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  • Lan Zhao
  • Jian Jin
  • Xiaohui Carol Song
  • Liangju Wang



Project TitleSmartphone Based Handheld Plant Phenotyping System with GIS Data Services
Track Code2017-JIN-67865
Short Description

Jian Jin

Purdue Phenotyping Tech Lab

Purdue Agricultural and Biological Engineering

Tagsagriculture, Computer Technology, Crop Improvements, crop management, farming, Mobile Apps, sensors, smartphones
Posted DateJul 11, 2017 4:41 PM


Lan Zhao
Jian Jin
Xiaohui Carol Song
Liangju Wang

Smartphone Based Handheld Plant Phenotyping System with GIS Data Services


Over the past decade, rapid development of plant phenotyping technologies targeted the research community and research-based users. Academia and industry have both invested heavily in developing plant phenotyping systems that are faster and more precise at predicting plant stresses, nutrition levels, growth stages, and yield performance. While farmers would greatly benefit from these evolving plant phenotyping systems, it remains a challenge given their high cost, out-of-date sensor technologies, and poor signal quality. Most farms manually check plant health, which lacks precision and efficiency. With 2.8 billion acres of farmland currently under cultivation worldwide, farmers need access to phenotyping research so they can predict plant stress, nutrition, and yield. There is a need for an affordable/reliable plant phenotyping technology.

Technology Summary

Researchers at Purdue University have developed a handheld plant sensor that wirelessly connects to a smartphone, which gives farmers access to a Purdue developed database of plant health prediction models, which Purdue continually improves and updates. An app processes the comprehensive plant health data within seconds of taking the measurement and sends it immediately to the GIS server together with the geolocation and time stamp data. Providing the georeferenced plant health data to various data services enhances their survey-based data by combining reliable plant health data with farm-level information on agricultural production, household income, and child nutrition and health.


  • Easy to use and inexpensive
  • Provides analysis within seconds
  • Access to prediction models
  • Includes geolocation data

Potential Applications

  • Farming
  • Agriculture research
  • Social workers

Stage of Development

Prototype testing

Web Links

For additional information, please contact

Intellectual Property

Patent Number Issue Date Type Country of Filing
None None Provisional United States