The Ministry of Agriculture and Farmers Welfare has introduced CROPIC

Concerning the Compilation of Real Time Observations and Images of Crops (CROPIC):

What does CROPIC mean?

CROPIC is an acronym for Collection of Current Observations & Images of Crops.

It is an initiative based on a mobile app to capture images of standing crops and evaluate them using AI-driven image recognition.

Created By: Created by the Union Ministry of Agriculture and Farmers’ Welfare as part of the Fund for Innovation and Technology (FIAT) under PMFBY.

Goals of CROPIC:

• Monitoring the growth phases and health of crops in real-time.
• Timely detection of plant stress and possible reduction in yield.
• Automated evaluation for prompt insurance claims under PMFBY.
• Create a database of cropped image signatures for use in machine learning models.
• Encourage digital evolution and robustness in the agricultural sector.

How Will CROPIC Function?

Farmer-Directed Photo Submission: Farmers will submit images of their crops 4–5 times throughout the crop cycle via the CROPIC mobile app, guaranteeing real-time, on-the-ground data collection.

AI-Driven Image Evaluation: These images are analyzed by an AI cloud system that employs computer vision to identify crop varieties, growth phases, stress indicators, and potential damages.

Diagnostic Output Creation: The model produces accurate diagnostics such as crop status, development stage, stress signals, and extent of loss using visual cues.

Online Dashboard for Officials: A consolidated digital interface presents analyzed information for district/state-level officials to monitor crop health and new threats.

Assistance for Insurance Claim Verification: The reviewed images act as confirmable proof to facilitate quick, clear, and automated handling of PMFBY compensation claims.

CROPIC's Main Characteristics:

Crowdsourced Data Gathering: Information is obtained straight from farmers via mobile applications, guaranteeing local involvement and broader inclusivity of on-the-ground agricultural conditions.

AI-Driven Photo-Analytics Engine: The system combines image recognition and machine learning to detect diseases, pest infestations, or anomalies that impact yield.

Visual Monitoring Dashboard: Real-time analytics are represented and displayed on a digital dashboard for authorities to take proactive action.