Horticulturae

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19 pages, 3706 KB

Open AccessArticle

Non-Destructive Determination of Moisture Content in White Tea During Withering Using VNIR Spectroscopy and Ensemble Modeling

by Qinghai He, Hongkai Shen, Zhiyuan Liu, Benxue Ma, Yong He, Zhi Lin, Weihong Liu, Pei Wang, Xiaoli Li and Peng Qi

Horticulturae 2026, 12(4), 488; https://doi.org/10.3390/horticulturae12040488 - 16 Apr 2026

Viewed by 577

Abstract

As one of the six major traditional tea types in China, white tea’s quality formation is primarily influenced by the withering process. However, traditional methods for monitoring withering fail to achieve precise and stable control of moisture content. To address this issue, a [...] Read more.

As one of the six major traditional tea types in China, white tea’s quality formation is primarily influenced by the withering process. However, traditional methods for monitoring withering fail to achieve precise and stable control of moisture content. To address this issue, a total of 650 samples were collected at 13 withering time points (0–36 h), and the dataset was split into training and test sets at a 7:3 ratio. This study proposes a PRXBoost ensemble model for quantitative detection of withered white tea, which integrates data augmentation and intelligent algorithms. The ensemble model uses a Bagging-based weighted integration technique to combine Partial Least Squares Regression (PLSR), Ridge, and Extreme Gradient Boosting (XGBoost) models, and it conducts an in-depth analysis of the decision-making process within the PRXBoost model. First, the effectiveness of the data augmentation strategy and the superiority of the gradient descent algorithm are verified through pre-modeling based on the PLSR model and hyperparameter pre-search using the XGBoost model, respectively. Additionally, the Bayes algorithm is employed to optimize the weights of the sub-models, further enhancing the overall predictive performance. The results show that the PRXBoost model achieved the best performance among the compared models on the test set, with R² = 0.854 and RMSE = 0.080, exceeding the highest R² of a single model by 6%. These results indicate that PRXBoost provided improved predictive performance for moisture estimation within the current dataset. Finally, the SHapley Additive exPlanations (SHAP) algorithm is used to analyze the influence of each input feature on the prediction results, successfully identifying the 1916 nm and 1453 nm spectral bands as significant influencers of the prediction outcomes. These results suggest that the proposed model can support rapid, non-destructive monitoring of moisture evolution and provide actionable information for withering endpoint decision control. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in the Processing of Horticultural Crops)

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24 pages, 6491 KB

Open AccessArticle

An Enhanced Network Based on Improved YOLOv7 for Apple Robot Picking

by Jie Wu, Huawei Yang, Shucheng Wang, Ning Li, Xiaojie Shi, Xuzhen Lu, Zhimin Lun, Shaowei Wang, Supakorn Wongsuk and Peng Qi

Horticulturae 2025, 11(12), 1539; https://doi.org/10.3390/horticulturae11121539 - 18 Dec 2025

Viewed by 605

Abstract

In the conventional agricultural production process, the harvesting of mature fruits is frequently dependent on the observation and labor of workers, a process that is often time-consuming and labor-intensive. This study proposes an enhanced YOLOv7 detection and recognition model that incorporates a cross-spatial-channel [...] Read more.

In the conventional agricultural production process, the harvesting of mature fruits is frequently dependent on the observation and labor of workers, a process that is often time-consuming and labor-intensive. This study proposes an enhanced YOLOv7 detection and recognition model that incorporates a cross-spatial-channel 3D attention mechanism, a prediction head, and a weighted bidirectional feature pyramid neck optimization. The motivation for this study is to address the issues of uneven target distribution, mutual occlusion of fruits, and uneven light distribution that are prevalent in harvesting operations within orchards. The experimental findings demonstrate that the proposed model achieves an mAP@0.5–0.95 of 89.3%, representing an enhancement of 8.9% in comparison to the initial network. This method has resolved the issue of detecting and positioning the harvesting manipulator in complex orchard scenarios, thereby providing technical support for unmanned agricultural operations. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in the Processing of Horticultural Crops)

