Open AccessAutomatic Fruit Detection and Couting System Using Neural Network
Author(s) -
Vaishnavi R Padiyar,
Nagaraja Hebbar N,
Shreya G Shetty
Publication year - 2021
Publication title -
international journal of scientific research in science and technology
Language(s) - English
Resource type - Journals
eISSN - 2395-602X
pISSN - 2395-6011
DOI - 10.32628/cseit217442
Subject(s) - computer science , artificial neural network , identification (biology) , artificial intelligence , process (computing) , field (mathematics) , focus (optics) , object detection , computer vision , pattern recognition (psychology) , mathematics , botany , physics , pure mathematics , optics , biology , operating system
In the field of agriculture, Identification and counting the number of fruits from the image helps the farmers in crop estimation. At present manual counting of fruits present in many places. The current practice of yield estimation based on the manual counting of fruits has many drawbacks as it is time consuming and expensive process. while considering the progress of fruit detection, estimating proper and accurate fruit counts from images in real-world scenarios such as orchards is still a challenging problem. The focus of this paper is on the web application of fruit yield estimation. This web application helps the farmers to count the number of fruits easily. This system provides an automated and efficient fruit counting system using computer vision techniques. This paper provides the progress towards in-field fruit counting using neural network object detection methods. So this process is done by recognizing each fruit in the image and taking the count. In the neural network, we have used YOLO architecture for recognizing the fruits.