Updated on November 22, 2022

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Created on October 26, 2022

Plantix

Upcoming Update

Plantix is a digital agritech solution that processes and classifies electronic photos depicting pests and plant diseases.

Developed By Unknown
Content Partners
Unknown

Author

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.

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