Around the world, agriculture is one of the important sectors of human life in terms of food, business, and employment opportunities. In the farming field, wheat is the most farmed crop but every year, its ultimate production is badly influenced by various diseases. On the other hand, early and precise recognition of wheat plant diseases can decrease damage, resulting in a greater yield. Researchers have used conventional and Machine Learning (ML)-based techniques for crop disease recognition and classification. However, these techniques are inaccurate and time-consuming due to the unavailability of quality data, inefficient preprocessing techniques, and the existing selection criteria of an efficient model. Therefore, a smart and intelligent system is needed which can accurately identify crop diseases. In this paper, we proposed an efficient ML-based framework for various kinds of wheat disease recognition and classification to automatically identify the brown- and yellow-rusted diseases in wheat crops. Our method consists of multiple steps. Firstly, the dataset is collected from different fields in Pakistan with consideration of the illumination and orientation parameters of the capturing device. Secondly, to accurately preprocess the data, specific segmentation and resizing methods are used to make differences between healthy and affected areas. In the end, ML models are trained on the preprocessed data. Furthermore, for comparative analysis of models, various performance metrics including overall accuracy, precision, recall, and F1-score are calculated. As a result, it has been observed that the proposed framework has achieved 99.8% highest accuracy over the existing ML techniques.

The advancement of open-source frameworks and user-friendly manipulation applications has accelerated the spread of deep fakes. In this study, we proposed optimal features assisted with a dual attention (DA) network strategy to combat this proliferation in consumer imaging devices. We employed EfficientNetV2 (ENV2) as the primary feature extractor, initially utilizing its pre-trained weights from the ImageNet dataset while keeping its layers frozen to leverage their rich feature extraction capabilities. We enhance this base model with a DA module that integrates the Convolutional Block Attention Module (CBAM), which utilizes both channel attention (CA) and spatial attention (SA) mechanisms to improve feature representation. CA dynamically adjusts channel-wise feature responses to capture interdependencies between channels, thereby improving feature discrimination. SA allows the network to focus on important regions within feature maps, enhancing localization and reducing noise. The features are assisted in multi-stages of the network with residual fashion to focus on discriminative visual information. During fine-tuning, we unfreeze the deeper layers of ENV2 for further refinement of learned features to better suit the deepfake dataset. We employed a targeted fine-tuning approach, unfreezing specific layers and applying iterative adjustments to optimize performance, providing valuable insights into countering the growing use of synthetic media in consumer imaging. To validate our network, we conducted comprehensive experiments on four benchmark datasets: FaceForensics++, the World Leaders, Celeb-DF, and DFDC. As a result, our network achieved superior performance compared to existing benchmarks and state-of-the-art approaches, offering a promising solution for robust deepfake detection (DD) in consumer imaging technologies.