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21 pages, 14294 KB

Open AccessArticle

ToRLNet: A Lightweight Deep Learning Model for Tomato Detection and Quality Assessment Across Ripeness Stages

by Huihui Sun, Xi Xi, An-Qi Wu and Rui-Feng Wang

Horticulturae 2025, 11(11), 1334; https://doi.org/10.3390/horticulturae11111334 - 5 Nov 2025

Cited by 5 | Viewed by 1433

Abstract

This study proposes ToRLNet, a lightweight tomato ripeness detector designed for real-time deployment in resource-constrained agricultural settings. Built on YOLOv12n, ToRLNet integrates three self-constructed modules (WaveFusionNet for frequency–spatial enhancement and feature extraction, ETomS for efficient context-aware encoding, and SFAConv for selective multi-scale downsampling) [...] Read more.

This study proposes ToRLNet, a lightweight tomato ripeness detector designed for real-time deployment in resource-constrained agricultural settings. Built on YOLOv12n, ToRLNet integrates three self-constructed modules (WaveFusionNet for frequency–spatial enhancement and feature extraction, ETomS for efficient context-aware encoding, and SFAConv for selective multi-scale downsampling) to address subtle inter-stage color transitions, small fruit instances, and cluttered canopies. We benchmark ToRLNet against lightweight and small-scale YOLO baselines (YOLOv8–YOLOv12) and conduct controlled ablations isolating each module’s contribution. ToRLNet attains Precision 90.27%, Recall 86.77%, F1-score 88.49%, mAP50 91.76%, and mAP 78.01% with only 6.9 GFLOPs, outperforming representative nano/small YOLO variants under comparable compute budgets. Ablation results show WaveFusionNet improves spectral–textural robustness, ETomS balances the precision–recall trade-off while reducing redundancy, and SFAConv preserves fine chromatic gradients and boundary structure during downsampling; their combination yields the most balanced performance. These findings demonstrate that ToRLNet delivers a favorable accuracy–efficiency trade-off and provides a practical foundation for on-board perception in automated harvesting, yield estimation, and greenhouse management. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in the Processing of Horticultural Crops)

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23 pages, 24237 KB

Open AccessArticle

BMDNet-YOLO: A Lightweight and Robust Model for High-Precision Real-Time Recognition of Blueberry Maturity

by Huihui Sun and Rui-Feng Wang

Horticulturae 2025, 11(10), 1202; https://doi.org/10.3390/horticulturae11101202 - 5 Oct 2025

Cited by 8 | Viewed by 1608

Abstract

Accurate real-time detection of blueberry maturity is vital for automated harvesting. However, existing methods often fail under occlusion, variable lighting, and dense fruit distribution, leading to reduced accuracy and efficiency. To address these challenges, we designed a lightweight deep learning framework that integrates [...] Read more.

Accurate real-time detection of blueberry maturity is vital for automated harvesting. However, existing methods often fail under occlusion, variable lighting, and dense fruit distribution, leading to reduced accuracy and efficiency. To address these challenges, we designed a lightweight deep learning framework that integrates improved feature extraction, attention-based fusion, and progressive transfer learning to enhance robustness and adaptability To overcome these challenges, we propose BMDNet-YOLO, a lightweight model based on an enhanced YOLOv8n. The backbone incorporates a FasterPW module with parallel convolution and point-wise weighting to improve feature extraction efficiency and robustness. A coordinate attention (CA) mechanism in the neck enhances spatial-channel feature selection, while adaptive weighted concatenation ensures efficient multi-scale fusion. The detection head employs a heterogeneous lightweight structure combining group and depthwise separable convolutions to minimize parameter redundancy and boost inference speed. Additionally, a three-stage transfer learning framework (source-domain pretraining, cross-domain adaptation, and target-domain fine-tuning) improves generalization. Experiments on 8250 field-collected and augmented images show BMDNet-YOLO achieves 95.6% mAP@0.5, 98.27% precision, and 94.36% recall, surpassing existing baselines. This work offers a robust solution for deploying automated blueberry harvesting systems. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in the Processing of Horticultural Crops)

