ABSTRACT :
This system proposes an improved multi-task cascaded convolutional network-based intelligent fruit and ripening detection method. This method has the capability to make the Automated Robot work in real time with high accuracy. Moreover, based on the relationship between the diversity samples of the dataset and the parameters of neural networks evolution, this paper presents an improved augmented method, a procedure that is based on image fusion to improve the detector performance. The chloroplast is responsible for providing the green colour in the plant. Where as the chromoplast its various types of colours in the plant. There is a change from Green to yellow colour in most of the fruit. This is due to the overgrowth of the chromoplast by replacement of the chloroplast hence there is feeding of the green colour and prominence of the yellow colour. The change of colour of unripe green fruit from green to red is because of the transformation of chloroplast to chromoplast because in immature stage chloroplast is green in colour while on maturation the chloroplast disappears and chromoplast containing carotenoids which impart red colour.
EXISTING SYSTEM:
Fruits are common food consumed by human since prehistoric era. They make important nutritional contribution to human well-being because of their high nutritive value. It is need to ensure the quality of fruits that are consumed in any places. To do this, a fruit detection system can be established that can recognizes various types of fruits from images that are captured by any digital camera or smart phone from various places. This system will help us to check the quality of fruits and also help us to develop a robotic harvesting system from orchards. To develop the system, machine learning techniques have used in this system. Accurate and efficient fruit detection is of critical importance for a machine.
PROPOSED SYSTEM :
A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm which can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image and be able to differentiate one from the other. The pre-processing required in a ConvNet is much lower as compared to other classification algorithms. While in primitive methods filters are hand-engineered, with enough training, ConvNets have the ability to learn these filters/characteristics. The architecture of a ConvNet is analogous to that of the connectivity pattern of Neurons in the Human Brain and was inspired by the organization of the Visual Cortex. Individual neurons respond to stimuli only in a restricted region of the visual field known as the Receptive Field. A collection of such fields overlap to cover the entire visual area.
SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10
HARDWARE REQUIREMENTS:
Processor : Core I3
RAM Capacity : 2 GB
Hard Disk : 250 GB
Monitor : 15″ Color
Mouse : 2 or 3 Button Mouse
Key Board : Windows 08/10
