The Plant Disease Detecting System leverages advances in artificial intelligence and deep learning to provide an automated, efficient, and reliable solution for identifying plant diseases at an early stage and contributes to increased crop productivity, reduced chemical usage, and sustainable farming practices.
The proposed automated leaf disease detection system using image processing and deep learning techniques can detect leaf diseases effectively and efficiently, making it a useful and cost-effective solution for supporting farmers and agricultural experts in early disease diagnosis.
Shilpa T. S., K. U, Anusha Jajur J· World Journal of Advanced En...· 0 citations
Various convolutional neural network architectures, including AlexNet, VGGNet, ResNet, DenseNet, EfficientNet, MobileNet, Inception, and Xception, are critically reviewed along with modern transformer-based models such as Vision Transformer (ViT), Swin Transformer, and hybrid CNN–Transformer frameworks.
Allupati Chakradhar Patro· International Journal of Sci...· 0 citations
Plant leaf diseases significantly reduce agricultural productivity and crop yield worldwide, making early and accurate detection essential to prevent large-scale crop damage. Traditional disease identification methods rely on manual inspection by experts, which is time-consuming, costly, and often inaccessible to farme...
Kuppala Ajay Kumar, Yella Sai Krishna, R. Kumar et al.· 2026 6th International Confe...· 0 citations
A method that which is detecting the disease of a tomato plant from their leaf images is proposed with the deep learning algorithms Convolutional Neural Network (CNN), and MobileNet which is a one of the transfer learning method of CNN.
Onumu Lokesh Kumar, Tulasi Miriyala· International Journal for Re...· 0 citations
The findings show that DL, especially CNN and transfer learning models, performed better than machine learning techniques, and points out several significant problems, such as dataset imbalance, insufficient generalization, computing inefficiency, and a dearth of real-world data.
Yuvraj Gholap, Rahul Joshi, Milind Gayakwad et al.· Journal of Intelligent Decis...· 0 citations
Various pre-trained deep learning models, including DenseNet121, DenseNet201, ResNet50, VGG16, VGG19, and InceptionResNetV2 are explored to identify rice plant diseases and nutrient deficiencies, with Notably, a fuzzy distance-based ensemble of DenseNet121, DenseNet201, and VGG16 achieved the highest testing accuracy.
Kashyap Ramani, Nagamma Patil· SN Computer Science· 0 citations
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