  The fruit identification process involves analyzing and categorizing
different types of fruits based on their visual characteristics. This activity
can be achieved using a range of methodologies, encompassing manual
examination, conventional computer vision methodologies, and more sophisticated
methodologies employing machine learning and deep learning. Our study
identified a total of 15 distinct categories of fruit, consisting of class
Avocado, Banana, Cherry, Apple Braeburn, Apple golden 1, Apricot, Grape, Kiwi,
Mango, Orange, Papaya, Peach, Pineapple, Pomegranate and Strawberry. Neural
Architecture Search (NAS) is a technological advancement employed within the
realm of deep learning and artificial intelligence, to automate conceptualizing
and refining neural network topologies. NAS aims to identify neural network
structures that are highly suitable for tasks, such as the detection of fruits.
Our suggested model with 99.98% mAP increased the detection performance of the
preceding research study that used Fruit datasets. In addition, after the
completion of the study, a comparative analysis was carried out to assess the
findings in conjunction with those of another research that is connected to the
topic. When compared to the findings of earlier studies, the detector that was
proposed exhibited higher performance in terms of both its accuracy and its
precision.
