Open AccessFruit Classification Comparison Based on CNN and YOLO
Author(s) -
Riyanshu Raj,
Sriharsha Nagaraj,
Saurav Ritesh,
T A Thushar,
V. M. Aparanji
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1187/1/012031
Subject(s) - agriculture , automation , field (mathematics) , agricultural engineering , reliability (semiconductor) , computer science , yield (engineering) , irrigation , business , agricultural science , artificial intelligence , mathematics , engineering , environmental science , geography , agronomy , biology , mechanical engineering , pure mathematics , power (physics) , physics , materials science , archaeology , quantum mechanics , metallurgy
Today, agriculture has become more of science than art. The demand for fresh produce is growing, while availability of field workers is diminishing. Also, Farmers keep investing in land, irrigation, and labor, and much of the fruit is left hanging on the trees because there are not enough people to pick it when it is ripe. Automation in the agricultural field using modern information and Communication Technology can increase the efficiency, reliability, and precision; and reduces the need of human intervention. Thus, to meet solution to this problem is by reducing the costs for farmers and increasing the yield of crops which can be achieved by classifying of fruit by using modern technique in the field of Deep Neural Networking that involves CNN, YOLO etc. which provides a helping hand to farmers to optimize their farming patterns and boost yields by allowing them to grow taller fruit trees that can be harvested by classifying based on the condition of fruit and saving significant labour cost. In conclusion, the proposed methods are highly acceptable and recognizing the necessity, and unresolved classifications.