Plants

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24 pages, 2376 KiB

Open AccessArticle

An Efficient Weed Detection Method Using Latent Diffusion Transformer for Enhanced Agricultural Image Analysis and Mobile Deployment

by Yuzhuo Cui, Yingqiu Yang, Yuqing Xia, Yan Li, Zhaoxi Feng, Shiya Liu, Guangqi Yuan and Chunli Lv

Plants 2024, 13(22), 3192; https://doi.org/10.3390/plants13223192 - 13 Nov 2024

Viewed by 345

Abstract

This paper presents an efficient weed detection method based on the latent diffusion transformer, aimed at enhancing the accuracy and applicability of agricultural image analysis. The experimental results demonstrate that the proposed model achieves a precision of 0.92, a recall of 0.89, an [...] Read more.

This paper presents an efficient weed detection method based on the latent diffusion transformer, aimed at enhancing the accuracy and applicability of agricultural image analysis. The experimental results demonstrate that the proposed model achieves a precision of 0.92, a recall of 0.89, an accuracy of 0.91, a mean average precision (mAP) of 0.91, and an F1 score of 0.90, indicating its outstanding performance in complex scenarios. Additionally, ablation experiments reveal that the latent-space-based diffusion subnetwork outperforms traditional models, such as the the residual diffusion network, which has a precision of only 0.75. By combining latent space feature extraction with self-attention mechanisms, the constructed lightweight model can respond quickly on mobile devices, showcasing the significant potential of deep learning technologies in agricultural applications. Future research will focus on data diversity and model interpretability to further enhance the model’s adaptability and user trust. Full article

(This article belongs to the Special Issue The Future of Artificial Intelligence and Sensor Systems in Agriculture)

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23 pages, 1660 KiB

Open AccessArticle

A Deep Learning Model for Accurate Maize Disease Detection Based on State-Space Attention and Feature Fusion

by Tong Zhu, Fengyi Yan, Xinyang Lv, Hanyi Zhao, Zihang Wang, Keqin Dong, Zhengjie Fu, Ruihao Jia and Chunli Lv

Plants 2024, 13(22), 3151; https://doi.org/10.3390/plants13223151 - 9 Nov 2024

Viewed by 439

Abstract

In improving agricultural yields and ensuring food security, precise detection of maize leaf diseases is of great importance. Traditional disease detection methods show limited performance in complex environments, making it challenging to meet the demands for precise detection in modern agriculture. This paper [...] Read more.

In improving agricultural yields and ensuring food security, precise detection of maize leaf diseases is of great importance. Traditional disease detection methods show limited performance in complex environments, making it challenging to meet the demands for precise detection in modern agriculture. This paper proposes a maize leaf disease detection model based on a state-space attention mechanism, aiming to effectively utilize the spatiotemporal characteristics of maize leaf diseases to achieve efficient and accurate detection. The model introduces a state-space attention mechanism combined with a multi-scale feature fusion module to capture the spatial distribution and dynamic development of maize diseases. In experimental comparisons, the proposed model demonstrates superior performance in the task of maize disease detection, achieving a precision, recall, accuracy, and F1 score of 0.94. Compared with baseline models such as AlexNet, GoogLeNet, ResNet, EfficientNet, and ViT, the proposed method achieves a precision of 0.95, with the other metrics also reaching 0.94, showing significant improvement. Additionally, ablation experiments verify the impact of different attention mechanisms and loss functions on model performance. The standard self-attention model achieved a precision, recall, accuracy, and F1 score of 0.74, 0.70, 0.72, and 0.72, respectively. The Convolutional Block Attention Module (CBAM) showed a precision of 0.87, recall of 0.83, accuracy of 0.85, and F1 score of 0.85, while the state-space attention module achieved a precision of 0.95, with the other metrics also at 0.94. In terms of loss functions, cross-entropy loss showed a precision, recall, accuracy, and F1 score of 0.69, 0.65, 0.67, and 0.67, respectively. Focal loss showed a precision of 0.83, recall of 0.80, accuracy of 0.81, and F1 score of 0.81. State-space loss demonstrated the best performance in these experiments, achieving a precision of 0.95, with recall, accuracy, and F1 score all at 0.94. These results indicate that the model based on the state-space attention mechanism achieves higher detection accuracy and better generalization ability in the task of maize leaf disease detection, effectively improving the accuracy and efficiency of disease recognition and providing strong technical support for the early diagnosis and management of maize diseases. Future work will focus on further optimizing the model’s spatiotemporal feature modeling capabilities and exploring multi-modal data fusion to enhance the model’s application in real agricultural scenarios. Full article

