Scopus Indexed Publications
Paper Details
- Title
-
Defected Bitter Gourd Detection Using Convolutional Neural Network; A Computer Vision Approach to Reduce Cost and Time
- Author
-
Md. Mehedi Hasan,
Arafat Ullah Nur,
Khairul Alam,
Md. Newaz Ahmed Diganta,
Md. Tarek Habib,
- Email
-
- Abstract
-
Bitter Gourd: Botanical Title
is Memordica charantia L. Bitter gourd is known for its usage for
medicinal purposes in Asian countries. Bitter gourd could be a wealthy
source of vitamins and minerals. It contains press, magnesium,
potassium, and vitamins like A and C. Different anti-oxidants and
anti-inflammatory compounds are showed in Bitter gourd. Separating
diseased vegetables from healthy ones of medium size vegetables is one
tough job as well as costly for farmers. We propose a system which
detects diseased or even defected bitter Gourd. We used Deep Learning
(DL) to achieve the goal. Image processing is one of the most common yet
interesting fields of DL. We used Convolutional Neural Network(CNN) to
process images of Bitter Gourd. We created three different models by
diversifying Convolutional Layer by number and its value. We are giving
farmers a brief instrument utilizing Deep Learning that makes a
difference select the non-absconded thing. The world is developing with
AI within the future. So why not an agriculturist they are moreover
utilizing this digitalization. We got some interesting results through
the process and choose the model that performed best without test result
and evaluation result over-fitting. It's M3 model with 99.70% accuracy.
- Keywords
-
Image Classification , Deep Learning(DL) , CNN , Object Detection , Prediction
- Journal or Conference Name
- 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT)
- Publication Year
-
2021
- Indexing
-
scopus