Associate professor, Penn State, specializing in entomology and Biology
AgroTechnology, Machine Learning, AI
We are providing solutions to farmers and extension workers by leveraging advances in AI, mobile phones, drones, satellites, and nanotechnology. We don't think technology alone is the solution, but we do believe they have the potential to help smallholder farmers.
Provide critical support to farmers to overcome crop disease, manage the fluctuations in the climatic condition, and save crops from damage. Create a knowledge repository or a platform for providers to share critical information to growers across the globe.
Application will work in tandem with the camera of the device to capture images of plants with disease and give the user a preliminary diagnosis with highest level of accuracy. Q&A forum connecting users across the globe to pose questions about farming.
This is among the largest free library of science-based knowledge on plant diseases. The rapidly growing site covers 154 types of crops and more than 1,800 diseases and offers an all new image database.
A computerized plant diagnostic system with an intelligent algorithm capable of diagnosing 26 diseases in 14 crops with almost 99 percent accuracy rate. In essence, computers have developed a knack and have been "trained" to diagnose plant diseases by quickly and accurately comparing the images of healthy and diseased leaves.
Get strategic support and counselling to assist farmers get more information about climate-resilient crop varieties, cost-effective irrigation methods, and flood mitigation and soil conservation strategies, as well as several industry best practices.
Upon download, the app does not require wireless access to cellular data or remote computing power.
"PlantVillage Nuru" can fetch data from the United Nations' WaPOR (Water Productivity through Open access of Remotely sensed derived data) portal. This database integrates 10 years' worth of satellite-derived data from NASA and computes critical metrics related to crop productivity according to available water.
Farmers don't need much technical knowledge or literacy to use the app; they simply point a phone at the infested crop and the app will provide an accurate diagnosis using the talking AI assistant, Nuru.
Used by United Nations across 70 countries and 21 languages to help growers manage the invasive fall armyworm.
Malawi, Egypt, Togo, Guinea
Uganda, Kenya, Pakistan, India
The Penn State researchers and experts extensively tested the performance of the machine-learning models with locally sourced smartphones in high light and temperature settings typically found in an African farm. These tests showed the app was twice as good as human experts providing accurate diagnoses, and it phenomenally increased the ability of farmers to discover problems on their farms.
Downloads on a single day
Improvement in Yield
Diagnose crop diseases in the absence of even an internet connection
Seamless integration of TensorFlow, an open-source software fundamentally used for numerical computation using data flow graphs.
Providing a high accuracy level for disease detection.
Comprehensive integration of satellite weather data to predict conditions in future.
Power BI to present disease data records on the map. This information will empower scientists at Penn State to make decisions and inform the respective government to take preventive steps.