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Artificial Intelligence in Horticulture Production

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

Deadline for manuscript submissions: closed (20 December 2025) | Viewed by 14796

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Special Issue Editors

Dr. Md Parvez Islam

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Guest Editor

Faculty of Agriculture and The United Graduate School of Agricultural Sciences, Ehime University, Matsuyama 790-8566, Ehime, Japan
Interests: horticulture; image processing; AI
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Kenji Hatou

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Guest Editor

Dr. Xudong Sun

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Guest Editor

School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
Interests: fruit optimal harvest date estimation; fruit quality assessment using spectroscopy and image
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Today, the agricultural sector faces huge challenges due to local and global ecological and political factors. However, to feed a growing world population, new approaches are needed to optimize resource allocation for precision agriculture, predictable production planning to ensure food security, and autonomous solutions for harvesting, processing and marketing of agricultural products. In this context, data-driven AI offers a wide range of application areas such as crop monitoring to detect diseases, nutrient deficiencies, pests, yield prediction, and price prediction. The papers in this Special Issue on “Artificial Intelligence in Horticulture Production” will focus on basic and applied research targeting AI in all stages of agriculture. This Special Issue will cover a number of topics of interest, including but not limited to:

AI for smart farming and agriculture;
AI-assisted automation;
AI-assisted real-time IoT data analytics;
Computer vision in agriculture;
Spatial AI-based agricultural robotics;
AI-based soil and plant nutrient analysis;
AI-based crop monitoring;
AI-assisted intelligent irrigation for agriculture;
AI-assisted phenotyping and genotyping;
AI-assisted livestock health monitoring;
AI in food supply chain;
AI-assisted predictive analytics for agriculture;
DL for managing security in IoT data processing;
DL for IoT attack detection and prevention.

Dr. Md Parvez Islam
Prof. Dr. Kenji Hatou
Dr. Xudong Sun
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.

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Keywords

smart farming
automation IoT data
computer vision
agricultural robotics
crop monitoring
intelligent irrigation
health monitoring
food supply chain
phenotyping and genotyping

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Published Papers (8 papers)

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Research

28 pages, 1396 KB

Open AccessArticle

Environmental–Visual Fusion for Proactive Tomato Late Blight Management in Protected Horticulture

by Puxing Gao, Peigen Yang, Tangji Ke, Saiwei Wang, Yulong Wang, Fengman Xu and Yihong Song

Horticulturae 2026, 12(3), 299; https://doi.org/10.3390/horticulturae12030299 - 3 Mar 2026

Viewed by 670

Abstract

In protected horticultural production, tomato late blight shows strong environmental inducibility, with a short latent period, rapid risk accumulation, and a limited control window, which challenges conventional post-event disease monitoring. To address this, a tomato late blight risk perception and predictive control approach for protected production is proposed, integrating deep temporal modeling of environmental factors, visual symptom perception, and risk-driven greenhouse control to enable prospective assessment and proactive intervention. Based on disease mechanisms and real greenhouse conditions, an artificial intelligence (AI) framework covering perception, prediction, and regulation is constructed, moving beyond reliance on visible symptoms alone. Long-term evolution of key variables, including temperature, air humidity, leaf wetness, and light intensity, is modeled using deep temporal networks, while early weak lesions and subtle texture changes are captured by visual models. Cross-modal fusion in a unified risk space generates continuous risk scores to drive greenhouse regulation. Experiments on a multimodal dataset from a real greenhouse in Bayannur, Inner Mongolia, show that the proposed method outperforms vision-based and environment-based baselines in recognition and risk prediction. It achieves about 0.95 accuracy, 0.94 F1-score, and over 0.97 area under the receiver operating characteristic curve (AUC), while providing more than 20 h of early warning before disease onset. In environmental modeling, the deep temporal model consistently surpasses threshold-based methods, logistic regression, and long short-term memory/gated recurrent unit (LSTM/GRU) baselines in risk lead time, false alert rate, and prediction stability. Full article

(This article belongs to the Special Issue Artificial Intelligence in Horticulture Production)

