The image segmentation performs a significant role in the field of image processing because of its wide range of applications in the agricultural fields to identify plants diseases by classifying the different diseases. Classification is a technique to classify the plants diseases on different morphological characteristics. Different classifiers are used to classify such as SVM (Support Vector Machine), K-nearest neighbor classifiers, Artificial Neural Networks, Fuzzy Logic, etc. This paper presents different image processing techniques used for the early detection of different Plants diseases by different authors with different techniques. The main focus of our work is on the critical analysis of different plants disease segmentation techniques. The strengths and limitations of different techniques are discussed in the comparative evaluation of current classification techniques. This study also presents several areas of future research in the domain of plants disease segmentation. Our focus is to analyze the best classification techniques and then fuse certain best techniques to overcome the flaws of different techniques, in the future.

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Background and Aim: The Novel coronavirus disease has grasped the world as one of the most devastating pandemics of recent times. Many efforts such as social distancing was made to cut down the spread of the virus in its early days and restrictions were followed by numerous countries worldwide which resulted in serious hurdles in all sectors of our daily lives such as education, financial and social activities. This study was conducted to evaluate the implications of E-Learning and its future use by students and faculty members following the pandemic restrictions. Material and Methods: The study was a cross-sectional survey that was distributed online among students and faculty members belonging to medical and non-medical programs across universities in major cities of Pakistan. Results: Of the total 476 responses, it was evident that the majority of the students were not satisfied with the learning experience that virtual classrooms provided. Concerning clinical and practical skills, a majority agreed that the online teaching method is not an effective way to develop essential skills. Conclusion: Online education may be a necessary technological advancement needed in the field of education, but as concluded from the results of this study there is a disagreement about virtual classrooms being an effective medium of learning. It is also inconvenient to develop appropriate practical and clinical skills using E-learning as a medium.

Wheat (Triticum aestivum) is the most important staple food in Pakistan. Knowledge of its genetic diversity is critical for designing effective crop breeding programs. Here we report agro-morphological and yield data for 112 genotypes (including 7 duplicates) of wheat (Triticum aestivum) cultivars, advance lines, landraces and wild relatives, collected from several research institutes and breeders across Pakistan. We also report genotyping-by-sequencing (GBS) data for a selected sub-set of 52 genotypes. Sequencing was performed using Illumina HiSeq 2500 platform using the PE150 run. Data generated per sample ranged from 1.01 to 2.5 Gb; 90% of the short reads exhibited quality scores above 99.9%. TGACv1 wheat genome was used as a reference to map short reads from individual genotypes and to filter single nucleotide polymorphic loci (SNPs). On average,up to 364,074 SNPs per genotype were recorded. The sequencing data has been submitted to the SRA database of NCBI (accession number SRP179096). The agro-morphological and yield data, along with the sequence data and SNPs will be invaluable resources for wheat breeding programs in future.

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