(This article belongs to the Special Issue The Future of Artificial Intelligence and Sensor Systems in Agriculture)

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23 pages, 1081 KiB

Open AccessArticle

Implementing Real-Time Image Processing for Radish Disease Detection Using Hybrid Attention Mechanisms

by Mengxue Ji, Zizhe Zhou, Xinyue Wang, Weidong Tang, Yan Li, Yilin Wang, Chaoyu Zhou and Chunli Lv

Plants 2024, 13(21), 3001; https://doi.org/10.3390/plants13213001 - 27 Oct 2024

Viewed by 492

Abstract

This paper developed a radish disease detection system based on a hybrid attention mechanism, significantly enhancing the precision and real-time performance in identifying disease characteristics. By integrating spatial and channel attentions, this system demonstrated superior performance across numerous metrics, particularly achieving 93% precision [...] Read more.

This paper developed a radish disease detection system based on a hybrid attention mechanism, significantly enhancing the precision and real-time performance in identifying disease characteristics. By integrating spatial and channel attentions, this system demonstrated superior performance across numerous metrics, particularly achieving 93% precision and 91% accuracy in detecting radish virus disease, outperforming existing technologies. Additionally, the introduction of the hybrid attention mechanism proved its superiority in ablation experiments, showing higher performance compared to standard self-attention and the convolutional block attention module. The study also introduced a hybrid loss function that combines cross-entropy loss and Dice loss, effectively addressing the issue of class imbalance and further enhancing the detection capability for rare diseases. These experimental results not only validate the effectiveness of the proposed method, but also provide robust technical support for the rapid and accurate detection of radish diseases, demonstrating its vast potential in agricultural applications. Future research will continue to optimize the model structure and computational efficiency to accommodate a broader range of agricultural disease detection needs. Full article

(This article belongs to the Special Issue The Future of Artificial Intelligence and Sensor Systems in Agriculture)

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21 pages, 796 KiB

Open AccessArticle

High-Performance Grape Disease Detection Method Using Multimodal Data and Parallel Activation Functions

by Ruiheng Li, Jiarui Liu, Binqin Shi, Hanyi Zhao, Yan Li, Xinran Zheng, Chao Peng and Chunli Lv

Plants 2024, 13(19), 2720; https://doi.org/10.3390/plants13192720 - 28 Sep 2024

Cited by 1 | Viewed by 697

Abstract

This paper introduces a novel deep learning model for grape disease detection that integrates multimodal data and parallel heterogeneous activation functions, significantly enhancing detection accuracy and robustness. Through experiments, the model demonstrated excellent performance in grape disease detection, achieving an accuracy of 91%, [...] Read more.

This paper introduces a novel deep learning model for grape disease detection that integrates multimodal data and parallel heterogeneous activation functions, significantly enhancing detection accuracy and robustness. Through experiments, the model demonstrated excellent performance in grape disease detection, achieving an accuracy of 91%, a precision of 93%, a recall of 90%, a mean average precision (mAP) of 91%, and 56 frames per second (FPS), outperforming traditional deep learning models such as YOLOv3, YOLOv5, DEtection TRansformer (DETR), TinySegformer, and Tranvolution-GAN. To meet the demands of rapid on-site detection, this study also developed a lightweight model for mobile devices, successfully deployed on the iPhone 15. Techniques such as structural pruning, quantization, and depthwise separable convolution were used to significantly reduce the model’s computational complexity and resource consumption, ensuring efficient operation and real-time performance. These achievements not only advance the development of smart agricultural technologies but also provide new technical solutions and practical tools for disease detection. Full article

