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%.
Highlights
INTRODUCTIONFruits and vegetables are one of the cultivations in India
2.3 Data Pre-processing Data preprocessing is achieved in two steps, one is for Machine Learning Algorithms (ML), and another is for Deep Learning Algorithms (DL)
The data needs to be pre-processed prior to it can be used with a Machine Learning algorithm for the classification
Summary
Fruits and vegetables are one of the cultivations in India. Fruits and vegetables reported for over 3.7 trillion Indian rupees in the Indian economy in the year 2018. This area contributed about 28 percent to the GVA of crops that same year, an growth from 24 percent in the year 2012. Classification of fruits and vegetables is one of the key challenges in the agricultural field. It consumes a lot of money, time and needs more manpower. 2. METHODOLOGY Proposed different architecture or algorithms are used to solve the fruit/veg classification problem.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
