TY  - JOUR
T1  - Fruit Stage Classification using Machine Learning
AU - Antonidoss, A. AU - Lakshmoji, S. AU - Ramoji, S. AU - Sumith, K. 
JO  - Asian Journal of Information Technology
VL  - 20
IS  - 5
SP  - 130
EP  - 133
PY  - 2021
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2021.130.133
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2021.130.133
KW  - Fast R-CNN
KW  -supervised machine learning
KW  -SVM
KW  -fruits
KW  -YOLO
AB  - Fruits are the major source of food for humans.
According to the scientific research done it is found out
that quality seeds are needed which in turn leads to the
requirement of quality fruits for better yield of new crops.
So, it is required for farmers to identify the correct stage
of fruit. Farmers spend their lives solely to utilize their
time to discover this extraordinary aspect of farming.
Instead of farmers using their time in this process they can
use their effort in the fields where their work cannot be
replaced by any of the technological advancements and
this process can be automated by the latest technologies.
With the advancement of technology, the process of
identifying the stages of fruits can be done in short span
of time by using techniques like object detection which
has gained a huge popularity in recent times. It mainly
involves two phases. The first phase is to identify the type
of the given fruit and the second phase is to classify the
stage of the fruit which tells how likely the given fruit is
suitable for planting. The first phase can be obtained by
using Faster R-CNN of YOLO object detection model
which is faster than R-CNN. The second phase can be
obtained by using SVM model.
ER  -