A Dilated Segmentation Network with the Morphological Correction Method in Farming Area Image Series

Lin, Xingyi; Wa, Shiyun; Zhang, Yan; Qian, Ma

doi:10.3390/rs14081771

Cited by 10 publications

(4 citation statements)

References 13 publications

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“…In response to these challenges, the urgency of incorporating intelligent technologies for accurate and rapid detection of plant diseases is evident [9]. In this context, this study introduces a disease detection and agricultural question-answering system based on multimodal and large language model technologies [10], aimed at enhancing the level of agricultural production intelligence and providing effective decision support for agricultural workers.…”

Section: Introductionmentioning

confidence: 99%

Application of Multimodal Transformer Model in Intelligent Agricultural Disease Detection and Question-Answering Systems

Lu,

Zheng

et al. 2024

Plants

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In this study, an innovative approach based on multimodal data and the transformer model was proposed to address challenges in agricultural disease detection and question-answering systems. This method effectively integrates image, text, and sensor data, utilizing deep learning technologies to profoundly analyze and process complex agriculture-related issues. The study achieved technical breakthroughs and provides new perspectives and tools for the development of intelligent agriculture. In the task of agricultural disease detection, the proposed method demonstrated outstanding performance, achieving a precision, recall, and accuracy of 0.95, 0.92, and 0.94, respectively, significantly outperforming the other conventional deep learning models. These results indicate the method’s effectiveness in identifying and accurately classifying various agricultural diseases, particularly excelling in handling subtle features and complex data. In the task of generating descriptive text from agricultural images, the method also exhibited impressive performance, with a precision, recall, and accuracy of 0.92, 0.88, and 0.91, respectively. This demonstrates that the method can not only deeply understand the content of agricultural images but also generate accurate and rich descriptive texts. The object detection experiment further validated the effectiveness of our approach, where the method achieved a precision, recall, and accuracy of 0.96, 0.91, and 0.94. This achievement highlights the method’s capability for accurately locating and identifying agricultural targets, especially in complex environments. Overall, the approach in this study not only demonstrated exceptional performance in multiple tasks such as agricultural disease detection, image captioning, and object detection but also showcased the immense potential of multimodal data and deep learning technologies in the application of intelligent agriculture.

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Section: Introductionmentioning

confidence: 99%

Application of Multimodal Transformer Model in Intelligent Agricultural Disease Detection and Question-Answering Systems

Lu,

Zheng

et al. 2024

Plants

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“…With global agricultural production facing increasing challenges [1,2], effective pest control and management have become key factors in enhancing crop yields [3] and ensuring food safety [4]. Fruit flies, as widely distributed agricultural pests [5], cause significant damage to fruits and vegetables during their adult developmental stage [6].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Fruit Fly Detection in Complex Backgrounds Using Transformer Architecture with Step Attention Mechanism

Zhang,

Chen,

Zheng

et al. 2024

Agriculture

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This study introduces a novel high-accuracy fruit fly detection model based on the Transformer structure, specifically aimed at addressing the unique challenges in fruit fly detection such as identification of small targets and accurate localization against complex backgrounds. By integrating a step attention mechanism and a cross-loss function, this model significantly enhances the recognition and localization of fruit flies within complex backgrounds, particularly improving the model’s effectiveness in handling small-sized targets and its adaptability under varying environmental conditions. Experimental results demonstrate that the model achieves a precision of 0.96, a recall rate of 0.95, an accuracy of 0.95, and an F1-score of 0.95 on the fruit fly detection task, significantly outperforming leading object detection models such as YOLOv8 and DETR. Specifically, this research delves into and optimizes for challenges faced in fruit fly detection, such as recognition issues under significant light variation, small target size, and complex backgrounds. Through ablation experiments comparing different data augmentation techniques and model configurations, the critical contributions of the step attention mechanism and cross-loss function to enhancing model performance under these complex conditions are further validated. These achievements not only highlight the innovativeness and effectiveness of the proposed method, but also provide robust technical support for solving practical fruit fly detection problems in real-world applications, paving new paths for future research in object detection technology.

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“…In recent years, deep learning has made considerable contributions in fields such as agriculture [1,2], healthcare [3,4], energy usage [5], and finance [6]. This development provides a new solution for the time series prediction problem.…”

Section: Introductionmentioning

confidence: 99%

Time-Series Neural Network: A High-Accuracy Time-Series Forecasting Method Based on Kernel Filter and Time Attention

Zhang,

Wang,

et al. 2023

Information

Self Cite

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This research introduces a novel high-accuracy time-series forecasting method, namely the Time Neural Network (TNN), which is based on a kernel filter and time attention mechanism. Taking into account the complex characteristics of time-series data, such as non-linearity, high dimensionality, and long-term dependence, the TNN model is designed and implemented. The key innovations of the TNN model lie in the incorporation of the time attention mechanism and kernel filter, allowing the model to allocate different weights to features at each time point, and extract high-level features from the time-series data, thereby improving the model’s predictive accuracy. Additionally, an adaptive weight generator is integrated into the model, enabling the model to automatically adjust weights based on input features. Mainstream time-series forecasting models such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory Networks (LSTM) are employed as baseline models and comprehensive comparative experiments are conducted. The results indicate that the TNN model significantly outperforms the baseline models in both long-term and short-term prediction tasks. Specifically, the RMSE, MAE, and R2 reach 0.05, 0.23, and 0.95, respectively. Remarkably, even for complex time-series data that contain a large amount of noise, the TNN model still maintains a high prediction accuracy.

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A Dilated Segmentation Network with the Morphological Correction Method in Farming Area Image Series

Cited by 10 publications

References 13 publications

Application of Multimodal Transformer Model in Intelligent Agricultural Disease Detection and Question-Answering Systems

Application of Multimodal Transformer Model in Intelligent Agricultural Disease Detection and Question-Answering Systems

Enhancing Fruit Fly Detection in Complex Backgrounds Using Transformer Architecture with Step Attention Mechanism

Time-Series Neural Network: A High-Accuracy Time-Series Forecasting Method Based on Kernel Filter and Time Attention

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