Key Learnings & Impact
Impact
AgriMap: Identifies multiple common crop defects and attributes and is tailored to the top crops grown in the US.
AgriBot: Streamlines farmer information search experience by giving just one concise, relevant answer per question.
AgriMed Overall: A one-stop shop for farmers remediating problems on their farms. No other applications on the market today combine this set of features.
Top Technical Challenges
Handling the limitations of the agriculture-vision dataset such as class imbalance, low resolution of the training images, physical overlap and visual similarities between classes, and inability to access the original test dataset
Expensive to train, even more expensive to deploy
Customizing LangChain pipelines to add features, and then balancing those features with functionality
Researching and implementing compatible AWS and Streamlit architecture
Future Work
Incorporate unsupervised learning techniques to leverage larger unlabeled, raw cropland image dataset. We would utilize this supplemental data to improve learning of the CV model.
Retrain the model with higher resolution training images (stitch together groups of original, lower resolution images). This would allow the model to better learn high-level contextual information from each training image.
Obtain more RAG data - RAG may have been more effective with higher quality, more relevant data, but obtaining it was outside the scope of our project