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The purpose of this paper is to examine how food aid providers in Sussex and Southwest London responded and managed during the pandemic.

The methodological approach consists of three inter-related layers. A qualitative description research approach based on naturalistic inquiry, supplemented by site visits and personal observations was used.

The pandemic catalysed dramatic, often positive, changes to the provision of food aid, with a move away from the traditional food bank model. It brought about increased coordination and oversight, as well as the upscaling of capabilities, infrastructure and provisions.

The paper contributes to the literature on food aid in the UK It provides evidence for how providers are transforming the sector for the better and potentially helping to deal with the cost-of-living crisis.

Extraction of leaf region from the plant leaf images is a prerequisite process for species recognition, disease detection and classification and so on, which are required for crop management. Several approaches were developed to implement the process of leaf region segmentation from the background. However, most of the methods were applied to the images taken under laboratory setups or plain background, but the application of leaf segmentation methods is vital to be used on real-time cultivation field images that contain complex backgrounds. So far, the efficient method that automatically segments leaf region from the complex background exclusively for black gram plant leaf images has not been developed.

Extracting leaf regions from the complex background is cumbersome, and the proposed PLRSNet (Plant Leaf Region Segmentation Net) is one of the solutions to this problem. In this paper, a customized deep network is designed and applied to extract leaf regions from the images taken from cultivation fields.

The proposed PLRSNet compared with the state-of-the-art methods and the experimental results evident that proposed PLRSNet yields 96.9% of Similarity Index/Dice, 94.2% of Jaccard/IoU, 98.55% of Correct Detection Ratio, Total Segmentation Error of 0.059 and Average Surface Distance of 3.037, representing a significant improvement over existing methods particularly taking into account of cultivation field images.

In this work, a customized deep learning network is designed for segmenting plant leaf region under complex background and named it as a PLRSNet.