Indian Fruits Recorgization And Saturation Enhancement Using Deep Learning Method

Authors: Divya Rathore, Prof. Ashish Ranjan

Certificate: View Certificate

Abstract

Nowadays in agriculture industry exporting the fruits to other countries in bulk quantities is a difficult task. In this field farmers need manual inspection. Our system helps the farmers to pack their fruits as soon as possible by detecting the fruits and vegetable and identifying the disease this helps the farmers to save their time and they can delivery fruits and vegetable as soon as possible. We use CNN algorithm for fruits and vegetable detection and disease identification. Using neural network the image is segmented which is followed by extraction of some features from the segmented image finally fruits and vegetable image is identified and labeled.

Introduction

Food is one of the most fundamental needs for human life and existence. The agricultural sector works day and night to fulfill the food-related needs of the human population. Further, agriculture also plays an important role in the economic development of countries [1]. The related sectors are always looking for improvements of all kinds in all stages of agricultural activity. Over the last century, more and more technology has been adopted, transformed, and optimized in pursuit of increased yields [2]. Some of the recent examples include techniques of smart agriculture using artificial intelligence and machine learning methods [3,4], precision agriculture using information and communication technologies augmented with blockchain technology [5], and the use of biosensing technology [6]. Fruits are considered an important part of the human diet and their cultivation and production is a significant part of the overall farming activity.

Conclusion

The proposed project is able to recognize the fruit based on the features like shape, colour, and texture. This increases the knowledge of common people about some rare and unknown fruits. The project is mainly concentrating on reducing human effort and making human life easier. Fruit recognition will be able to reduce the current ongoing problems. It reduces confusion among the particular fruit. Future work that can be added to this project may be the development of a web app. Here the user can use this application anytime anywhere. An image processing predicated solution is proposed and evaluated for the detection and relegation of fruit quality. The proposed system is composed of mainly three steps. In the first step the data is accumulated. In the second step, features are extracted and the machine is trained. In the third step, the trained machine will identify the fruit image and label it. In the fourth step it will identify the disease associated with the identified