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Swapnil Dadabhau Daphal's Projects

apple-leaf-disease-detection icon apple-leaf-disease-detection

Build a deep learning model that can classify the different types of diseases based on Apple leaf using image enhancement, segmentation, and feature extraction techniques.

awesome-object-detection icon awesome-object-detection

Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html

bottle-inspection icon bottle-inspection

This project simulates the inspection of shampoo bottles on a conveyor belt to verify if the bottle cap has been placed properly or not. It uses openCV to make the image processing and has an intuitive supervisory interface to display history of misplacement.

byspire icon byspire

🌱 This project aims to bridge the current gap between controlled-environment agriculture and the computer vision and robotics fields. The intended outcome is to develop an open-source toolkit for plant analysis that can be adapted to container-size smart farms, vertical farms, or even small window farms at home. The project will enable farmers world wide to automate and optimize their food production.

cifar10 icon cifar10

Predict CIFAR-10 labels with 88% accuracy using keras.

cse1002_c_cpp icon cse1002_c_cpp

This repository includes all the codes that were a part of the Object Oriented Programming Course - CSE1002. The Languages used are - C and CPP

graduation-project-detection-of-plant-leaves-diseases icon graduation-project-detection-of-plant-leaves-diseases

The project consists of an android application that is used by the house gardeners and anyone who owns a plant in their houses to take a picture of a plant or choose a picture from their mobile. Moreover, the application screens the image to detect a disease based on the disease symptoms by processing it in an image processing system and predict the type of disease and displays the result to the user.

krishi-unnati icon krishi-unnati

A Mobile Application for Plant and Crop Disease Detection using Convolutional Neural Networks.

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