ABSTRACT:Quality is an important aspect in the trading and business of vegetable and fruits. The customer satisfaction and acceptance depends greatly on the external appearance of these food items. The qualities assessed include the size, shape, colour and texture. At present only manual inspection is being done to brand the quality of the product. Manual inspection is challenging in terms of the speed of inspection and the reliability of the output. Machine vision systems may be used as an object of quality evaluation. Various methods such as colour sorting and pattern recognition can be used to evaluate the quality of the fruit or vegetable. Machine vision systems provide a nondestructive, reliable, accurate and fast results regarding the external appearance of the fruit or vegetable. The application of lighting, cameras and image processing are being discussed here with an eye on the future developments possible.

Supervised machine learning is one of the machine learning task that generates required function from the training data which is labelled. The aim of supervised machine learning is to build or construct a model that makes predictions by using the function inferred from the labelled training data. This paper put a light on how the supervised machine-learning techniques are used to build a predictive model from the dataset of titanic disaster and also a comparative analysis of supervised machine learning methods like Random Forests and Decision Trees are implemented. In this work, with a training dataset containing features or labels like sex, age and class, survivors are predicted from the four test datasets. And from the observations of results a comparative analysis of both supervised machine learning methods namely Decision Trees and Random Forests is implemented.