Agriculture

Research

23 pages, 5549 KB

Open AccessArticle

A Precision Weeding System for Cabbage Seedling Stage

by Pei Wang, Weiyue Chen, Qi Niu, Chengsong Li, Yuheng Yang and Hui Li

Agriculture 2026, 16(3), 384; https://doi.org/10.3390/agriculture16030384 - 5 Feb 2026

Viewed by 524

This study developed an integrated vision–actuation system for precision weeding in indoor soil bin environments, with cabbage as a case example. The system integrates lightweight object detection, 3D co-ordinate mapping, path planning, and a three-axis synchronized conveyor-type actuator to enable precise weed identification [...] Read more.

This study developed an integrated vision–actuation system for precision weeding in indoor soil bin environments, with cabbage as a case example. The system integrates lightweight object detection, 3D co-ordinate mapping, path planning, and a three-axis synchronized conveyor-type actuator to enable precise weed identification and automated removal. By integrating ECA and CBAM attention mechanisms into YOLO11, we developed the YOLO11-WeedNet model. This integration significantly enhanced the detection performance for small-scale weeds under complex lighting and cluttered backgrounds. Based on the optimal model performance achieved during experimental evaluation, the model achieved 96.25% precision, 86.49% recall, 91.10% F1-score, and a mean Average Precision (mAP@0.5) of 91.50% calculated across two categories (crop and weed). An RGB-D fusion localization method combined with a protected-area constraint enabled accurate mapping of weed spatial positions. Furthermore, an enhanced Artificial Hummingbird Algorithm (AHA⁺) was proposed to optimize the execution path and reduce the operating trajectory while maintaining real-time performance. Indoor soil bin tests showed positioning errors of less than 8 mm on the X/Y axes, depth control within ±1 mm on the Z-axis, and an average weeding rate of 88.14%. The system achieved zero contact with cabbage seedlings, with a processing time of 6.88 s per weed. These results demonstrate the feasibility of the proposed system for precise and automated weeding at the cabbage seedling stage. Full article

(This article belongs to the Special Issue Automation Strategy Using Machine Learning in Horticultural Crop Cultivation)

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25 pages, 2789 KB

Open AccessArticle

Hybrid Zero-Shot Node-Count Estimation and Growth-Information Sharing for Lisianthus (Eustoma grandiflorum) Cultivation in Fukushima’s Floricultural Revitalization

by Hiroki Naito, Kota Kobayashi, Osamu Inaba, Fumiki Hosoi, Norihiro Hoshi and Yoshimichi Yamashita

Agriculture 2026, 16(3), 296; https://doi.org/10.3390/agriculture16030296 - 23 Jan 2026

Viewed by 691

Abstract

This paper presents a hybrid pipeline based on zero-shot vision models for automatic node count estimation in Lisianthus (Eustoma grandiflorum) cultivation and a system for real-time growth information sharing. The multistage image analysis pipeline integrates Grounding DINO for zero-shot leaf-region detection, [...] Read more.

This paper presents a hybrid pipeline based on zero-shot vision models for automatic node count estimation in Lisianthus (Eustoma grandiflorum) cultivation and a system for real-time growth information sharing. The multistage image analysis pipeline integrates Grounding DINO for zero-shot leaf-region detection, MiDaS for monocular depth estimation, and a YOLO-based classifier, using daily time-lapse images from low-cost fixed cameras in commercial greenhouses. The model parameters are derived from field measurements of 2024 seasonal crops (Trial 1) and then applied to different cropping seasons, growers, and cultivars (Trials 2 and 3) without any additional retraining. Trial 1 indicates high accuracy (R² = 0.930, mean absolute error (MAE) = 0.73). Generalization performance is confirmed in Trials 2 (MAE = 0.45) and 3 (MAE = 1.14); reproducibility across multiple growers and four cultivars yields MAEs of approximately ±1 node. The model effectively captures the growth progression despite variations in lighting, plant architecture, and grower practices, although errors increase during early growth stages and under unstable leaf detection. Furthermore, an automated Discord-based notification system enables real-time sharing of node trends and analytical images, facilitating communication. The feasibility of combining zero-shot vision models with cloud-based communication tools for sustainable and collaborative floricultural production is thus demonstrated. Full article

(This article belongs to the Special Issue Automation Strategy Using Machine Learning in Horticultural Crop Cultivation)