(This article belongs to the Special Issue The Future of Artificial Intelligence and Sensor Systems in Agriculture)

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23 pages, 3738 KiB

Open AccessArticle

Integration of Diffusion Transformer and Knowledge Graph for Efficient Cucumber Disease Detection in Agriculture

by Ruiheng Li, Xiaotong Su, Hang Zhang, Xiyan Zhang, Yifan Yao, Shutian Zhou, Bohan Zhang, Muyang Ye and Chunli Lv

Plants 2024, 13(17), 2435; https://doi.org/10.3390/plants13172435 - 31 Aug 2024

Viewed by 1002

Abstract

In this study, a deep learning method combining knowledge graph and diffusion Transformer has been proposed for cucumber disease detection. By incorporating the diffusion attention mechanism and diffusion loss function, the research aims to enhance the model’s ability to recognize complex agricultural disease [...] Read more.

In this study, a deep learning method combining knowledge graph and diffusion Transformer has been proposed for cucumber disease detection. By incorporating the diffusion attention mechanism and diffusion loss function, the research aims to enhance the model’s ability to recognize complex agricultural disease features and to address the issue of sample imbalance efficiently. Experimental results demonstrate that the proposed method outperforms existing deep learning models in cucumber disease detection tasks. Specifically, the method achieved a precision of 93%, a recall of 89%, an accuracy of 92%, and a mean average precision (mAP) of 91%, with a frame rate of 57 frames per second (FPS). Additionally, the study successfully implemented model lightweighting, enabling effective operation on mobile devices, which supports rapid on-site diagnosis of cucumber diseases. The research not only optimizes the performance of cucumber disease detection, but also opens new possibilities for the application of deep learning in the field of agricultural disease detection. Full article

(This article belongs to the Special Issue The Future of Artificial Intelligence and Sensor Systems in Agriculture)

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24 pages, 2249 KiB

Open AccessArticle

Enhancing Jujube Forest Growth Estimation and Disease Detection Using a Novel Diffusion-Transformer Architecture

by Xiangyi Hu, Zhihao Zhang, Liping Zheng, Tailai Chen, Chao Peng, Yilin Wang, Ruiheng Li, Xinyang Lv and Shuo Yan

Plants 2024, 13(17), 2348; https://doi.org/10.3390/plants13172348 - 23 Aug 2024

Cited by 1 | Viewed by 704

Abstract

This paper proposes an advanced deep learning model that integrates the Diffusion-Transformer structure and parallel attention mechanism for the tasks of growth estimation and disease detection in jujube forests. Existing methods in forestry monitoring often fall short in meeting the practical needs of [...] Read more.

This paper proposes an advanced deep learning model that integrates the Diffusion-Transformer structure and parallel attention mechanism for the tasks of growth estimation and disease detection in jujube forests. Existing methods in forestry monitoring often fall short in meeting the practical needs of large-scale and highly complex forest areas due to limitations in data processing capabilities and feature extraction precision. In response to this challenge, this paper designs and conducts a series of benchmark tests and ablation experiments to systematically evaluate and verify the performance of the proposed model across key performance metrics such as precision, recall, accuracy, and F1-score. Experimental results demonstrate that compared to traditional machine learning models like Support Vector Machines and Random Forests, as well as common deep learning models such as AlexNet and ResNet, the model proposed in this paper achieves a precision of 95%, a recall of 92%, an accuracy of 93%, and an F1-score of 94% in the task of disease detection in jujube forests, showing similarly superior performance in growth estimation tasks as well. Furthermore, ablation experiments with different attention mechanisms and loss functions further validate the effectiveness of parallel attention and parallel loss function in enhancing the overall performance of the model. These research findings not only provide a new technical path for forestry disease monitoring and health assessment but also contribute rich theoretical and experimental foundations for related fields. Full article

