The PlantCity dataset addresses significant agricultural yield losses in Pakistan from plant diseases. It provides 10,667 high-resolution images of leaves from 12 key crops: apple, apricot, bean, cherry, maize, fig, grape, loquat, pear, tomato, walnut, and persimmon. The images are organized into 52 classes (41 diseased and 11 healthy) and augmented to a total of 52,273 images. Data was collected in real-field conditions in Charsadda (34.15°N, 71.74°E, typical temperature 40–44 °C) and Chitral (35.85°N, 71.79°E, typical temperature 25–30 °C) from April to July 2023–2024. The dataset enables the development of deep learning models for automated disease classification and captures a range of environmental factors, including high temperatures that can exacerbate disease symptoms. It utilizes smartphone-based computer vision to facilitate early disease identification, thereby supporting precision farming and sustainable agriculture in Pakistan.

Before the 19th century, all communication and official records relied on handwritten documents, cherished as valuable artefacts by different ethnic groups. While significant efforts have been made to automate the transcription of major languages like English, French, Arabic, and Chinese, there has been less research on regional and minor languages, despite their importance from geographical and historical perspectives. This research focuses on detecting and recognizing Pashto handwritten characters and ligatures, which is essential for preserving this regional cursive language in Pakistan and its status as the national language of Afghanistan. Deep learning techniques were employed to detect and recognize Pashto characters and ligatures, utilizing a newly developed dataset specific to Pashto. A further enhancement was done on the dataset by implementing data augmentation, i.e., scaling and rotation on Pashto handwritten characters and ligatures, which gave us many variations of a single trajectory. Different morphological operations for minimizing gaps in the trajectories were also performed. The median filter was used for the removal of different noises. This dataset will be combined with the existing PHWD-V2 dataset. Various deep-learning techniques were evaluated, including VGG19, MobileNetV2, MobileNetV3, and a customized CNN. The customized CNN demonstrated the highest accuracy and minimal loss, achieving a training accuracy of 93.98%, validation accuracy of 92.08% and testing accuracy of 92.99%.

To determine the role of chroma and illumination conditions on assessments of unique hue stimuli (UHs: Y, B, R, and G) 25 color normal observers selected Munsell color chips under controlled but different light sources that included simulated D65 daylight, incandescent (A), CWF and TL84, from two rotatable trays—one containing low and the other high chroma samples. Three independent evaluations were obtained from each observer with a gap of at least 24 h between assessments. A total of 2400 UH assessments were thus obtained. The mean, as well as the range and variability, in UH selections was calculated according to gender, illumination condition, and chroma. Results show statistically significant differences for uB and uR stimuli selections from the low to the high chroma sets under all lighting conditions, and for uY under light source A and CWF while differences in uG selections in different lighting conditions were statistically insignificant. No significant difference was found between male and female UH selections. Hue shift predictions based on CAT02 transform were found to agree well with observer responses under different illumination conditions. It was also found that observers’ UH choices varied more for low chroma samples under different lighting conditions compared to high chroma samples. The highest variability was obtained under light source CWF, while the simulated D65 source resulted in the lowest variability in selections.

Information security is one of the most challenging problems in today's technological world. In order to secure the transmission of secret data over the public network (Internet), various schemes have been presented over the last decade. Steganography combined with cryptography, can be one of the best choices for solving this problem. This paper proposes a new steganographic method based on gray-level modification for true colour images using image transposition, secret key and cryptography. Both the secret key and secret information are initially encrypted using multiple encryption algorithms (bitxor operation, bits shuffling, and stego key-based encryption); these are, subsequently, hidden in the host image pixels. In addition, the input image is transposed before data hiding. Image transposition, bits shuffling, bitxoring, stego key-based encryption, and gray-level modification introduce five different security levels to the proposed scheme, making the data recovery extremely difficult for attackers. The proposed technique is evaluated by objective analysis using various image quality assessment metrics, producing promising results in terms of imperceptibility and security. Moreover, the high quality stego images and its minimal histogram changeability, also validate the effectiveness of the proposed approach.