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30 pages, 6863 KB

Open AccessArticle

Explainable Deep Learning and Edge Inference for Chilli Thrips Severity Classification in Strawberry Canopies

by Uchechukwu Ilodibe, Daeun Choi, Sriyanka Lahiri, Changying Li, Daniel Hofstetter and Yiannis Ampatzidis

Agriculture 2026, 16(2), 252; https://doi.org/10.3390/agriculture16020252 - 19 Jan 2026

Viewed by 762

Abstract

Traditional plant scouting is often a costly and labor-intensive task that requires experienced specialists to diagnose and manage plant stresses. Artificial intelligence (AI), particularly deep learning and computer vision, offers the potential to transform scouting by enabling rapid, non-intrusive detection and classification of [...] Read more.

Traditional plant scouting is often a costly and labor-intensive task that requires experienced specialists to diagnose and manage plant stresses. Artificial intelligence (AI), particularly deep learning and computer vision, offers the potential to transform scouting by enabling rapid, non-intrusive detection and classification of early stress symptoms from plant images. However, deep learning models are often opaque, relying on millions of parameters to extract complex nonlinear features that are not interpretable by growers. Recently, eXplainable AI (XAI) techniques have been used to identify key spatial regions that contribute to model predictions. This project explored the potential of convolutional neural networks (CNNs) for classifying the severity of chilli thrips damage in strawberry plants in Florida and employed XAI techniques to interpret model decisions and identify symptom-relevant canopy features. Four CNN architectures, YOLOv11, EfficientNetV2, Xception, and MobileNetV3, were trained and evaluated using 2353 square RGB canopy images of different sizes (256, 480, 640 and 1024 pixels) to classify symptoms as healthy, moderate, or severe. Trade-offs between image size, model parameter count, inference speed, and accuracy were examined in determining the best-performing model. The models achieved accuracies ranging from 77% to 85% with inference times of 5.7 to 262.3 ms, demonstrating strong potential for real-time pest severity estimation. Gradient-Weighted Class Activation Mapping (Grad-CAM) visualization revealed that model attention focused on biologically relevant regions such as fruits, stems, leaf edges, leaf surfaces, and dying leaves, areas commonly affected by chilli thrips. Subsequent analysis showed that model attention spread from localized regions in healthy plants to wide diffuse regions in severe plants. This alignment between model attention and expert scouting logic suggests that CNNs internalize symptom-specific visual cues and can reliably classify pest-induced plant stress. Full article

(This article belongs to the Special Issue Automation Strategy Using Machine Learning in Horticultural Crop Cultivation)

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22 pages, 3716 KB

Open AccessArticle

SPAD Retrieval of Jujube Canopy Using UAV-Based Multispectral and RGB Features with Genetic Algorithm–Optimized Ensemble Learning

by Guojun Hong, Caili Yu, Jianqiang Lu and Lin Liu

Agriculture 2026, 16(2), 191; https://doi.org/10.3390/agriculture16020191 - 12 Jan 2026

Viewed by 537

Abstract

The Soil and Plant Analyzer Development (SPAD) value is a reliable proxy for chlorophyll, yet conventional field measurement remains labor-intensive and spatially limited. Current remote sensing inversion models typically depend on costly multispectral sensors and rarely account for phenological changes, restricting their applicability [...] Read more.

The Soil and Plant Analyzer Development (SPAD) value is a reliable proxy for chlorophyll, yet conventional field measurement remains labor-intensive and spatially limited. Current remote sensing inversion models typically depend on costly multispectral sensors and rarely account for phenological changes, restricting their applicability across orchards and seasons. To overcome these limitations, this study introduces a stage-aware and low-cost SPAD inversion framework for jujube trees, integrating multi-source data fusion and an optimized ensemble model. A two-year experiment (2023–2024) combined UAV multispectral vegetation indices (VI) with RGB-derived color indices (CI) across leaf expansion, flowering, and fruit-setting stages. Rather than using static features, stage-specific predictors were systematically identified through a hybrid selection mechanism combining Random Forest Cumulative Feature Importance (RF-CFI), Recursive Feature Elimination (RFE), and F-tests. Building on these tailored features, XGBoost, decision tree (DT), CatBoost, and an Optimized Integrated Architecture (OIA) were developed, with all hyperparameters globally tuned using a genetic algorithm (GA). The RFI-CFI-OIA-GA model delivered superior accuracy (R² = 0.758–0.828; MSE = 0.214–2.593; MAPE = 0.01–0.045 in 2024) in the training dataset, and robust cross-year transferability (R² = 0.541–0.608; MSE = 0.698–5.139; MAPE = 0.015–0.058 in 2023). These results demonstrate that incorporating phenological perception into multi-source data fusion substantially reduces interference and enhances generalizability, providing a scalable and reusable strategy for precision orchard management and spatiotemporal SPAD mapping. Full article