(This article belongs to the Special Issue The Future of Artificial Intelligence and Sensor Systems in Agriculture)

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27 pages, 18580 KiB

Open AccessArticle

YOLOv5s-BiPCNeXt, a Lightweight Model for Detecting Disease in Eggplant Leaves

by Zhedong Xie, Chao Li, Zhuang Yang, Zhen Zhang, Jiazhuo Jiang and Hongyu Guo

Plants 2024, 13(16), 2303; https://doi.org/10.3390/plants13162303 - 19 Aug 2024

Viewed by 974

Abstract

Ensuring the healthy growth of eggplants requires the precise detection of leaf diseases, which can significantly boost yield and economic income. Improving the efficiency of plant disease identification in natural scenes is currently a crucial issue. This study aims to provide an efficient [...] Read more.

Ensuring the healthy growth of eggplants requires the precise detection of leaf diseases, which can significantly boost yield and economic income. Improving the efficiency of plant disease identification in natural scenes is currently a crucial issue. This study aims to provide an efficient detection method suitable for disease detection in natural scenes. A lightweight detection model, YOLOv5s-BiPCNeXt, is proposed. This model utilizes the MobileNeXt backbone to reduce network parameters and computational complexity and includes a lightweight C3-BiPC neck module. Additionally, a multi-scale cross-spatial attention mechanism (EMA) is integrated into the neck network, and the nearest neighbor interpolation algorithm is replaced with the content-aware feature recombination operator (CARAFE), enhancing the model’s ability to perceive multidimensional information and extract multiscale disease features and improving the spatial resolution of the disease feature map. These improvements enhance the detection accuracy for eggplant leaves, effectively reducing missed and incorrect detections caused by complex backgrounds and improving the detection and localization of small lesions at the early stages of brown spot and powdery mildew diseases. Experimental results show that the YOLOv5s-BiPCNeXt model achieves an average precision (AP) of 94.9% for brown spot disease, 95.0% for powdery mildew, and 99.5% for healthy leaves. Deployed on a Jetson Orin Nano edge detection device, the model attains an average recognition speed of 26 FPS (Frame Per Second), meeting real-time requirements. Compared to other algorithms, YOLOv5s-BiPCNeXt demonstrates superior overall performance, accurately detecting plant diseases under natural conditions and offering valuable technical support for the prevention and treatment of eggplant leaf diseases. Full article

(This article belongs to the Special Issue The Future of Artificial Intelligence and Sensor Systems in Agriculture)

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12 pages, 4434 KiB

Open AccessArticle

Agronomic and Technical Evaluation of Herbicide Spot Spraying in Maize Based on High-Resolution Aerial Weed Maps—An On-Farm Trial

by Alicia Allmendinger, Michael Spaeth, Marcus Saile, Gerassimos G. Peteinatos and Roland Gerhards

Plants 2024, 13(15), 2164; https://doi.org/10.3390/plants13152164 - 5 Aug 2024

Viewed by 840

Abstract

Spot spraying can significantly reduce herbicide use while maintaining equal weed control efficacy as a broadcast application of herbicides. Several online spot-spraying systems have been developed, with sensors mounted on the sprayer or by recording the RTK-GNSS position of each crop seed. In [...] Read more.

Spot spraying can significantly reduce herbicide use while maintaining equal weed control efficacy as a broadcast application of herbicides. Several online spot-spraying systems have been developed, with sensors mounted on the sprayer or by recording the RTK-GNSS position of each crop seed. In this study, spot spraying was realized offline based on georeferenced unmanned aerial vehicle (UAV) images with high spatial resolution. Studies were conducted in four maize fields in Southwestern Germany in 2023. A randomized complete block design was used with seven treatments containing broadcast and spot applications of pre-emergence and post-emergence herbicides. Post-emergence herbicides were applied at 2–4-leaf and at 6–8-leaf stages of maize. Weed and crop density, weed control efficacy (WCE), crop losses, accuracy of weed classification in UAV images, herbicide savings and maize yield were measured and analyzed. On average, 94% of all weed plants were correctly identified in the UAV images with the automatic classifier. Spot-spraying achieved up to 86% WCE, which was equal to the broadcast herbicide treatment. Early spot spraying saved 47% of herbicides compared to the broadcast herbicide application. Maize yields in the spot-spraying plots were equal to the broadcast herbicide application plots. This study demonstrates that spot-spraying based on UAV weed maps is feasible and provides a significant reduction in herbicide use. Full article

