Ai-powered weed identification and herbicide recommendation app for rice-wheat cropping systems

Overview

This solution is a mobile application designed to help farmers accurately identify weed species and select the most effective herbicide for control. It addresses a critical challenge in the rice-wheat cropping system, where recently introduced narrow-spectrum herbicides target only one or two major weed species, making correct identification essential for successful weed management. By providing a visual identification tool backed by artificial intelligence, the app reduces the risk of poor weed control caused by misidentification, saving farmers money and energy while improving crop productivity.

The app is being developed with validation focused on common weeds infesting rice and wheat crops across different growing environments and growth stages. It builds on existing agricultural technology infrastructure, including the Kisan App developed by Punjab Agricultural University, and aims to add an AI-driven identification interface for weeds, insect-pests, disease symptoms, and nutrient deficiencies.

Technical specifications

Key features:

• Comprehensive image database of weed species at multiple morphological and phenological growth stages
• AI-based image recognition allowing users to upload a photograph for weed identification
• Herbicide recommendation engine that matches identified weed species to appropriate control options, including narrow-spectrum herbicides such as bispyribac sodium for Echinochloa species and fenoxaprop for Digitaria ciliaris, Dactyloctenium aegyptium, and Acrachne racemosa
• Coverage of weed flora specific to puddled transplanted rice, direct seeded rice, and wheat cropping systems
• Integration potential with existing agricultural advisory platforms for broader crop production and protection support

Technology readiness level

The concept is at an early-to-mid development stage. The research team has identified the problem through extensive field queries at the Agricultural Information Technological Centre at Punjab Agricultural University and has outlined a validation plan involving the creation of a weed image database across different growth stages and environments. Future validation will focus on building and testing the AI identification interface for rice-wheat system weeds, including herbicide-resistant species such as Phalaris minor and Rumex dentatus in wheat. The app is not yet commercially deployed and requires further database development, AI model training, and field validation before market readiness.