(This article belongs to the Special Issue Automation Strategy Using Machine Learning in Horticultural Crop Cultivation)

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15 pages, 1820 KB

Open AccessArticle

Probabilistic Deep Learning Framework for Greenhouse Microclimate Prediction with Time-Varying Uncertainty and Covariance Analysis

by Woo-Joo Choi and Myongkyoon Yang

Agriculture 2025, 15(23), 2461; https://doi.org/10.3390/agriculture15232461 - 27 Nov 2025

Cited by 1 | Viewed by 895

Abstract

Although greenhouse microclimates typically exhibit gradual and near-linear transitions, abrupt fluctuations in external weather conditions and actuator operations introduce nonlinear dynamics that complicate accurate interpretation and prediction. Predicting greenhouse microclimate is a key element for achieving stable and energy efficient crop production, particularly [...] Read more.

Although greenhouse microclimates typically exhibit gradual and near-linear transitions, abrupt fluctuations in external weather conditions and actuator operations introduce nonlinear dynamics that complicate accurate interpretation and prediction. Predicting greenhouse microclimate is a key element for achieving stable and energy efficient crop production, particularly in strawberry greenhouse. However, existing greenhouse microclimate deterministic prediction models do not adequately reflect the nonlinear, time-varying characteristics of greenhouses and the inherent uncertainty in data, limiting probabilistic decision-making. In this study, we developed a probabilistic deep learning framework to estimate and interpret uncertainty while simultaneously predicting greenhouse microclimate quantitatively. The proposed one-dimensional convolutional neural network model learned the time-series characteristics of greenhouse internal and external environmental information and control data, predicting a total of nine parameters, including three-dimensional predicted values 3 h later and six-dimensional covariance elements. The model demonstrated high sharpness and calibration performance, with an average R² of 0.93, a negative log likelihood of 2.08, and a Coverage 90% of 0.901 for three microclimates. In addition, the estimated covariance matrix was used to interpret the time-varying correlations between microclimate variables, confirming local simultaneous variability not captured by global correlation analysis. These results suggest that the model in this study can provide greenhouse operators with explainable uncertainty interpretation and robust control decision support information. Full article

(This article belongs to the Special Issue Automation Strategy Using Machine Learning in Horticultural Crop Cultivation)

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23 pages, 2568 KB

Open AccessArticle

Reinforcement Learning-Driven Digital Twin for Zero-Delay Communication in Smart Greenhouse Robotics

by Cristian Bua, Luca Borgianni, Davide Adami and Stefano Giordano

Agriculture 2025, 15(12), 1290; https://doi.org/10.3390/agriculture15121290 - 15 Jun 2025

Cited by 7 | Viewed by 3245

Abstract

This study presents a networked cyber-physical architecture that integrates a Reinforcement Learning-based Digital Twin (DT) to enable zero-delay interaction between physical and digital components in smart agriculture. The proposed system allows real-time remote control of a robotic arm inside a hydroponic greenhouse, using [...] Read more.

This study presents a networked cyber-physical architecture that integrates a Reinforcement Learning-based Digital Twin (DT) to enable zero-delay interaction between physical and digital components in smart agriculture. The proposed system allows real-time remote control of a robotic arm inside a hydroponic greenhouse, using a sensor-equipped Wearable Glove (SWG) for hand motion capture. The DT operates in three coordinated modes: Real2Digital, Digital2Real, and Digital2Digital, supporting bidirectional synchronization and predictive simulation. A core innovation lies in the use of a Reinforcement Learning model to anticipate hand motions, thereby compensating for network latency and enhancing the responsiveness of the virtual–physical interaction. The architecture was experimentally validated through a detailed communication delay analysis, covering sensing, data processing, network transmission, and 3D rendering. While results confirm the system’s effectiveness under typical conditions, performance may vary under unstable network scenarios. This work represents a promising step toward real-time adaptive DTs in complex smart greenhouse environments. Full article

