Self biographies are weird but here goes: I’m Murphy Elo, a firm believer in the power of technology to tackle real-world problems.
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Overview This article is the first in a series of tutorials that will cover the development of a rice leaf disease detection web application for AgroVisionLabs, a fictional company made up for the purpose of this project. It will aim to develop a Rice Disease Detection that allows farmers to upload rice leaf images for disease detection. The application will leverage image recognition and cloud-native tools to achieve objectives. This article will introduce the system’s objectives, explore functional and non-functional requirements, and outline the architecture and workflow. ...
Overview This guide will walk you through the process of setting up and running the Rice Disease Detection application on your local machine. Prerequisites Before you begin, ensure you have the following installed: Node.js npm (Comes with Node.js) Git AWS account credentials Step 1: Clone the Repository Open your terminal Clone the repository: git clone https://github.com/amanlikemurphy/rice-detection-app.git Step 2: Install Dependencies Run the following command to install the dependencies: npm install Step 3: Configure Environment Variables The application uses AWS Rekognition, S3, and DynamoDB. You will need to configure the environment variables for these services. ...
Setting up AWS S3 for Image Storage This tutorial will walk you through how AWS S3 bucket was configured for the Rice Leaf Disease Detection application. Prerequisites An AWS account with administrative access, preferably an IAM user with the necessary permissions. AWS CLI installed (optional but recommended) Have the repo cloned and the application running locally - Rice Disease Detection The following key requirements were considered when setting up the S3 bucket: ...
This guide details the steps taken to train a custom dataset, link it to Amazon S3, and integrate it with AWS Lambda function for real-time image analysis. Amazon Rekognition Custom Labels enables the training of custom computer vision models. Unlike the general object detection feature of Rekognition, Custom Labels allows us to define and train models for specific domain-based tasks—like, in our case, detecting diseases in rice leaves. Read more about Custom Labels here . ...
Configuring AWS Lambda for Rice Leaf Disease Detection This guide walks through setting up an AWS Lambda function that processes rice leaf images using AWS Rekognition and stores results in DynamoDB. Prerequisites AWS Account with administrative access S3 bucket already configured (refer to S3 setup guide) DynamoDB table created (refer to DynamoDB setup guide) Rekognition model already trained and deployed (refer to Rekognition setup guide) Basic understanding of JavaScript/Node.js The Lambda function for this project is responsible for the following tasks: ...