For billions of people worldwide, enhancing the quantity and quality of paddy production stands as an essential goal. Rice, being a primary grain consumed in Asia, demands efficient farming techniques to ensure both sufficient yields and high-quality crops. Detecting diseases in rice crops is crucial to prevent financial losses and maintain food quality. Traditional methods in the agricultural industry often fall short in accurately identifying and addressing these issues. However, leveraging artificial intelligence (AI) offers a promising avenue due to its superior accuracy and speed in evaluation. Nutrient deficiencies significantly impact paddy growth, causing issues like insufficient potassium, phosphorus, and nitrogen. Identifying these deficiencies in paddy leaves, especially during the mid-growth stage, poses a considerable challenge. In response to these obstacles, a novel approach is proposed in this study—a deep learning model.

In Asia, rice is the most consumed grain by humans, serving as a staple food in India. The yield of rice paddies is easily affected by nutrient deficiencies and leaf diseases. To overcome this problem and improve the yield productivity of rice, nutrient deficiency and leaf disease identification are essential. The main nutrient elements in paddies are potassium, phosphorus, and nitrogen (PPN), the deficiency of any of which strongly affects the rice plants. When multiple nutrient elements are deficient, the leaf color of the rice plants is altered. To overcome this problem, optimal nutrient delivery is required. Hence, the present study proposes the use of Fuzzy C Means clustering (FCM) with Improved Tunicate Swarm Optimization (ITSO) to segment the lesions in rice plant leaves and identify the deficient nutrients. The proposed ITSO integrates the Tunicate Swarm Optimization (TSO) and Bacterial Foraging Optimization (BFO) approaches. The Hybrid Convolutional Neural Network (HCNN), a deep learning model, is used with ITSO to classify the rice leaf diseases, as well as nutrient deficiencies in the leaves. Two datasets, namely, a field work dataset and a Kaggle dataset, were used for the present study. The proposed HCNN-ITSO classified Bacterial Leaf Bright (BLB), Narrow Brown Leaf Spot (NBLS), Sheath Rot (SR), Brown Spot (BS), and Leaf Smut (LS) in the field work dataset. Furthermore, the potassium-, phosphorus-, and nitrogen-deficiency-presenting leaves were classified using the proposed HCNN-ITSO in the Kaggle dataset. The MATLAB platform was used for experimental analysis in the field work and Kaggle datasets in terms of various performance measures. When compared to previous methods, the proposed method achieved the best accuracies of 98.8% and 99.01% in the field work and Kaggle datasets, respectively.

Enormous usage of multimedia necessitates maintaining a secured environment. Security in multimedia communication can be achieved with many cryptographic techniques. Perceptual video encryption is an essential technique to prevent the unwanted use of the video which is transmitted. Encryption is used in order to maintain a safe content of data from attackers. This paper explains about the perceptual video encryption using SPIHT (Set Partitioning in Hierarchical Tree) transform. It achieves good PSNR values and it is more secured to transfer videos.

Color constancy is a concept used in various computer vision applications, such as image segmentation, object recognition, scene classification, and image forensics. This work speaks about designing an adaptive color constancy algorithm which identifies the regions of skin found in faces. Illumination level is estimated by training and is corrected using proposed algorithm with different source of illumination, thus proving the variation in skin color pixels under different illumination conditions. This algorithm is implemented on images captured using webcam under different illuminations. It is helpful to estimate the illumination and correct the same with respect to the reference image under natural light.

The paper presents MRI brain diagnosis support system for structure segmentation and its analysis using kernel weighted segmentation. The Proposed method has been used to segment normal tissues and abnormal tissue like tumor part of MR image automatically. MR images are often corrupted by Intensity in homogeneity and artifacts. This may affect the performance of image processing techniques used for brain image analysis. Due to this type of artifacts and noises unknowingly some normal tissue in MRI may be misclassified as other type of normal tissue and it leads to error during diagnosis process. The proposed system remove noise from the given images using a method called spectral subtraction de noising (SSD) and Kernel Weighted Fuzzy C Means (KWFCM) has been used for segmentation. This method incorporates spatial information and the membership weighting of every cluster has been altered after the cluster distribution in the neighborhood is considered. The paper results will be presented as segmented tissues with various parameter evaluations to show algorithm efficiency.