DOI: 10.31220/agriRxiv.2021.00075

•  

  Abstract: Abstract Agriculture is one of the important parts of Indian economy. Agricultural field has more contribution towards growth and stability of the nation. Therefore, a current technologies and innovations can help in order to experiment new techniques and methods in the agricultural field. At Present Artificial Intelligence (AI) is one of the main, effective, and widely used technology. Especially, Deep Learning (DL) has numerous functions due to its capability to learn robust interpretations from images. Convolutional Neural Networks (CNN) is the major Deep Learning architecture for image classification. This paper is mainly focus on the deep learning techniques to classify Fruits and Vegetables, the model creation and implementation to identify Fruits and Vegetables on the fruit360 dataset. The models created are Support Vector Machine (SVM), K Nearest Neighbor (KNN), Decision Tree (DT), ResNet Pretrained Model, Convolutional Neural Network (CNN), Multilayer Perceptron (MLP). Among the different models ResNet pretrained Model performed the best with an accuracy of 95.83%.

  Key words: fruits and vegetables; the model; model; resnet; agricultural field; pretraine

  Submit time: 1 January 2021

  Copyright: The copyright holder for this preprint is the author/funder, who has granted agriXiv a license to display the preprint in perpetuity.
• html

• 图表

• Akhil Wilson, Raji Sukumar, N. Hemalath. Machine learning model for rice yield prediction using KNN regression.. 2021. doi: 10.31220/agriRxiv.2021.00070

  Akhil Wilson, N. Hemalatha, Raji Sukuma. Computational prediction model for pepper yield prediction using support vector regression.. 2021. doi: 10.31220/agriRxiv.2021.00069

  Yusuke Toda, Goshi Sasaki, Yoshihiro Omori, Yuji Yamasaki, Hirokazu Takahashi, Hideki Takanashi, Masayuki Tsuda, Hiromi Kajiya‐Kanegae, Raúl López-Lozano, Hisashi Tsujimoto, Akito Kaga, Mikio Nakazono, Toru Fujiwara, Frédéric Baret, Hiroyoshi Iwat. Genomic prediction of green fraction dynamics in soybean using UAV observations.. 2021. doi: 10.31220/agriRxiv.2021.00097

  Anna Rini, N. Hemalatha, Raji Sukuma. Decision tree classification of digital soil, weather, crop mapping and yield prediction using linear regression with region influences.. 2021. doi: 10.31220/agriRxiv.2021.00072

  Rohit Raj Jha, Gopal Prasad Khanal, Kavita Isvara. Factors affecting crop field use by Blackbuck in Krishnasaar Conservation Area, Nepal.. 2021. doi: 10.31220/agriRxiv.2021.00098

  C. A. Rose Mary, A. Raji Sukumar, N. Hemalath. Text based smart answering system in agriculture using RNN.. 2021. doi: 10.31220/agriRxiv.2021.00071

  Ilce Gabriela Medina-Meza, Joshua VanderWeide, Cristóbal Torres-Palacios, Paolo Sabbatin. Quantitative metabolomics unveils the impact of agricultural practices in grape metabolome.. 2021. doi: 10.31220/agriRxiv.2021.00029

  Munir Zia, A. Lodhi, Muhammad Zahid Aziz, Aftab Naseem, Waqar Ahmad, R. Murray Lark, Michael J. Watts, Martin R. Broadley, E.L. Ande. Soil fertility mapping and agronomic advice at the regional scale using private sector data.. 2021. doi: 10.31220/agriRxiv.2021.00113

  Moritz Laub, Lisa Pataczek, Arndt Feuerbacher, Sabine Zikeli, Petra Hög. Contrasting yield responses at varying levels of shade suggest different suitability of crops for dual land-use systems. A meta-analysis.. 2021. doi: 10.31220/agriRxiv.2021.00099

  Aida Safina Aridi, Ling ChinNyuk, Nur Akmal Ishak, M. Y. Nor Nadiah, Mohammad Faisal Ahmed, Yus Aniza Yuso. Structural FTIR analysis of cellulose functional groups isolated from Leucaena leucocephala pods using different bleaching agents.. 2020. doi: 10.31220/agriRxiv.2020.00026

• ID	Submit time	Number	Download
  1	2021-01-01	10.31220/agriRxiv.2021.00075V1	Download

• Public  Anonymous  To author only Submit Comments