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Shah A, Attique Khan M, Ibrahim Alzahrani A, Alalwan N, Hamza A, Manic S, Zhang Y, Damaševic̆ius R. FuzzyShallow: A framework of deep shallow neural networks and modified tree growth optimization for agriculture land cover and fruit disease recognition from remote sensing and digital imaging. MEASUREMENT 2024; 237:115224. [DOI: 10.1016/j.measurement.2024.115224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/25/2024]
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Saranya T, Deisy C, Sridevi S. Efficient agricultural pest classification using vision transformer with hybrid pooled multihead attention. Comput Biol Med 2024; 177:108584. [PMID: 38788371 DOI: 10.1016/j.compbiomed.2024.108584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 03/21/2024] [Accepted: 05/08/2024] [Indexed: 05/26/2024]
Abstract
Accurate pest classification plays a pivotal role in modern agriculture for effective pest management, ensuring crop health and productivity. While Convolutional Neural Networks (CNNs) have been widely used for classification, their limited ability to capture both local and global information hinders precise pest identification. In contrast, vision transformers have shown promise in capturing global dependencies and enhancing classification performance. However, the traditional attention mechanism employed in vision transformers, which uses the same query (Q), key (K), and value (V), overlooks spatial relationships between patches, limiting the model's capacity to capture fine-grained details and long-range dependencies in the image. To address these limitations, this study presents a novel approach, termed Hybrid Pooled Multihead Attention (HPMA), for superior pest classification that outperforms both CNN models and vision transformers. The HPMA model integrates hybrid pooling techniques and modifies the attention mechanism to effectively capture local and global features within images. By emphasizing discriminative features and suppressing irrelevant information, the HPMA model achieves heightened robustness and generalization capabilities. The model is trained and tested on a newly built dataset consisting of 10 pest classes, achieving a remarkable accuracy of 98 %. Furthermore, the proposed HPMA model is validated on two benchmark datasets and achieves accuracies of 98 % and 95 %, demonstrating its effectiveness across diverse pest datasets. The results and ablation study of the proposed model contribute to exceptional performance in accurate pest classification. This tackles agricultural pest challenges and enables prompt pest control to reduce crop losses.
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Affiliation(s)
- T Saranya
- Thiagarajar College of Engineering, Madurai, India.
| | - C Deisy
- Thiagarajar College of Engineering, Madurai, India.
| | - S Sridevi
- Thiagarajar College of Engineering, Madurai, India.
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Aghamohammadesmaeilketabforoosh K, Nikan S, Antonini G, Pearce JM. Optimizing Strawberry Disease and Quality Detection with Vision Transformers and Attention-Based Convolutional Neural Networks. Foods 2024; 13:1869. [PMID: 38928810 PMCID: PMC11202458 DOI: 10.3390/foods13121869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2024] [Revised: 06/10/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
Machine learning and computer vision have proven to be valuable tools for farmers to streamline their resource utilization to lead to more sustainable and efficient agricultural production. These techniques have been applied to strawberry cultivation in the past with limited success. To build on this past work, in this study, two separate sets of strawberry images, along with their associated diseases, were collected and subjected to resizing and augmentation. Subsequently, a combined dataset consisting of nine classes was utilized to fine-tune three distinct pretrained models: vision transformer (ViT), MobileNetV2, and ResNet18. To address the imbalanced class distribution in the dataset, each class was assigned weights to ensure nearly equal impact during the training process. To enhance the outcomes, new images were generated by removing backgrounds, reducing noise, and flipping them. The performances of ViT, MobileNetV2, and ResNet18 were compared after being selected. Customization specific to the task was applied to all three algorithms, and their performances were assessed. Throughout this experiment, none of the layers were frozen, ensuring all layers remained active during training. Attention heads were incorporated into the first five and last five layers of MobileNetV2 and ResNet18, while the architecture of ViT was modified. The results indicated accuracy factors of 98.4%, 98.1%, and 97.9% for ViT, MobileNetV2, and ResNet18, respectively. Despite the data being imbalanced, the precision, which indicates the proportion of correctly identified positive instances among all predicted positive instances, approached nearly 99% with the ViT. MobileNetV2 and ResNet18 demonstrated similar results. Overall, the analysis revealed that the vision transformer model exhibited superior performance in strawberry ripeness and disease classification. The inclusion of attention heads in the early layers of ResNet18 and MobileNet18, along with the inherent attention mechanism in ViT, improved the accuracy of image identification. These findings offer the potential for farmers to enhance strawberry cultivation through passive camera monitoring alone, promoting the health and well-being of the population.