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23 pages, 1259 KB

Open AccessArticle

Semantic Alignment and Knowledge Injection for Cross-Modal Reasoning in Intelligent Horticultural Decision Support Systems

by Yuhan Cao, Yawen Zhu, Hanwen Zhang, Yuxuan Jiang, Ke Chen, Haoran Tang, Zhewei Wang and Yihong Song

Horticulturae 2026, 12(1), 23; https://doi.org/10.3390/horticulturae12010023 - 25 Dec 2025

Viewed by 728

Abstract

This study was conducted to address the demand for interpretable intelligent recognition of fruit tree diseases in smart horticultural environments. A KAD-Former framework integrating an agricultural knowledge graph with a visual Transformer was proposed and systematically validated through extensive cross-regional, multi-variety, and multi-disease experiments. The primary objective of this work was to overcome the limitations of conventional deep models, including insufficient interpretability, unstable recognition of weak disease features, and poor cross-regional generalization. In the experimental evaluation, the model achieved significant advantages across multiple representative tasks: in the overall performance comparison, KAD-Former reached an accuracy of

0.946

, an F1-score of

0.933

, and a mAP of

0.938

, outperforming classical models such as ResNet50, EfficientNet, and Swin-T. In the cross-regional generalization assessment, a DGS of

0.933

was obtained, notably surpassing competing models. In terms of explainability consistency, a Consistency@5 score of

0.826

indicated strong alignment between the model’s attention regions and expert annotations. The ablation experiments further demonstrated that the three core modules—AKG (agricultural knowledge graph), SAM (semantic alignment module), and KGA (knowledge-guided attention)—each contributed substantially to final performance, with the complete model exhibiting the best results. These findings collectively demonstrate the comprehensive advantages of KAD-Former in disease classification, symptom localization, model interpretability, and cross-domain transfer. The proposed method not only achieved state-of-the-art performance in pure visual tasks but also advanced knowledge-enhanced and interpretable reasoning by emulating the diagnostic logic employed by agricultural experts in real orchard scenarios. Through the integration of the agricultural knowledge graph, semantic alignment, and knowledge-guided attention, the model maintained stable performance under challenging conditions such as complex illumination, background noise, and weak lesion features, while exhibiting strong robustness in cross-region and cross-variety transfer tests. Furthermore, the experimental results indicated that the approach enhanced fine-grained recognition capabilities for various fruit tree diseases, including apple ring rot, brown spot, powdery mildew, and downy mildew. Full article

(This article belongs to the Special Issue Artificial Intelligence in Horticulture Production)

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28 pages, 2084 KB

Open AccessArticle

A Multimodal Deep Learning Framework for Intelligent Pest and Disease Monitoring in Smart Horticultural Production Systems

by Chuhuang Zhou, Yuhan Cao, Bihong Ming, Jingwen Luo, Fangrou Xu, Jiamin Zhang and Min Dong

Horticulturae 2026, 12(1), 8; https://doi.org/10.3390/horticulturae12010008 - 21 Dec 2025

Cited by 5 | Viewed by 1320

Abstract

This study addressed the core challenge of intelligent pest and disease monitoring and early warning in smart horticultural production by proposing a multimodal deep learning framework based on multi-parameter environmental sensor arrays. The framework integrates visual information with electrical signals to overcome the inherent limitations of conventional single-modality approaches in terms of real-time capability, stability, and early detection performance. A long-term field experiment was conducted over 18 months in the Hetao Irrigation District of Bayannur, Inner Mongolia, using three representative horticultural crops—grape (Vitis vinifera), tomato (Solanum lycopersicum), and sweet pepper (Capsicum annuum)—to construct a multimodal dataset comprising illumination intensity, temperature, humidity, gas concentration, and high-resolution imagery, with a total of more than

2.6 \times 10^{6}

recorded samples. The proposed framework consists of a lightweight convolution–Transformer hybrid encoder for electrical signal representation, a cross-modal feature alignment module, and an early-warning decision module, enabling dynamic spatiotemporal modeling and complementary feature fusion under complex field conditions. Experimental results demonstrated that the proposed model significantly outperformed both unimodal and traditional fusion methods, achieving an accuracy of

