This paper analyses the status of food security in selected South Pacific Island countries, namely Cook Islands, Fiji Islands, Papua New Guinea, Samoa, Solomon Islands, Tonga, and Vanuatu at the national and household levels during the period 1991-2002. Due to narrow resource base and production conditions, Pacific Islands concentrate on a few primary commodities for production and exports. During recent years import dependency for food items has increased mainly due to a decline in per capita food production and a rapid rate of rural-urban migration. Currently, export earnings can finance food imports but earnings could fall short of the requirements needed after the expiry of some commodity preferential price agreements with importing countries. National food security is dependent on the continuation of subsistence farming and tapping ocean resources in conjunction with the on-going commercial farming of those crops in which Pacific Islands have a comparative advantage. Increased productivity is crucial for improving agricultural performance through government investment in rural infrastructure, agricultural research and extension, irrigation and appropriate price incentives. This would also help alleviate poverty for improvement in economic accessibility of food by households. There is also a need to design appropriate disaster risk management programmes to minimize any adverse effects on the food supply.

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Object detection has experienced a surge in interest due to its relevance in video analysis and image interpretation. Traditional object detection approaches relied on handcrafted features and shallow trainable algorithms, which limited their performance. However, the advancement of Deep learning (DL) has provided more powerful tools that can extract semantic, highlevel, and deep features, addressing the shortcomings of previous systems. Deep Learning-based object detection models differ regarding network architecture, training techniques, and optimization functions. In this study, common generic designs for object detection and various modifications and tips to enhance detection performance have been investigated. Furthermore, future directions in object detection research, including advancements in Neural Network-based learning systems and the challenges have been discussed. In addition, comparative analysis based on performance parameters of various versions of YOLO approach for multiple object detection has been presented.

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