Optimizing Potato Leaf Disease Recognition: Insights DENSE-NET-121 and Gaussian Elimination Filter Fusion

Heliyon, Год журнала: 2025, Номер unknown, С. e42318 - e42318

Опубликована: Янв. 1, 2025

Язык: Английский

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A Detection Method for Sweet Potato Leaf Spot Disease and Leaf-Eating Pests

Agriculture, Год журнала: 2025, Номер 15(5), С. 503 - 503

Опубликована: Фев. 26, 2025

Traditional sweet potato disease and pest detection methods have the limitations of low efficiency, poor accuracy manual dependence, while deep learning-based target can achieve an efficient accurate detection. This paper proposed leaf method SPLDPvB, as well a low-complexity version SPLDPvT, to identification spots pests, such hawk moth wheat moth. First, residual module containing three depthwise separable convolutional layers skip connection was effectively retain key feature information. Then, extraction integrating attention mechanism designed significantly improve capability. Finally, in model architecture, only structure backbone network decoupling head combination retained, traditional replaced by module, which greatly reduced complexity. The experimental results showed that mAP0.5 mAP0.5:0.95 SPLDPvB were 88.7% 74.6%, respectively, number parameters amount calculation 1.1 M 7.7 G, respectively. Compared with YOLOv11S, increased 2.3% 2.8%, 88.2% 63.8%, achieves higher complexity, demonstrating excellent performance detecting pests diseases. realizes automatic diseases provides technical guidance for spraying

Язык: Английский

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Past, present and future of deep plant leaf disease recognition: A survey

Computers and Electronics in Agriculture, Год журнала: 2025, Номер 234, С. 110128 - 110128

Опубликована: Март 12, 2025

Язык: Английский

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Attention mechanism‐based ultralightweight deep learning method for automated multi‐fruit disease recognition system

Agronomy Journal, Год журнала: 2025, Номер 117(2)

Опубликована: Март 1, 2025

Abstract Automated disease recognition plays a pivotal role in advancing smart artificial intelligence (AI)‐based agriculture and is crucial for achieving higher crop yields. Although substantial research has been conducted on deep learning‐based automated plant systems, these efforts have predominantly focused leaf diseases while neglecting affecting fruits. We propose an efficient architecture effective fruit with state‐of‐the‐art performance to address this gap. Our method integrates advanced techniques, such as multi‐head attention mechanisms lightweight convolutions, enhance both efficiency performance. Its ultralightweight design emphasizes minimizing computational costs, ensuring compatibility memory‐constrained edge devices, enhancing accessibility practical usability. Experimental evaluations were three diverse datasets containing multi‐class images of disease‐affected healthy samples sugar apple ( Annona squamosa ), pomegranate Punica granatum guava Psidium guajava ). proposed model attained exceptional results test set accuracies weighted precision, recall, f1‐scores exceeding 99%, which also outperformed pretrain large‐scale models. Combining high accuracy represents significant step forward developing accessible AI solutions agriculture, contributing the advancement sustainable agriculture.

Язык: Английский

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High precision banana variety identification using vision transformer based feature extraction and support vector machine

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 26, 2025

Bananas, renowned for their delightful flavor, exceptional nutritional value, and digestibility, are among the most widely consumed fruits globally. The advent of advanced image processing, computer vision, deep learning (DL) techniques has revolutionized agricultural diagnostics, offering innovative automated solutions detecting classifying fruit varieties. Despite significant progress in DL, accurate classification banana varieties remains challenging, particularly due to difficulty identifying subtle features at early developmental stages. To address these challenges, this study presents a novel hybrid framework that integrates Vision Transformer (ViT) model global semantic feature representation with robust capabilities Support Vector Machines. proposed was rigorously evaluated on two datasets: four-class BananaImageBD six-class BananaSet. mitigate data imbalance issues, evaluation strategy employed, resulting remarkable accuracy rate (CAR) 99.86% $$\:\pm\:$$ 0.099 BananaSet 99.70% 0.17 BananaImageBD, surpassing traditional methods by margin 1.77%. ViT model, leveraging self-supervised semi-supervised mechanisms, demonstrated promise extracting nuanced critical applications. By combining cutting-edge machine classifiers, system establishes new benchmark precision reliability detection These findings underscore potential DL frameworks advancing diagnostics pave way future innovations domain.

Язык: Английский

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Bayesian Ensemble Model with Detection of Potential Misclassification of Wax Bloom in Blueberry Images

Agronomy, Год журнала: 2025, Номер 15(4), С. 809 - 809

Опубликована: Март 25, 2025

Identifying blueberry characteristics such as the wax bloom is an important task that not only helps in phenotyping (for novel variety development) but also classifying berries better suited for commercialization. Deep learning techniques image analysis have long demonstrated their capability solving classification problems. However, they usually rely on large architectures could be difficult to implement field due high computational needs. This paper presents a small (only 1502 parameters) Bayesian–CNN ensemble architecture can implemented any electronic device and able classify content images. The Bayesian model was using Keras libraries consists of two convolutional layers (eight four filters, respectively) dense layer. It includes statistical module with metrics combines results detect potential misclassifications. first metric based Euclidean distance (L2) between Gaussian mixture models while second quantile binary class predictions. Both attempt establish whether find good prediction or not. Three experiments were performed: first, compared state-of-the-art architectures. In experiment 2, detecting misclassifications evaluated similar derived from literature. Experiment 3 reports cross validation compares performance considering trade-off accuracy number samples considered potentially misclassified (not classified). show competitive state art are improve 96.98% 98.72±0.54% 98.38±0.34% L2 r2 metrics, respectively.

Язык: Английский

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Fast and Accurate Detection of Forty Types of Fruits and Vegetables: Dataset and Method

Agriculture, Год журнала: 2025, Номер 15(7), С. 760 - 760

Опубликована: Апрель 1, 2025

Accurate detection of fruits and vegetables is a key task in agricultural automation. However, existing methods typically focus on identifying single type fruit or vegetable are not equipped to handle complex diverse environments. To address this, we introduce the first large-scale benchmark dataset for detection—FV40. This contains 14,511 images, covering 40 different categories vegetables, with over 100,000 annotated bounding boxes. Additionally, propose novel framework detection—FVRT-DETR. Based Transformer architecture, this features an end-to-end real-time algorithm. FVRT-DETR enhances feature extraction by integrating Mamba backbone network improves performance objects varying scales through design multi-scale deep fusion encoder (MDFF encoder) module. Extensive experiments show that performs excellently FV40 dataset. In particular, it demonstrates significant advantage small under scenarios. Compared state-of-the-art algorithms, such as YOLOv10, achieves better results across multiple metrics. The provide efficient scalable solution detection, offering academic value practical application potential.

Язык: Английский

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Adoption of Drone, Sensor, and Robotic Technologies in Organic Farming Systems of Visegrad Countries

Heliyon, Год журнала: 2024, Номер 11(1), С. e41408 - e41408

Опубликована: Дек. 20, 2024

Язык: Английский

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Designing of Lightweight Deep Learning Framework for Plant Disease Detection

SN Computer Science, Год журнала: 2024, Номер 5(6)

Опубликована: Авг. 2, 2024

Язык: Английский

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Vision foundation model for agricultural applications with efficient layer aggregation network

Expert Systems with Applications, Год журнала: 2024, Номер 257, С. 124972 - 124972

Опубликована: Авг. 10, 2024

Язык: Английский

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