Application of Artificial Intelligence in the Processing of Horticultural Crops

A special issue of Horticulturae (ISSN 2311-7524). This special issue belongs to the section "Processed Horticultural Products".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2318

Special Issue Editor


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Guest Editor
School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang 212013, China
Interests: hyperspectral imaging; machine vision; colorimetric sensing;intelligent sensory evaluation of agricultural products and food quality
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Special Issue Information

Dear Colleagues,

The application of artificial intelligence (AI) in the processing of horticultural crops integrates advanced multi-sensor technologies and intelligent control systems to enable precise monitoring, optimized handling, and quality enhancement throughout the stages of harvesting, preservation, storage, processing, and quality detection. This interdisciplinary approach promotes automation, enhances product consistency, and supports the sustainable development of horticultural production.

Topics of interest for this Special Issue include, but are not limited to, the following:

  • Intelligent harvesting: The application of AI-driven recognition and localization technologies (e.g., computer vision, deep learning, robotic systems) in the automated harvesting of horticultural crops.
  • Intelligent preservation: The integration of IoT-based environmental sensing and predictive modeling for the dynamic control of preservation conditions.
  • Intelligent storage: The development of multi-sensor fusion systems and AI algorithms for the real-time monitoring and modeling of crop status during storage.
  • Intelligent quality detection: The application of multi-modal sensing technologies (e.g., imaging, spectroscopy, olfaction, photoelectric sensors) combined with AI models for the comprehensive, non-destructive quality assessment and grading of horticultural products.

Dr. Xiaoyu Tian
Guest Editor

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Keywords

  • artificial intelligence
  • horticultural crop processing
  • automated harvesting
  • postharvest preservation
  • storage environment monitoring
  • sensor fusion technology
  • process control and optimization
  • non-destructive quality assessment
  • computer vision
  • multimodal sensing
  • machine learning algorithms

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Published Papers (4 papers)

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Research

22 pages, 24236 KB  
Article
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
Viewed by 305
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
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21 pages, 12646 KB  
Article
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
Viewed by 472
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
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21 pages, 2464 KB  
Article
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
Viewed by 704
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
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20 pages, 41202 KB  
Article
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 1 | Viewed by 510
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
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