AgriAid

Inspiration

Agriculture is the backbone of many economies, especially in developing nations like India. Yet, farmers often face critical challenges when it comes to identifying crop diseases early. The lack of timely diagnosis leads to reduced yield, financial strain, and food insecurity. As students passionate about AI and its potential to drive impact, we wanted to build a tool that empowers farmers by bridging this gap. That’s how AgriAid was born — a smart, accessible, and reliable disease prediction tool for four major crops of India: wheat, rice, corn, and potato.

What it does

AgriAid leverages deep learning and AI to detect plant diseases from leaf images with remarkable accuracy. By simply uploading an image of an infected leaf, farmers or agricultural experts can instantly receive a prediction of the disease, allowing for faster and more effective treatment. The model currently supports wheat, rice, corn, and potato, and achieves over 90% accuracy — reaching up to 98% in some cases.

How we built it

We started by collecting and curating high-quality datasets of diseased and healthy plant leaves from trusted open-source platforms such as PlantVillage and Kaggle. We preprocessed the images using data augmentation and normalization techniques to improve model generalization. For the architecture, we implemented a Convolutional Neural Network (CNN) , Maximum Pooling and Artificial Neural Network (ANN) . We evaluated the models across multiple metrics (accuracy, precision, recall) and optimized for performance across each crop category.

Challenges we ran into

1. The major problem was the Lack of GPU and good device. Due to this we were not able to train more high quality image data resulting in somewhat low accuracy and exorbitant amount of time to train the data.
2. Class imbalance in datasets was a major hurdle, especially for crops with fewer disease categories.

3.Achieving consistently high accuracy across all crops while keeping the model lightweight was technically demanding.

Accomplishments that we're proud of

Achieved over 90% accuracy across all four crops, with certain categories reaching up to 98%.

What we learned

This project deepened our understanding of real-world Deep Learning deployment — from data preprocessing to model optimization . We also learned how to handle domain-specific challenges like class imbalance and variability in field data.

What's next for AgriAid

Expand crop and disease coverage to include fruits, vegetables, and region-specific crops.

Develop a mobile version and web version of AgriAid to improve field accessibility.

Integrate voice support in local languages to make the tool even more inclusive.

Collaborate with agricultural NGOs and research institutions to pilot test AgriAid in rural communities.

Built With

• deeplearning
• kaggle
• machine-learning
• python