cropsia — drone imagery + CV to detect crop disease. pitch deck.
Cropsia leverages drone-collected aerial imagery and computer vision to identify crop diseases early, helping farmers prevent significant production losses. the system classifies 38 distinct plant diseases from a dataset of 87,000+ images and delivers actionable recommendations through a farm management app.
the global challenge:
crop disease causes an estimated $220 billion in losses annually — roughly 40% of global crop production, according to the FAO. the problem isn't just economic: it's a food security crisis. early identification is critical, but visual inspection at scale across thousands of acres is practically impossible for a human. that's the gap Cropsia fills.
the system:
drones collect high-resolution aerial images of farmland, including NDVI (normalized difference vegetation index) imagery that highlights plant health through spectral analysis. these images feed a convolutional neural network trained on 87,000+ images of healthy and diseased plants. the model classifies 38 different plant diseases and generates targeted treatment recommendations for each diagnosis.
farmer interface:
the output isn't just a classification — it's an actionable recommendation delivered through a farm management app. farmers receive real-time alerts, field analytics, and a cost analysis tool that estimates savings and ROI when deploying the monitoring system. the design is built for farm owners who are not technologists.
impact:
Cropsia democratizes access to agricultural monitoring that was previously available only to large industrial farming operations. by making this technology accessible to small and mid-scale farmers, it contributes to food security and farmer livelihoods across diverse geographies.