The early and effective diagnosis of tomato leaf diseases is very important to enhance crop yield and reduce economic loss in precision agriculture. The conventional image-based methods are typically based on single architecture model, which cannot capture fine-grained lesion details and global contextual patterns simultaneously in the real-field. To this end, we introduce a deep hybrid Convolutional Neural Network (CNN) –Transformer architecture by combining ConvNeXt Large (ConvNeXt-L) (as local feature extractor) and Swin Transformer (as global context encoder). The concatenated features vector is then fed to a shallow classifier to predict the disease. The model was tested on two datasets, namely a field dataset in agriculture areas from Madhya Pradesh (India) and a benchmark tomato leaf dataset. Experimental results revealed that the proposed scheme achieved accuracy of 92.83% on a primary dataset, and performance was significantly high with an accuracy of up to 95.65% in terms of generalization rate for computing technique models from various environmental conditions.
Reliable identification of millet cultivars is essential for maintaining grain quality, supporting seed authentication, and improving automation in post-harvest processing. Despite recent advances in computer vision, accurate classification of millet varieties remains challenging…
India’s economy is primarily based on agriculture. Agriculture has significant contribution in nation’s GDP. Food security and employment significantly influenced by agriculture. However factors like uncertain weather conditions, poor quality of seeds and plant diseases impact on…
Federated learning (FL) has emerged as a prospective model training paradigm for distributed clients, which provides data privacy while maintaining the ability to train a collaborative model.In healthcare, finance and edge intelligence applications, federated learning (FL) is a p…
The extensive use of smart devices and several security weaknesses of networks has intensively enhanced the number of cyber-attacks in Internet of Things (IoT) networks. The detection and classification of malicious traffic is a key to ensure the security of those systems. It aim…
Hyperspectral image (HSI) classification is a crucial task in many remote sensing applications including environmental monitoring, precision agriculture, mineral exploration, and land-cover mapping. However, the high dimensionality of hyperspectral data, spectral redundancy, nois…
- Enhancing agricultural productivity and attaining sustainable crop management depend on the early and precise identification of leaf disease. Using state-of-the-art technologies in precision agriculture like machine learning (ML) and image processing greatly increases the effec…