(This article belongs to the Special Issue The Future of Artificial Intelligence and Sensor Systems in Agriculture)

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28 pages, 6209 KiB

Open AccessArticle

Implementation and Evaluation of Spatial Attention Mechanism in Apricot Disease Detection Using Adaptive Sampling Latent Variable Network

by Bingyuan Han, Peiyan Duan, Chengcheng Zhou, Xiaotong Su, Ziyan Yang, Shutian Zhou, Mengxue Ji, Yucen Xie, Jianjun Chen and Chunli Lv

Plants 2024, 13(12), 1681; https://doi.org/10.3390/plants13121681 - 18 Jun 2024

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Abstract

In this study, an advanced method for apricot tree disease detection is proposed that integrates deep learning technologies with various data augmentation strategies to significantly enhance the accuracy and efficiency of disease detection. A comprehensive framework based on the adaptive sampling latent variable [...] Read more.

In this study, an advanced method for apricot tree disease detection is proposed that integrates deep learning technologies with various data augmentation strategies to significantly enhance the accuracy and efficiency of disease detection. A comprehensive framework based on the adaptive sampling latent variable network (ASLVN) and the spatial state attention mechanism was developed with the aim of enhancing the model’s capability to capture characteristics of apricot tree diseases while ensuring its applicability on edge devices through model lightweighting techniques. Experimental results demonstrated significant improvements in precision, recall, accuracy, and mean average precision (mAP). Specifically, precision was

0.92

, recall was

0.89

, accuracy was

0.90

, and mAP was

0.91

, surpassing traditional models such as YOLOv5, YOLOv8, RetinaNet, EfficientDet, and DEtection TRansformer (DETR). Furthermore, through ablation studies, the critical roles of ASLVN and the spatial state attention mechanism in enhancing detection performance were validated. These experiments not only showcased the contributions of each component for improving model performance but also highlighted the method’s capability to address the challenges of apricot tree disease detection in complex environments. Eight types of apricot tree diseases were detected, including Powdery Mildew and Brown Rot, representing a technological breakthrough. The findings provide robust technical support for disease management in actual agricultural production and offer broad application prospects. Full article

(This article belongs to the Special Issue The Future of Artificial Intelligence and Sensor Systems in Agriculture)

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27 pages, 7676 KiB

Open AccessArticle

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

by Yuchun Lu, Xiaoyi Lu, Liping Zheng, Min Sun, Siyu Chen, Baiyan Chen, Tong Wang, Jiming Yang and Chunli Lv

Plants 2024, 13(7), 972; https://doi.org/10.3390/plants13070972 - 28 Mar 2024

Cited by 5 | Viewed by 2443

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue The Future of Artificial Intelligence and Sensor Systems in Agriculture)

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12 pages, 1155 KiB

Open AccessArticle

Using Machine Learning Methods Combined with Vegetation Indices and Growth Indicators to Predict Seed Yield of Bromus inermis

by Chengming Ou, Zhicheng Jia, Shoujiang Sun, Jingyu Liu, Wen Ma, Juan Wang, Chunjiao Mi and Peisheng Mao

Plants 2024, 13(6), 773; https://doi.org/10.3390/plants13060773 - 8 Mar 2024

Cited by 1 | Viewed by 1038

Abstract

Smooth bromegrass (Bromus inermis) is a perennial, high-quality forage grass. However, its seed yield is influenced by agronomic practices, climatic conditions, and the growing year. The rapid and effective prediction of seed yield can assist growers in making informed production decisions [...] Read more.