(This article belongs to the Special Issue Automation Strategy Using Machine Learning in Horticultural Crop Cultivation)

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15 pages, 5185 KB

Open AccessArticle

Research on Recognition of Green Sichuan Pepper Clusters and Cutting-Point Localization in Complex Environments

by Qi Niu, Wenjun Ma, Rongxiang Diao, Wei Yu, Chunlei Wang, Hui Li, Lihong Wang, Chengsong Li and Pei Wang

Agriculture 2025, 15(10), 1079; https://doi.org/10.3390/agriculture15101079 - 16 May 2025

Viewed by 1229

Abstract

The harvesting of green Sichuan pepper remains heavily reliant on manual field operations, but automation can enhance the efficiency, quality, and sustainability of the process. However, challenges such as intertwined branches, dense foliage, and overlapping pepper clusters hinder intelligent harvesting by causing inaccuracies [...] Read more.

The harvesting of green Sichuan pepper remains heavily reliant on manual field operations, but automation can enhance the efficiency, quality, and sustainability of the process. However, challenges such as intertwined branches, dense foliage, and overlapping pepper clusters hinder intelligent harvesting by causing inaccuracies in target recognition and localization. This study compared the performance of multiple You Only Look Once (YOLO) algorithms for recognition and proposed a cluster segmentation method based on K-means++ and a cutting-point localization strategy using geometry-based iterative optimization. A dataset containing 14,504 training images under diverse lighting and occlusion scenarios was constructed. Comparative experiments on YOLOv5s, YOLOv8s, and YOLOv11s models revealed that YOLOv11s achieved a recall of 0.91 in leaf-occluded environments, marking a 21.3% improvement over YOLOv5s, with a detection speed of 28 Frames Per Second(FPS). A K-means++-based cluster separation algorithm (K = 1~10, optimized via the elbow method) was developed and was combined with OpenCV to iteratively solve the minimum circumscribed triangle vertices. The longest median extension line of the triangle was dynamically determined to be the cutting point. The experimental results demonstrated an average cutting-point deviation of 20 mm and a valid cutting-point ratio of 69.23%. This research provides a robust visual solution for intelligent green Sichuan pepper harvesting equipment, offering both theoretical and engineering significance for advancing the automated harvesting of Sichuan pepper (Zanthoxylum schinifolium) as a specialty economic crop. Full article

(This article belongs to the Special Issue Automation Strategy Using Machine Learning in Horticultural Crop Cultivation)

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30 pages, 16384 KB

Open AccessEditor’s ChoiceArticle

Determination of Optimal Dataset Characteristics for Improving YOLO Performance in Agricultural Object Detection

by Jisu Song, Dongseok Kim, Eunji Jeong and Jaesung Park

Agriculture 2025, 15(7), 731; https://doi.org/10.3390/agriculture15070731 - 28 Mar 2025

Cited by 14 | Viewed by 3981

Abstract

Recent advances in artificial intelligence and computer vision have led to significant progress in the use of agricultural technologies for yield prediction, pest detection, and real-time monitoring of plant conditions. However, collecting large-scale, high-quality image datasets in the agriculture sector remains challenging, particularly [...] Read more.

Recent advances in artificial intelligence and computer vision have led to significant progress in the use of agricultural technologies for yield prediction, pest detection, and real-time monitoring of plant conditions. However, collecting large-scale, high-quality image datasets in the agriculture sector remains challenging, particularly for specialized datasets such as plant disease images. This study analyzed the effects of the image size (320–640+) and the number of labels on the performance of a YOLO-based object detection model using diverse agricultural datasets for strawberries, tomatoes, chilies, and peppers. Model performance was evaluated using the intersection over union and average precision (AP), where the AP curve was smoothed using the Savitzky–Golay filter and EEM. The results revealed that increasing the number of labels improved the model performance to a certain degree, after which the performance gradually diminished. Furthermore, while increasing the image size from 320 to 640 substantially enhanced the model performance, additional increases beyond 640 yielded only marginal improvements. However, the training time and graphics processing unit usage scaled linearly with increasing image sizes, as larger size images require greater computational resources. These findings underscore the importance of an optimal strategy for selecting the image size and label quantity under resource constraints in real-world model development. Full article

(This article belongs to the Special Issue Automation Strategy Using Machine Learning in Horticultural Crop Cultivation)

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