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Affiliation(s)
| | - Soodeh Nikan
- Department of Electrical & Computer Engineering, Western University, London, ON N6A 3K7, Canada
| | - Giorgio Antonini
- Department of Electrical & Computer Engineering, Western University, London, ON N6A 3K7, Canada
| | - Joshua M. Pearce
- Department of Electrical & Computer Engineering, Western University, London, ON N6A 3K7, Canada
- Ivey Business School, Western University, London, ON N6A 3K7, Canada
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Rybacki P, Niemann J, Derouiche S, Chetehouna S, Boulaares I, Seghir NM, Diatta J, Osuch A. Convolutional Neural Network (CNN) Model for the Classification of Varieties of Date Palm Fruits ( Phoenix dactylifera L.). SENSORS (BASEL, SWITZERLAND) 2024; 24:558. [PMID: 38257650 PMCID: PMC10818393 DOI: 10.3390/s24020558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/29/2023] [Accepted: 01/10/2024] [Indexed: 01/24/2024]
Abstract
The popularity and demand for high-quality date palm fruits (Phoenix dactylifera L.) have been growing, and their quality largely depends on the type of handling, storage, and processing methods. The current methods of geometric evaluation and classification of date palm fruits are characterised by high labour intensity and are usually performed mechanically, which may cause additional damage and reduce the quality and value of the product. Therefore, non-contact methods are being sought based on image analysis, with digital solutions controlling the evaluation and classification processes. The main objective of this paper is to develop an automatic classification model for varieties of date palm fruits using a convolutional neural network (CNN) based on two fundamental criteria, i.e., colour difference and evaluation of geometric parameters of dates. A CNN with a fixed architecture was built, marked as DateNET, consisting of a system of five alternating Conv2D, MaxPooling2D, and Dropout classes. The validation accuracy of the model presented in this study depended on the selection of classification criteria. It was 85.24% for fruit colour-based classification and 87.62% for the geometric parameters only; however, it increased considerably to 93.41% when both the colour and geometry of dates were considered.
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Affiliation(s)
- Piotr Rybacki
- Department of Agronomy, Poznań University of Life Sciences, Dojazd 11, 60-632 Poznań, Poland
| | - Janetta Niemann
- Department of Genetics and Plant Breeding, Poznań University of Life Sciences, Dojazd 11, 60-632 Poznań, Poland;
| | - Samir Derouiche
- Department of Cellular and Molecular Biology, Faculty of Natural Sciences and Life, University of El Oued, El Oued 39000, Algeria; (S.D.); (I.B.)
- Laboratory of Biodiversity and Application of Biotechnology in the Agricultural Field, Faculty of Natural Sciences and Life, University of El Oued, El Oued 39000, Algeria;
| | - Sara Chetehouna
- Department of Microbiology and Biochemistry, Faculty of Sciences, Mohamed Boudiaf-M’sila University, M’sila 28000, Algeria;
| | - Islam Boulaares
- Department of Cellular and Molecular Biology, Faculty of Natural Sciences and Life, University of El Oued, El Oued 39000, Algeria; (S.D.); (I.B.)