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21 pages, 12646 KB

Open AccessArticle

A Vision-Based Information Processing Framework for Vineyard Grape Picking Using Two-Stage Segmentation and Morphological Perception

by Yifei Peng, Jun Sun, Zhaoqi Wu, Jinye Gao, Lei Shi and Zhiyan Shi

Horticulturae 2025, 11(9), 1039; https://doi.org/10.3390/horticulturae11091039 - 2 Sep 2025

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Abstract

To achieve efficient vineyard grape picking, a vision-based information processing framework integrating two-stage segmentation with morphological perception is proposed. In the first stage, an improved YOLOv8s-seg model is employed for coarse segmentation, incorporating two key enhancements: first, a dynamic deformation feature aggregation module [...] Read more.

To achieve efficient vineyard grape picking, a vision-based information processing framework integrating two-stage segmentation with morphological perception is proposed. In the first stage, an improved YOLOv8s-seg model is employed for coarse segmentation, incorporating two key enhancements: first, a dynamic deformation feature aggregation module (DDFAM), which facilitates the extraction of complex structural and morphological features; and second, an efficient asymmetric decoupled head (EADHead), which improves boundary awareness while reducing parameter redundancy. Compared with mainstream segmentation models, the improved model achieves superior performance, attaining the highest mAP@0.5 of 86.75%, a lightweight structure with 10.34 M parameters, and a real-time inference speed of 10.02 ms per image. In the second stage, the fine segmentation of fruit stems is performed using an improved OTSU thresholding algorithm, which is applied to a single-channel image derived from the hue component of the HSV color space, thereby enhancing robustness under complex lighting conditions. Morphological features extracted from the preprocessed fruit stem, including centroid coordinates and a skeleton constructed via medial axis transform (MAT), are further utilized to establish the spatial relationships with a picking point and cutting axis. The visualization analysis confirms the high feasibility and adaptability of the proposed framework, providing essential technical support for the automation of grape harvesting. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in the Processing of Horticultural Crops)

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21 pages, 2464 KB

Open AccessArticle

Prediction of Selected Minerals in Beef-Type Tomatoes Using Machine Learning for Digital Agriculture

by Aylin Kabaş, Uğur Ercan, Onder Kabas and Georgiana Moiceanu

Horticulturae 2025, 11(8), 971; https://doi.org/10.3390/horticulturae11080971 - 16 Aug 2025

Cited by 2 | Viewed by 1424

Abstract

Tomato is one of the most important vegetables due to its high production and nutritional value. With the development of digital agriculture, the tomato breeding and processing industries have seen a rapid increase in the need for simple, low-labor, and inexpensive methods for [...] Read more.

Tomato is one of the most important vegetables due to its high production and nutritional value. With the development of digital agriculture, the tomato breeding and processing industries have seen a rapid increase in the need for simple, low-labor, and inexpensive methods for analyzing tomato composition. This study proposes a digital method to predict four minerals (calcium, potassium, phosphorus, and magnesium) in beef-type tomato using machine learning models, including k-nearest neighbors (kNN), artificial neural networks (ANNs), and Support Vector Regression (SVR). The models were discriminated using the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The kNN model showed the best performance for estimation of quantity of calcium, potassium, phosphorus, and magnesium. The results demonstrate that kNN consistently outperforms ANNs and SVR across all target nutrients, achieving the highest R² and the lowest error metrics (RMSE, MAE, and MAPE). Notably, kNN achieved an exceptional R² of 0.8723 and a remarkably low MAPE of 3.95% in predicting phosphorus. This study highlights how machine learning can provide a versatile, accurate, and efficient solution for tomato mineral analysis in digital agriculture. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in the Processing of Horticultural Crops)

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20 pages, 41202 KB

Open AccessArticle

Copper Stress Levels Classification in Oilseed Rape Using Deep Residual Networks and Hyperspectral False-Color Images

by Yifei Peng, Jun Sun, Zhentao Cai, Lei Shi, Xiaohong Wu, Chunxia Dai and Yubin Xie

Horticulturae 2025, 11(7), 840; https://doi.org/10.3390/horticulturae11070840 - 16 Jul 2025

Cited by 3 | Viewed by 1157

Abstract

In recent years, heavy metal contamination in agricultural products has become a growing concern in the field of food safety. Copper (Cu) stress in crops not only leads to significant reductions in both yield and quality but also poses potential health risks to [...] Read more.

