Tomato is one of the most essential and consumable crops in the world. Tomatoes differ in quantity depending on how they are fertilized. Leaf disease is the primary factor impacting the amount and quality of crop yield. As a result, it is critical to diagnose and classify these disorders appropriately. Different kinds of diseases influence the production of tomatoes. Earlier identification of these diseases would reduce the disease’s effect on tomato plants and enhance good crop yield. Different innovative ways of identifying and classifying certain diseases have been used extensively. The motive of work is to support farmers in identifying early-stage diseases accurately and informing them about these diseases. The Convolutional Neural Network (CNN) is used to effectively define and classify tomato diseases. Google Colab is used to conduct the complete experiment with a dataset containing 3000 images of tomato leaves affected by nine different diseases and a healthy leaf. The complete process is described: Firstly, the input images are preprocessed, and the targeted area of images are segmented from the original images. Secondly, the images are further processed with varying hyper-parameters of the CNN model. Finally, CNN extracts other characteristics from pictures like colors, texture, and edges, etc. The findings demonstrate that the proposed model predictions are 98.49% accurate.

The paddy crop is the most essential and consumable agricultural produce. Leaf disease impacts the quality and productivity of paddy crops. Therefore, tackling this issue as early as possible is mandatory to reduce its impact. Consequently, in recent years, deep learning methods have been essential in identifying and classifying leaf disease. Deep learning is used to observe patterns in disease in crop leaves. For instance, organizing a crop’s leaf according to its shape, size, and color is significant. To facilitate farmers, this study proposed a Convolutional Neural Networks-based Deep Learning (CNN-based DL) architecture, including transfer learning (TL) for agricultural research. In this study, different TL architectures, viz. InceptionV3, VGG16, ResNet, SqueezeNet, and VGG19, were considered to carry out disease detection in paddy plants. The approach started with preprocessing the leaf image; afterward, semantic segmentation was used to extract a region of interest. Consequently, TL architectures were tuned with segmented images. Finally, the extra, fully connected layers of the Deep Neural Network (DNN) are used to classify and identify leaf disease. The proposed model was concerned with the biotic diseases of paddy leaves due to fungi and bacteria. The proposed model showed an accuracy rate of 96.4%, better than state-of-the-art models with different variants of TL architectures. After analysis of the outcomes, the study concluded that the anticipated model outperforms other existing models.

The widespread adoption of advanced technologies may be responsible for the extensive dissemination of forged photographs and videos on the Internet. This could potentially result in the proliferation of fraudulent identities online, raising safety concerns in society. The traditional method for detecting forgery, commonly referred to as the classical forgery method, lacks the capability to accurately identify such fraudulent activities. This limitation arises because these algorithms are trained on publicly available centralized datasets and do not prioritize privacy and security considerations. Consequently, they adversely affect the ability to detect counterfeit content. As a potential solution to this problem, we employed a highly effective deep learning methodology rooted in federated learning. We introduced a novel deep learning approach that combines features to assess the authenticity of photographs and videos shared on social media platforms. The proposed model was trained using three widely recognized forensic datasets: FaceForensics++, Deepforensic-1.0, and WildDeepfake. Visual features were extracted using two widely recognized deep learning approaches, namelyInception and Xception. These features were then combined into a feature vector using Canonical Correlation Analysis, and Convolutional Neural Networks were trained on these features to identify manipulated images and videos. The experiments were carried out with publicly available datasets and involved changing several parameters. Finally, the proposed model’s performance was compared with other deep learning models within federated learning environments to identify forgeries. Our proposed approach demonstrated exceptional performance, achieving an accuracy rate of 98.99% when evaluated on the merged dataset.