Smooth bromegrass (Bromus inermis) is a perennial, high-quality forage grass. However, its seed yield is influenced by agronomic practices, climatic conditions, and the growing year. The rapid and effective prediction of seed yield can assist growers in making informed production decisions and reducing agricultural risks. Our field trial design followed a completely randomized block design with four blocks and three nitrogen levels (0, 100, and 200 kg·N·ha⁻¹) during 2022 and 2023. Data on the remote vegetation index (RVI), the normalized difference vegetation index (NDVI), the leaf nitrogen content (LNC), and the leaf area index (LAI) were collected at heading, anthesis, and milk stages. Multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) regression models were utilized to predict seed yield. In 2022, the results indicated that nitrogen application provided a sufficiently large range of variation of seed yield (ranging from 45.79 to 379.45 kg ha⁻¹). Correlation analysis showed that the indices of the RVI, the NDVI, the LNC, and the LAI in 2022 presented significant positive correlation with seed yield, and the highest correlation coefficient was observed at the heading stage. The data from 2022 were utilized to formulate a predictive model for seed yield. The results suggested that utilizing data from the heading stage produced the best prediction performance. SVM and RF outperformed MLR in prediction, with RF demonstrating the highest performance (R² = 0.75, RMSE = 51.93 kg ha⁻¹, MAE = 29.43 kg ha⁻¹, and MAPE = 0.17). Notably, the accuracy of predicting seed yield for the year 2023 using this model had decreased. Feature importance analysis of the RF model revealed that LNC was a crucial indicator for predicting smooth bromegrass seed yield. Further studies with an expanded dataset and integration of weather data are needed to improve the accuracy and generalizability of the model and adaptability for the growing year. Full article

(This article belongs to the Special Issue The Future of Artificial Intelligence and Sensor Systems in Agriculture)

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17 pages, 3866 KiB

Open AccessArticle

AI-Enabled Crop Management Framework for Pest Detection Using Visual Sensor Data

by Asma Khan, Sharaf J. Malebary, L. Minh Dang, Faisal Binzagr, Hyoung-Kyu Song and Hyeonjoon Moon

Plants 2024, 13(5), 653; https://doi.org/10.3390/plants13050653 - 27 Feb 2024

Cited by 3 | Viewed by 4276

Abstract

Our research focuses on addressing the challenge of crop diseases and pest infestations in agriculture by utilizing UAV technology for improved crop monitoring through unmanned aerial vehicles (UAVs) and enhancing the detection and classification of agricultural pests. Traditional approaches often require arduous manual [...] Read more.

Our research focuses on addressing the challenge of crop diseases and pest infestations in agriculture by utilizing UAV technology for improved crop monitoring through unmanned aerial vehicles (UAVs) and enhancing the detection and classification of agricultural pests. Traditional approaches often require arduous manual feature extraction or computationally demanding deep learning (DL) techniques. To address this, we introduce an optimized model tailored specifically for UAV-based applications. Our alterations to the YOLOv5s model, which include advanced attention modules, expanded cross-stage partial network (CSP) modules, and refined multiscale feature extraction mechanisms, enable precise pest detection and classification. Inspired by the efficiency and versatility of UAVs, our study strives to revolutionize pest management in sustainable agriculture while also detecting and preventing crop diseases. We conducted rigorous testing on a medium-scale dataset, identifying five agricultural pests, namely ants, grasshoppers, palm weevils, shield bugs, and wasps. Our comprehensive experimental analysis showcases superior performance compared to various YOLOv5 model versions. The proposed model obtained higher performance, with an average precision of 96.0%, an average recall of 93.0%, and a mean average precision (mAP) of 95.0%. Furthermore, the inherent capabilities of UAVs, combined with the YOLOv5s model tested here, could offer a reliable solution for real-time pest detection, demonstrating significant potential to optimize and improve agricultural production within a drone-centric ecosystem. Full article

(This article belongs to the Special Issue The Future of Artificial Intelligence and Sensor Systems in Agriculture)

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