- Laboratory of Biodiversity and Application of Biotechnology in the Agricultural Field, Faculty of Natural Sciences and Life, University of El Oued, El Oued 39000, Algeria;
| | - Nili Mohammed Seghir
- Laboratory of Biodiversity and Application of Biotechnology in the Agricultural Field, Faculty of Natural Sciences and Life, University of El Oued, El Oued 39000, Algeria;
- Department of Agricultural Sciences, University of El Oued, El Oued 39000, Algeria
| | - Jean Diatta
- Department of Agricultural Chemistry and Environmental Biogeochemistry, Poznań University of Life Sciences, Ul. Wojska Polskiego 71F, 60-625 Poznań, Poland;
| | - Andrzej Osuch
- Department of Biosystems Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-637 Poznań, Poland;
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Li Y, Guo J, Qiu H, Chen F, Zhang J. Denoising Diffusion Probabilistic Models and Transfer Learning for citrus disease diagnosis. FRONTIERS IN PLANT SCIENCE 2023; 14:1267810. [PMID: 38146275 PMCID: PMC10749533 DOI: 10.3389/fpls.2023.1267810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 11/14/2023] [Indexed: 12/27/2023]
Abstract
Problems Plant Disease diagnosis based on deep learning mechanisms has been extensively studied and applied. However, the complex and dynamic agricultural growth environment results in significant variations in the distribution of state samples, and the lack of sufficient real disease databases weakens the information carried by the samples, posing challenges for accurately training models. Aim This paper aims to test the feasibility and effectiveness of Denoising Diffusion Probabilistic Models (DDPM), Swin Transformer model, and Transfer Learning in diagnosing citrus diseases with a small sample. Methods Two training methods are proposed: The Method 1 employs the DDPM to generate synthetic images for data augmentation. The Swin Transformer model is then used for pre-training on the synthetic dataset produced by DDPM, followed by fine-tuning on the original citrus leaf images for disease classification through transfer learning. The Method 2 utilizes the pre-trained Swin Transformer model on the ImageNet dataset and fine-tunes it on the augmented dataset composed of the original and DDPM synthetic images. Results and conclusion The test results indicate that Method 1 achieved a validation accuracy of 96.3%, while Method 2 achieved a validation accuracy of 99.8%. Both methods effectively addressed the issue of model overfitting when dealing with a small dataset. Additionally, when compared with VGG16, EfficientNet, ShuffleNet, MobileNetV2, and DenseNet121 in citrus disease classification, the experimental results demonstrate the superiority of the proposed methods over existing approaches to a certain extent.
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Affiliation(s)
| | - Jianwen Guo
- School of Mechanical Engineering, Dongguan University of Technology, Dongguan, Guangdong, China
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Valente J, António J, Mora C, Jardim S. Developments in Image Processing Using Deep Learning and Reinforcement Learning. J Imaging 2023; 9:207. [PMID: 37888314 PMCID: PMC10607786 DOI: 10.3390/jimaging9100207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 09/24/2023] [Accepted: 09/28/2023] [Indexed: 10/28/2023] Open
Abstract
The growth in the volume of data generated, consumed, and stored, which is estimated to exceed 180 zettabytes in 2025, represents a major challenge both for organizations and for society in general. In addition to being larger, datasets are increasingly complex, bringing new theoretical and computational challenges. Alongside this evolution, data science tools have exploded in popularity over the past two decades due to their myriad of applications when dealing with complex data, their high accuracy, flexible customization, and excellent adaptability. When it comes to images, data analysis presents additional challenges because as the quality of an image increases, which is desirable, so does the volume of data to be processed. Although classic machine learning (ML) techniques are still widely used in different research fields and industries, there has been great interest from the scientific community in the development of new artificial intelligence (AI) techniques. The resurgence of neural networks has boosted remarkable advances in areas such as the understanding and processing of images. In this study, we conducted a comprehensive survey regarding advances in AI design and the optimization solutions proposed to deal with image processing challenges. Despite the good results that have been achieved, there are still many challenges to face in this field of study. In this work, we discuss the main and more recent improvements, applications, and developments when targeting image processing applications, and we propose future research directions in this field of constant and fast evolution.
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Affiliation(s)
- Jorge Valente
- Techframe-Information Systems, SA, 2785-338 São Domingos de Rana, Portugal; (J.V.); (J.A.)
| | - João António
- Techframe-Information Systems, SA, 2785-338 São Domingos de Rana, Portugal; (J.V.); (J.A.)
| | - Carlos Mora
- Smart Cities Research Center, Polytechnic Institute of Tomar, 2300-313 Tomar, Portugal;
| | - Sandra Jardim
- Smart Cities Research Center, Polytechnic Institute of Tomar, 2300-313 Tomar, Portugal;
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