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Modern agriculture has been fascinated by various advancements in agriculture and the processing of foods and supply management that provision farmers to improve production. The health of plants is essential to improve production and economic growth. Diseases in plants can affect production and create a rigorous impact on the quality and create a hazard to food safety. Hence, detecting and classifying plant leaf diseases is essential to prevent the disease spread across the plants in the agriculture field and to improve productivity. The researchers in existing frameworks utilized artificial intelligence and machine learning techniques to demonstrate noteworthy solutions. However, a few issues exist related to noises in the images, hyperparameter selection problems, and over-fitting problems that influence prediction accuracy. The proposed model jellyfish ResNet (JF-ResNet) works well to achieve a better accuracy level by incorporating jellyfish optimized ResNET for tomato plant leaf disease identification and classification. The performance metrics such as accuracy, specificity, sensitivity, and F1-Score is used to evaluate the performance of the JF-ResNet model. The proposed model achieves 97.3% accuracy, 95.3% sensitivity, 96.1% specificity, 96.9% recall, 96.4% precision and 97.1% F1-Score. [ABSTRACT FROM AUTHOR]

The S-Star Trial Bioinformatics on-line course (www.s-star.org) is a global experiment in bioinformatics distance education. Six universities from five continents have participated in this project. One hundred and fifty students participated in the first trial course of which 96 followed through the entire course and 70 fulfilled the overall course requirements. The course was built up of 12 lectures covering different basic and advanced topics within bioinformatics. The lectures consist of streaming video and synchronized slides with follow-up discussion forums and a final assessment. It draws on experts from diverse institutions to share their teaching expertise and resources via distance learning. A major goal of S-Star is to provide free introductory web-based education. Here we present the outline of the course, experiences from a global collaboration, and feedback from the first trial of the on-line S-Star course. (Contains 2 tables, 1 figure and 1 footnote.)