Red Ripe Ventures

Our website
DL using to predict Plant disease
DL using to predict Plant disease
DL using to predict Plant disease and thier output
DL using to predict Plant disease and thier output
DL using to predict Plant disease and thier output
our service
our website
our virtual assistant
working with chatbot
farmers guidence
ml prediction
ml prediction for profit
farmers guidence
ml prediction

.## Inspiration To inspire increased tomato cultivation in Korea, we can highlight its numerous benefits. Tomatoes are a versatile and nutritious crop, rich in vitamins and antioxidants. Encouraging local cultivation can reduce import dependence, bolstering food security. Additionally, promoting sustainable farming practices can address environmental concerns while creating economic opportunities. By showcasing the success stories of local farmers and providing support through training and subsidies, we can stimulate interest and investment in tomato cultivation. With its potential for delicious dishes, health benefits, and economic growth, tomatoes can become a thriving agricultural sector in Korea, fostering a brighter and more sustainable future

What it does

To address these issues, this project seeks to develop data-driven solutions. Firstly, we will gather historical climate data, soil quality information, and pest incidence records. Machine learning algorithms will be applied to this dataset to predict weather patterns, detect pest outbreaks, and optimize irrigation and fertilization schedules. Deep learning models will be employed for image recognition to monitor plant health and growth.

Additionally, we will create a user-friendly chatbot application for farmers that provides real-time recommendations based on data analysis, helping them make informed decisions. By integrating these technologies into the farming process, we aim to increase tomato yields, improve quality, and reduce resource wastage. The project's success will not only benefit local farmers but also contribute to the overall food security and sustainability goals of South Korea while fostering innovation in the agricultural sector.

How we built it

Using machine learning and deep learning models to predict and detect our problems To guide farmer how to increase yield and how to get more profit spends less area and fertilizers , we guide them to farm using organic method using AI , ML, DL and other technology like sensors.

Challenges we ran into

To train the deep learning model, we had to use cloud service and its GPU such as Google Colab as we didn’t have a sufficient hardware system. We faced problems in deploying a website that is flask enabled and some version incompatibility during this process. It took some time to clear them out and deploy successfully. To ensure good accuracy, obtaining good datasets and labeling them is a challenge especially if a lot of them are required