0.921

, a precision of

0.935

, a recall of

0.912

, an F1-score of

0.923

, and an area under curve (AUC) of

0.957

, confirming its superior recognition stability and early-warning capability. Ablation experiments further revealed that the electrical feature encoder, cross-modal alignment module, and early-warning module each played a critical role in enhancing performance. This research provides a low-cost, scalable, and energy-efficient solution for precise pest and disease management in intelligent horticulture, supporting efficient monitoring and predictive decision-making in greenhouses, orchards, and facility-based production systems. It offers a novel technological pathway and theoretical foundation for artificial-intelligence-driven sustainable horticultural production. Full article

(This article belongs to the Special Issue Artificial Intelligence in Horticulture Production)

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23 pages, 491 KB

Open AccessArticle

A Cross-Crop and Cross-Regional Generalized Deep Learning Framework for Intelligent Disease Detection and Economic Decision Support in Horticulture

by Jifeng Li, Tangji Ke, Fansen Yue, Nuo Wang, Kexin Guo, Lingdong Mei and Yihong Song

Horticulturae 2025, 11(11), 1397; https://doi.org/10.3390/horticulturae11111397 - 19 Nov 2025

Cited by 4 | Viewed by 1513

Abstract

In facility horticultural production, intelligent disease recognition and precise intervention are vital for crop health and economic efficiency. We construct a multi-source dataset from Bayan Nur, Weifang, and Honghe that integrates handheld camera photos, drone field images, and laboratory-controlled samples. Handheld images capture fine lesion texture for close-up diagnosis common in greenhouses; drone images provide canopy-scale patterns and spatial context suited to open-field management; laboratory images offer controlled illumination and background for stable supervision and cross-crop feature learning. Our objective is robust cross-crop, cross-regional diagnosis and economically rational control. To this end, a model named CCGD-Net is proposed. It is designed as a multi-task framework. The framework incorporates a multi-scale perception module (MSFE) to produce hierarchical representations. It includes a cross-domain alignment module (CDAM) that reduces distribution shifts between greenhouse and open-field environments. The training follows an unsupervised domain adaptation setting that uses unlabeled target-region images. When such images are not available, the model functions in a pure generalization mode. The framework also integrates a regional economic strategy module (RESM) that transforms recognition outputs and local cost information into optimized intervention intensity. Experiments show an accuracy of 91.6%, an F1-score of 89.8%, and an mAP of 88.9%, outperforming Swin Transformer and ConvNeXt; removing RESM reduces F1 to 87.2%. In cross-regional testing (Weifang training → Honghe testing), the model attains an F1 of 88.0% and mAP of 86.5%. These results indicate that integrating complementary imaging modalities with domain alignment and economic optimization provides an effective solution for disease diagnosis across greenhouse and field systems. Full article

(This article belongs to the Special Issue Artificial Intelligence in Horticulture Production)

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20 pages, 3807 KB

Open AccessEditor’s ChoiceArticle

Analysis of Multi-Environment-Driven Variations in Net Photosynthetic Rate and Predictive Model Development for Tomatoes During Early Flowering and Fruit Development Stages in Winter Solar Greenhouses

by Yongsan Cheng, Nianhua Li, Zongyao Li, Aiwu Zhou, Bin Li and Yanxiu Miao

Horticulturae 2025, 11(11), 1367; https://doi.org/10.3390/horticulturae11111367 - 13 Nov 2025