In recent years, heavy metal contamination in agricultural products has become a growing concern in the field of food safety. Copper (Cu) stress in crops not only leads to significant reductions in both yield and quality but also poses potential health risks to humans. This study proposes an efficient and precise non-destructive detection method for Cu stress in oilseed rape, which is based on hyperspectral false-color image construction using principal component analysis (PCA). By comprehensively capturing the spectral representation of oilseed rape plants, both the one-dimensional (1D) spectral sequence and spatial image data were utilized for multi-class classification. The classification performance of models based on 1D spectral sequences was compared from two perspectives: first, between machine learning and deep learning methods (best accuracy: 93.49% vs. 96.69%); and second, between shallow and deep convolutional neural networks (CNNs) (best accuracy: 95.15% vs. 96.69%). For spatial image data, deep residual networks were employed to evaluate the effectiveness of visible-light and false-color images. The RegNet architecture was chosen for its flexible parameterization and proven effectiveness in extracting multi-scale features from hyperspectral false-color images. This flexibility enabled RegNetX-6.4GF to achieve optimal performance on the dataset constructed from three types of false-color images, with the model reaching a Macro-Precision, Macro-Recall, Macro-F₁, and Accuracy of 98.17%, 98.15%, 98.15%, and 98.15%, respectively. Furthermore, Grad-CAM visualizations revealed that latent physiological changes in plants under heavy metal stress guided feature learning within CNNs, and demonstrated the effectiveness of false-color image construction in extracting discriminative features. Overall, the proposed technique can be integrated into portable hyperspectral imaging devices, enabling real-time and non-destructive detection of heavy metal stress in modern agricultural practices. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in the Processing of Horticultural Crops)

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Review

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34 pages, 1255 KB

Open AccessReview

Harnessing the Synergy Between Edible Coatings and Non-Thermal Technologies for Improved Food Quality and Sustainable Preservation

by Xiaoyu Tian, Hui Dong, Qin Fang, Xiaorui Zhang, Chunxia Dai and Joshua Harrington Aheto

Horticulturae 2025, 11(12), 1466; https://doi.org/10.3390/horticulturae11121466 - 4 Dec 2025

Cited by 4 | Viewed by 1151

Abstract

This review explores the synergistic integration of edible coatings and non-thermal preservation technologies as a multifaceted approach to maintaining food quality, safety, and sustainability. Edible coatings—composed of polysaccharides, proteins, lipids, or composite biopolymers—serve as biodegradable barriers that control moisture, gas, and solute transfer [...] Read more.

This review explores the synergistic integration of edible coatings and non-thermal preservation technologies as a multifaceted approach to maintaining food quality, safety, and sustainability. Edible coatings—composed of polysaccharides, proteins, lipids, or composite biopolymers—serve as biodegradable barriers that control moisture, gas, and solute transfer while acting as carriers for bioactive compounds such as antimicrobials and antioxidants. Meanwhile, non-thermal techniques, including high-pressure processing, cold plasma, ultrasound, photodynamic inactivation, modified atmosphere packaging, and irradiation, offer microbial inactivation and enzymatic control without compromising nutritional and sensory attributes. When combined, these technologies exhibit complementary effects: coatings enhance the stability of bioactives and protect surface quality, while non-thermal treatments boost antimicrobial efficacy and promote active compound penetration. The review highlights their comparative advantages over individual treatments—improved microbial inhibition, nutrient retention, and sensory quality. It further discusses the possible mechanisms through which edible coatings and selected hurdles induced microbial decontamination. Finally, the study identified major drawbacks and provided strategic recommendations to overcome these limitations, including optimizing coating formulations for specific food matrices, tailoring process parameters to minimize adverse physicochemical changes, and conducting pilot-scale validations to bridge the gap between laboratory success and industrial application. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in the Processing of Horticultural Crops)

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