AgriMed: Mitigating Cropland Defects

Problem & Motivation

The increased frequency and severity of extreme weather events due to climate change threatens the health of cropland. At the same time, agriculture is one of the largest sources of greenhouse gas emissions contributing to climate change. Precision agriculture leverages multimodal data to empower farmers to optimize crop yields, while reducing use of greenhouse gas emitters like fertilizer. However, many existing precision agriculture solutions are costly to implement and maintain. In the US, small family farms make up the majority of farms but a minority of the profits earned. Small-scale farmers struggle most to afford technologies that can improve their crop yields and, ultimately, their profit. 

AgriMed is a precision agriculture tool that empowers small-scale farmers to optimize crop yields and reduce use of harmful pesticides, all for a fraction of the cost of existing tools. AgriMed takes in multispectral aerial cropland imagery and maps out the incidence of common crop defects such as nutrient deficiencies and weed clusters. Users can then consult AgriMed’s chatbot to develop targeted mitigation strategies for these defects and generate other recommendations for managing their farms. AgriMed is a one-stop solution to make precision agriculture accessible to small-scale farmers.

Data Source & Data Science Approach

Datasets:

• Agriculture-Vision Database: High-quality and open-source multi-spectral aerial image dataset. Contains expert-labeled RGB and NIR images of corn and soybean cropland in Iowa and Illinios. 

Approach:

• Semantic Segmentation Model: Segment multi-spectral aerial images of corn and soybean cropland into categories of common crop defects. 
• LangChain & RAG-Based Chatbot: 

Evaluation

Our solution is evaluated through:

• Mean Intersection Over Union (mIOU): Optimizing overlap between the ground-truth segmentation maps and the predicted segmentation maps. 
• User Feedback: Conducting surveys and pilot tests to assess usability and the feasibility of recommendations.

Key Learnings

Impact

Acknowledgements

We extend our gratitude to our project advisors, UC Berkeley W210 course instructors, and the organizations providing access to datasets. Special thanks to Chris Padwick at Blue River Technologies.