Cited by 2 | Viewed by 1100

Abstract

In protected horticulture, precise regulation of light intensity [i.e., photosynthetic photon flux density (PPFD)], ambient temperature, and ambient CO₂ concentration is crucial for optimizing crop photosynthesis. Tomatoes, a key greenhouse crop, exhibit temporal variations in photosynthetic efficiency across their growth cycle. However, the differences in the dynamic responses of net photosynthetic rate (Pn) of tomatoes to environmental factors during flowering and fruit development stages in winter solar greenhouses, as well as how to utilize these differences respectively to achieve more precise on-demand environmental regulation, still require in-depth exploration. Based on measured data, this study employed decision tree (DT), random forest (RF), and XGBoost (XGB) models to predict net photosynthetic rate (Pn) across two growth periods. The results demonstrated that, in comparison with the early flowering stage, the photosynthetic potential of tomato leaves increased during the fruit development stage, with the Pn peak increasing by 11.5%. The proportion of observed data points in the high Pn range (25–35 μmol m⁻² s⁻¹) at the fruit development stage was 14.2%, which was significantly higher than the 6.7% observed at the early flowering stage. Meanwhile, the sensitivity of tomato leaves to changes in environmental factors also increased during the fruit development stage. On the independent test set, the XGB model exhibited the best predictive performance: the root mean square error (RMSE) for the early flowering stage model was 0.47 μmol m⁻² s⁻¹, with a mean absolute error (MAE) of 0.36 μmol m⁻² s⁻¹; for the fruit development stage, the RMSE was 0.60 μmol m⁻² s⁻¹, and the MAE was 0.41 μmol m⁻² s⁻¹. This study demonstrated the variation patterns of photosynthetic characteristics of tomatoes at different growth stages in response to environment factors. The established XGB model and the generated three-dimensional visualized Pn prediction surfaces provide a quantitative basis and decision-support tools to facilitate precise environmental management strategies for the coordinated dynamic regulation of light, temperature, and CO₂ in solar greenhouses. Full article

(This article belongs to the Special Issue Artificial Intelligence in Horticulture Production)

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20 pages, 3887 KB

Open AccessArticle

Predicting Sweet Pepper Yield Based on Fruit Counts at Multiple Ripeness Stages Monitored by an AI-Based System Mounted on a Pipe-Rail Trolley

by Kota Shimomoto, Mitsuyoshi Shimazu, Takafumi Matsuo, Syuji Kato, Hiroki Naito, Masakazu Kashino, Nozomu Ohta, Sota Yoshida and Tokihiro Fukatsu

Horticulturae 2025, 11(7), 718; https://doi.org/10.3390/horticulturae11070718 - 20 Jun 2025

Viewed by 1510

Abstract

In our previous study, we developed a monitoring system for automatically counting tomatoes produced in protected horticulture using deep learning–based object detection. In this study, we adapted the system for sweet peppers and developed a monitoring system tailored to this crop. We evaluated its fruit detection and counting performance in a large-scale commercial greenhouse. Furthermore, we investigated the relationship between fruit counts at different ripeness stages and the total yield in the cultivation area, and we assessed the accuracy when predicting the yield for the following week. The results confirmed that the system maintained a stable fruit detection performance throughout the trial, and that its outputs were reliable enough to indicate its potential to replace manual counting. In addition, the average number of fruits at the 1–40% and 41–80% ripeness stages across six planting rows showed a correlation with the total weekly yield in the entire 0.6 ha cultivation area the following week. A yield prediction model using average fruit counts at these two ripeness stages as explanatory variables achieved a WAPE of 21.35%, indicating that the monitoring system is effective for yield prediction. Full article

(This article belongs to the Special Issue Artificial Intelligence in Horticulture Production)

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20 pages, 23934 KB

Open AccessArticle

LCLN-CA: A Survival Regression Analysis-Based Prediction Method for Catechin Content in Yunnan Sun-Dried Tea

by Hongxu Li, Qiaomei Wang, Houqiao Wang, Limei Li, Xinghua Wang, Tianyu Wu, Chun Wang, Ye Qian, Xiaohua Wang, Yuxin Xia, Jin Xie, Wenxia Yuan and Baijuan Wang

Horticulturae 2024, 10(12), 1321; https://doi.org/10.3390/horticulturae10121321 - 11 Dec 2024

