under significant distribution shifts. Ablation studies verify the synergistic contributions of the proposed core modules to overall performance improvement. The results indicate that the proposed framework enables the transition from single recognition to risk-driven plant protection decision-making, providing a technically viable pathway for pest diagnosis and control strategy optimization in fruit and vegetable horticulture. Full article
(This article belongs to the Special Issue Pest Diagnosis and Control Strategies for Fruit and Vegetable Plants—Second Edition)
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15 pages, 2567 KB  
Article
Evaluation of the Population Growth Potential of Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) on Six Common Potato Cultivars in China
by Shu-Yan Yan, He-Sen Yang, Hong-Yu Gao, Feng-Zhi Deng, Gui-Fen Zhang, Chuan-Ren Li, Fang-Hao Wan, Wan-Xue Liu, Cong Huang and Yi-Bo Zhang
Horticulturae 2026, 12(1), 41; https://doi.org/10.3390/horticulturae12010041 - 28 Dec 2025
Viewed by 2049
Abstract
The South American tomato leaf miner, Tuta absoluta (Meyrick, 1917) (Syn.: Phthorimaea absoluta), is a pest of great economic importance worldwide. Although T. absoluta shows a strong preference for tomato, it can also attack potato, eggplant, and various wild solanaceous plants, thereby [...] Read more.
The South American tomato leaf miner, Tuta absoluta (Meyrick, 1917) (Syn.: Phthorimaea absoluta), is a pest of great economic importance worldwide. Although T. absoluta shows a strong preference for tomato, it can also attack potato, eggplant, and various wild solanaceous plants, thereby posing new challenges for pest control. To assess the adaptability of this pest to different potato varieties, an age-stage, two-sex life table method was used to determine the development, survival, reproduction, and key population parameters of the pest on six common potato varieties (Hezuo No. 88, Lishu No. 6, Weiyu No. 3, Zhongshu No. 5, Qingshu No. 9, and Qingshu No. 10) in China. The results showed that T. absoluta could complete its entire life cycle on all cultivars. However, key life history parameters varied significantly. On cultivars Qingshu No. 9 and Qingshu No. 10, the pest exhibited significantly prolonged preadult duration and total pre-oviposition period (TPOP), as well as reduced adult fecundity. In contrast, Hezuo No. 88 supported the highest intrinsic rate of increase (r) and net reproductive rate (R0). The 60-day population projections further highlighted this contrast, showing that the T. absoluta population on Hezuo No. 88 increased by a factor of 4.26 and 3.52 times compared to that on Qingshu No. 9 and Qingshu No. 10, respectively. We conclude that cultivars Qingshu No. 9 and Qingshu No. 10 exhibit antibiosis resistance against T. absoluta. This study not only provides a theoretical foundation and candidate materials for breeding pest-resistant potato varieties, but also establishes a basis for IPM strategies against T. absoluta that are founded on host resistance. Full article
(This article belongs to the Special Issue Pest Diagnosis and Control Strategies for Fruit and Vegetable Plants—Second Edition)
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