have no conflicts of interest that are directly relevant to the content of this article. Funding No sources of funding were used to prepare this manuscript. Ethics approval Not applicable. Consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials Not applicable.

In recent years, electrospinning has emerged as a promising technique for the preparation of nanofibers with unique properties like flexibility, high porosity and high surface area. In the context of nanodelivery systems, polymer-based nanofibers have become promising carriers of drugs and bioactive compounds ensuring their sustained release and targeted delivery. In this study, neem extract-loaded nanofibers were developed as sustained delivery systems using the electrospinning method. The chitosan, alginate and polyethylene oxide were used as the polymeric matrix for loading of aqueous extract of neem leaves. The prepared nanofibers NF1, NF2 and NF3 carrying 2%, 4% and 6% extract respectively were characterized using SEM, FTIR, XRD and TGA. Further, the as-prepared nanocomposites exhibited a high degree of swelling and dual-phase release of phytoconstituents. Moreover, the developed controlled delivery systems were tested for antifungal and antioxidant potential. Importantly, the bioactivities of the prepared nanofibers could be improved further by using organic extracts which are generally enriched with phytoconstituents. Herein, we selected biodegradable and mucoadhesive biopolymers and an aqueous extract of neem for the development of controlled-delivery nanofibers by electrospinning through a sustainable and cleaner production process. Thus, the prepared biocompatible nanofibrous systems with biphasic release profile could be employed for biomedical applications including wound dressing, soft tissue scaffolds and as transdermal carriers.

In view of the challenges faced by organizations and departments concerned with agricultural capacity observations, we collected In-Situ data consisting of diverse crops (More than 11 consumable vegetation types) in our pilot region of Harichand Charsadda, Khyber Pakhtunkhwa (KP), Pakistan. Our proposed Long Short-Term Memory based Deep Neural network model was trained for land cover land use statistics generation using the acquired ground truth data, for a synergy between Planet-Scope Dove and European Space Agency’s Sentinel-2. Total of 4 bands from both sentinel-2 and planet scope including Red, Green, Near-Infrared (NIR) and Normalised Difference Vegetation Index (NDVI) were used for classification purpose. Using short temporal frame of Sentinel-2 comprising 5 date images, we propose an realistic and implementable procedure for generating accurate crop statistics using remote sensing. Our self collected data-set consists of a total number of 107,899 pixels which was further split into 70% and 30% for training and testing purpose of the model respectively. The collected data is in the shape of field parcels, which has been further split for training, validation and test sets, to avoid spatial auto-correlation. To ensure the quality and accuracy 15% of the training data was left out for validation purpose, and 15% for testing. Prediction was also performed on our trained model and visual analysis of the area from the image showed significant results. Further more a comparison between Sentinel-2 time series is performed separately from the fused Planet-Scope and Sentinel-2 time-series data sets. The results achieved shows a weighted average of 93% for Sentinel-2 time series and 97% for fused Planet-Scope and Sentinel-2 time series.

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