Cited by 1 | Viewed by 2495

Abstract

Catechins are pivotal determinants of tea quality, with soil environmental factors playing a crucial role in the synthesis and accumulation of these compounds. To investigate the impact of changes in tea garden soil environments on the catechin content in sun-dried tea, this study measured the catechin content in soil samples and corresponding tea leaves from Nanhua, Yunnan, China. By integrating the variations in catechin content with those of 17 soil factors and employing COX regression factor analysis, it was found that pH, organic matter (OM), fluoride, arsenic (As), and chromium (Cr) were significantly correlated with catechin content (p < 0.05). Further, using the LASSO regression for variable selection, a model named LCLN-CA was constructed with four variables including pH, OM, fluoride, and As. The LCLN-CA model demonstrated high fitting accuracy with AUC values of 0.674, 0.784, and 0.749 for catechin content intervals of CA ≤ 10%, 10% < CA ≤ 20%, and 20% < CA ≤ 30% in the training set, respectively. The validation set showed AUC values of 0.630, 0.756, and 0.723, respectively, indicating a well-calibrated curve. Based on the LCLN-CA model and the DynNom framework, a visual prediction system for catechin content in Yunnan sun-dried tea was developed. External validation with a test dataset achieved an

A c c u r a c y

of 0.870. This study explored the relationship between soil-related factors and variations in catechin content, paving a new way for the prediction of catechin content in tea and enhancing the practical application value of artificial intelligence technology in agricultural production. Full article

(This article belongs to the Special Issue Artificial Intelligence in Horticulture Production)

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18 pages, 5188 KB

Open AccessArticle

Using Machine Learning Algorithms to Investigate the Impact of Temperature Treatment and Salt Stress on Four Forage Peas (Pisum sativum var. arvense L.)

by Onur Okumuş, Ahmet Say, Barış Eren, Fatih Demirel, Satı Uzun, Mehmet Yaman and Adnan Aydın

Horticulturae 2024, 10(6), 656; https://doi.org/10.3390/horticulturae10060656 - 20 Jun 2024

Cited by 15 | Viewed by 2748

Abstract

The combination of high or low temperatures and high salt may cause significant harm to the yield, quality, and overall productivity of forage pea crops. The germination process, a crucial phase in the life cycle of forage peas, may be greatly influenced by varying temperature and salinity conditions. To comprehend the influence of these elements on the germination of forage peas, one must use many tactics, including the choice of resilient forage pea cultivars. The experiment aimed to evaluate the response of four forage pea cultivars (Arda, Ozkaynak, Taskent, and Tore) caused by various temperature (10 °C, 15 °C, and 20 °C) and salt (0, 5, 10, 15, and 20 dS m⁻¹) conditions at the germination stage using multivariate analysis and machine learning methods. An observation of statistical significance (p < 0.01) was made regarding the variations between genotypes, temperature–salt levels, and the interaction of the observed factors: germination percentage (GP), shoot length (SL), root length (RL), fresh weight (FW), and dry weight (DW). The cultivar Tore had the best values for SL (1.63 cm), RL (5.38 cm), FW (1.10 g), and DW (0.13 g) among all the cultivars. On the other hand, the Ozkaynak cultivar had the highest value for GP (89.13%). The values of all of the parameters that were investigated decreased as the salt level rose, whereas the values increased when the temperature level increased. As a result, the Tore cultivar exhibited the highest values for shoot length, root length, fresh weight, and dry weight variables when exposed to a maximum temperature of 20 °C and a saline level of 0 dS m⁻¹. It was determined that temperature treatment of fodder peas can reduce salt stress if kept at optimum levels. The effects of temperature and salt treatments on the germination data of several fodder pea cultivars were analyzed and predicted. Three distinct machine learning algorithms were used to create predictions. Based on R² (0.899), MSE (5.344), MAPE (6.953), and MAD (4.125) measures, the MARS model predicted germination power (GP) better. The GPC model performed better in predicting shoot length (R² = 0.922, MSE = 0.602, MAPE = 11.850, and MAD = 0.326) and root length (R² = 0.900, MSE = 0.719, MAPE = 12.673, and MAD = 0.554), whereas the Xgboost model performed better in estimating fresh weight (R² = 0.966, MSE = 0.130, MAPE = 11.635, and MAD = 0.090) and dry weight (R² = 0.895, MSE = 0.021, MAPE = 12.395, and MAD = 0.013). The results of the research show that the techniques and analyses used can estimate stress tolerance, susceptibility levels, and other plant parameters, making it a cost-effective and reliable way to quickly and accurately study forage peas and related species. Full article

(This article belongs to the Special Issue Artificial Intelligence in Horticulture Production)

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