Jul 2026· Dandao Xuebao/Journal of Ballistics· Vol 38, pp. 224-236· 0 citations
TL;DR
A hybrid model, Hybrid plant disease classification using deep learning models, which uses a Convolutional Neural Network to obtain local features of the affected plant leaves and a pre-trained Vision Transformer to get global features is proposed.
Abstract
Plant diseases are one of the many factors which reduce agricultural productivity and global food security. Accurate and early diagnosis of plant diseases helps to reduce significant losses to crops and aid in sustainable agriculture. In recent years, deep learning methods for plant disease diagnosis have been of great interest in the field of agriculture. This study proposes a hybrid model, Hybrid plant disease classification using deep learning models, which uses a Convolutional Neural Network (CNN) to obtain local features of the affected plant leaves and a pre-trained Vision Transformer (ViT) to get global features. In this work, the proposed hybrid model is validated using 15 plant disease classes of which different data augmentation techniques such as changing the light exposure and object orientation are employed. Experimental results revealed the convergence stability of the model, the strong generalization ability, and the better accuracy as compared with each of the models used independently.
Plant leaf diseases are known to affect agricultural productivity and food security on a global level. "Therefore, the detection and diagnosis of diseases are important aspects of maintaining the health of crops on a sustainable level. Traditionally, the detection of diseases in plants is performed manually by experts. This process is considered to be a tedious and time-consuming task due to the chances of human error during the process. To overcome the challenges of traditional methods of detecting and diagnosing diseases in plants, a deep learning-based system is proposed in this research study to detect and classify diseases in plants. This system uses the object detection model YOLOv8x and YOLOv10x to detect the objects in the images and classify the images accordingly. Deep learning models are used to classify the images of the plants. This study uses various deep learning models like the convolutional neural network model ResNet50 and EfficientNet, and the transformer model Vision Transformer and Swin Transformer. Moreover, a hybrid model is proposed in this study by combining the transformer and convolutional neural network model to improve the efficiency of the system in detecting and classifying the diseases of the plants. This system uses the PlantVillage dataset to classify the images of the plants and detect the diseases accordingly. From the results obtained in this study, it can be observed that the proposed system is highly efficient in detecting and classifying the diseases of the plants with the help of the transformer and hybrid model.
The proposed AGO-CNN–Transformer framework provides an effective and computationally feasible solution for intelligent maize disease diagnosis and precision agriculture applications.
Akhilesh Kumar, Ashish Kumar Pandey, L. S. Umrao· Journal of Crop Health· 0 citations
This research presents the evaluation of plant leaf disease detection system based on a convolutional neural network (CNN) optimized with different nature-inspired algorithms, and indicates that application of the grey wolf optimizer (GWO) for tuning key hyperparameters of the model produced the best results.
Two deep learning models, a custom CNN model and a transfer learning model based on the DenseNet-121 architecture are presented, showing that the developed transfer learning model based on DenseNet-121 had superior performance over the CNN model.
H. Jeiad, S. Samaan, Omar Janeh et al.· Automation· 0 citations
A deep hybrid Convolutional Neural Network –Transformer architecture is introduced by combining ConvNeXt Large (ConvNeXt-L) and Swin Transformer architecture by combining ConvNeXt Large (ConvNeXt-L) and Swin Transformer (as local feature extractor) and Swin Transformer (as global context encoder) to predict tomato leaf diseases.
The timely detection of plant health status is essential to obtaining several advantages, such as increasing crop yield, reducing the toxic pollutants used in crops, enhancing healthy crops, and enhancing economic returns. A computer-aided plant status identification system enables plant health identification using plant leaves because plant leaves are the most prominent in the plant. Deep learning (DL) and convolutional neural networks (CNN) are distinguished in the area of plant health identification. Still, CNN cannot handle images of variable size and fails to extract features efficiently if the image has complex backgrounds. To avoid these problems, this research proposes a hybrid model for plant (groundnut) illness status identification using the CNN and vision transformer (HCVT). CNN is efficient in identifying local features, and ViTs are efficient in identifying global features from leaf images; leveraging these advantages enhances the model's efficacy. The HCVT model used the two datasets, groundnut and rice datasets, for experimentation. The HCVT model obtained an accuracy of 96.40% on the groundnut leaf image dataset, with 99.86% accuracy on the rice dataset. The ablation analysis performed the necessity of each component in the HCVT model. The LIME (local interpretable model-agnostic explanations) technique is utilized to comprehend the HCVT method functionality and its results showed that the HCVT model accomplished better than the present cutting-edge models in identifying plant illness and exhibiting its generalization potential. The HCVT model is affordable and widely accessible for analysing plant leaf images. Utilizing the HCVT model offers a robust and easily accessible approach for diagnosing plant diseases by analysing leaf images.
Unknown authors· Engineering Research Express· 0 citations
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