GIT-hackforEnvironment-ML

This repo is created for the submission of our Machine Learning model code for the Girls in Tech - Hack for Environment challenge

About our product (It's in the development pipeline:)

We are empowering urban gardeners to battle climate change using HomeGrown, our AI-powered application. Urban Gardening sits at the center of climate change with a greenhouse gas emission gap to be 11 giga tonnes between expected agricultural emissions in 2050 and the target level needed to hold global warming below 3.6°F, the level necessary for preventing the worst climate impacts. Various studies from leading organizations suggest that Urban Gardening is one of THE sustainable ways to battle climate change.

What do we do?

We are looking to develop an end-to-end solution with our Artificial intelligence application. Based on the user's region, we have developed a crop recommendation model tailored to that specific area. The crop recommendation model, known as 'GardenPal' works by considering parameters such as temperature, humidity, pH value, and rainfall rate to predict suitable crops for the region.

Once users have selected the ideal crops to grow in their region, we establish milestones to monitor plant growth. We accompany gardeners on their journey through a series of milestones, acting as their gardening friends to make the process enjoyable. We send reminders throughout the lifecycle of the plant to celebrate milestones and give instructions on how to take care of it. To support plant growth, we are looking to integrate a plant diagnosis model with Plantix, an application interface that utilizes the mobile camera to predict crop diseases during the growth process.

After predicting a disease, users can learn about the plant diagnosis procedure through our Jarvis chatbot. This chatbot is integrated with ChatGPT, a large language model chatbot developed by OpenAI. Jarvis chatbot can address user queries and provide guidance throughout the diagnosis process

Machine Learning model:

We developed our machine-learning model prototype using Pandas and Numpy for data analysis and Linear Algebra respectively. We also imported libraries like Matplotlib and Seaborn as libraries for data visualization.

This is where we got the dataset from: https://www.kaggle.com/datasets/atharvaingle/crop-recommendation-dataset

Do reach out to me for any queries at mgmsreviji@gmail.com