Product Description
Plantix is a multi-lingual mobile application that diagnoses and classifies pest damage, plant disease, and nutrient deficiencies by capturing an image of the affected crop. Through their community platform, users can discuss possible causes and solutions with each other or with paid experts to monitor infestations and provide scientifically verified solutions. The product was designed and developed by the Plantix team in India.
Distributors / Implementing Organizations
Google PlayStore Partners: CABI, ICRISAT, CIMMYT, ZALF, Government of Andhra Pradesh
Manufacturing/Building Method
Unknown
Intellectural Property Type
Patent Protected
User Provision Model
Users can download the app from Google PlayStore
Distributions to Date Status
10 Million+ downloads
Design Specifications
This product was developed to allow: crop net, fertilizer calculator, pests and disease diagnosis, cultivation tips, and community platform.
Technical Support
Users will contact directly Plantix for any technical assistance.
Replacement Components
None
Lifecycle
Unknown
Manufacturer Specified Performance Parameters
Accuracy of disease detection and health monitoring.
Vetted Performance Status
Plantix claims to have automated diagnostics for over 350 globally identified plant diseases, pests, and nutrient deficiencies on more than 65 agricultural crops.
Safety
None
Complementary Technical Systems
Unknown
Academic Research and References
Rupavatharam, S and Kennepohl, A and Kummer, B and Parimi, V (2018) Automated plant disease diagnosis using innovative android App (Plantix) for farmers in the Indian state of Andhra Pradesh. Phytopathology (TSI), 108 (10). ISSN 0031-949X
Kothari, Jubin Dipakkumar. Plant Disease Identification using Artificial Intelligence: Machine Learning Approach. International Journal of Innovative Research in Computer and Communication Engineering 7.11 (2018): 11082-11085
Kumar, S. Aravindh, and C. Karthikeyan. “Status of Mobile Agricultural Apps in the Global Mobile Ecosystem.” International Journal of Education and Development using Information and Communication Technology 15.3 (2019): 63-74.
BARDHAN, TANNISHTHA, et al. “Science behind user friendliness of agricultural mobile apps: A study on readability.” The Indian Journal of Agricultural Sciences 92.1 (2022)
Wang, Sherrie, et al. “Mapping crop types in southeast India with smartphone crowdsourcing and deep learning.” Remote Sensing 12.18 (2020): 2957.
Compliance with regulations
Unknown
Other Information
This product has received various awards including: World Summit Awards, AGRI Awards, CeBIT Innovation Awards 2017, Data Driven Farming Prize, MIT Technology Review, The Better India, Forbes, BBC News.
Get more information about Solutions Library and its features.
Learn MoreHave thoughts on how we can improve?
Give Us Feedback