Aakansha Jagga

Inspiration

It is inspiring to see how agriculture in India provides livelihood and opportunities for nearly 60% of the population. The bond between Indians and agriculture makes it a vital industry. Hence, identifying diseases in plants is crucial for the success of agriculture.

What it does

Identifying plant diseases visually is a challenging and time-consuming task that can only be done in specific areas. However, with the help of automatic detection methods, the process becomes much easier, quicker, and more accurate. This system's primary goal is to identify leaf diseases and alert farmers, enabling them to use the appropriate pesticides on affected leaves. This approach significantly reduces the risk of nearby leaves becoming infected in a short amount of time. By leveraging image processing, we can quickly identify infected areas in the leaves, making it a highly effective solution for farmers.

How we built it

Our project harnesses the power of artificial intelligence and machine learning to detect plant diseases with exceptional speed and accuracy. We deploy the Convolutional Neural Network (CNN) Model to identify the affected area and diagnose the disease with the highest degree of precision. Through advanced deep-learning techniques, the CNN model has been developed to identify and diagnose plant diseases by analyzing simple leaf images of healthy and diseased plants. This enables the timely use of appropriate fertilizers to prevent further damage caused by pathogenic viruses. To aid in image classification and model generation, we utilized a software called 'Roboflow.'

Challenges we ran into

The project's foundation lies in the machine learning model and images. The journey towards success involved overcoming two challenges - finding a suitable image dataset to train the model annotating the data, and achieving higher accuracy in detecting the disease. But with perseverance, dedication, and proper training and testing, these obstacles were solved.

Accomplishments that we're proud of

The prototype's high accuracy is evident in its successful identification of major diseases such as power and rust from uploaded images. This project is a resounding success.

What we learned

It's exciting to consider how AI, with the assistance of machine learning, will continue to aid us in object detection and classification in the future.

What's next for Aakansha Jagga

I have been considering the possibility of further developing this project by introducing a range of new features, and ultimately, making it accessible solely to individuals.

Built With

• ai
• machine-learning
• object-detection
• python
• roboflow