Walnut Programme | Agri-Excellence

Programme Overview

The walnut programme is organised into two coordinated activities so orchard automation and food safety screening can move forward together.

Activity 1 focuses on smart harvesting, inshell segregation and machine vision grading.
Activity 2 focuses on rapid aflatoxin detection and deployable screening for compliance and export readiness.

Interactive Activity Switcher

Use the activity buttons below to move between the two walnut tracks without leaving the page.

Open Activity 1 Open Activity 2

Activity Explorer

Combined Walnut Activities

Each activity stays clearly separated inside one responsive page, with its own objectives, methodology, outcomes and team.

Walnut Activity 1

Smart harvesting, shell analysis and kernel grading

Integrated automation, sensor fusion and machine vision for efficient walnut handling from orchard to quality sorting.

3 objective streams

AI enabled harvesting and grading

56% quality inshell output growth target by 2035

Objectives

Development of a smart harvester for green walnuts.
Development of a solution for segregating in-shelled walnuts based on shell thickness and shrivelled from healthy ones.
Development of colour and size grading solutions for walnut kernels.

Walnut activity 1 methodology — Methodology

Integrated automation, sensing and machine vision workflow for walnut harvesting, segregation and grading.

Objective 1

First of its kind AI enabled mechanical harvester system for walnut.
AI-enabled shaking and collection control for optimized nut detachment with minimal tree stress.
Portable and lightweight system, suitable for hilly terrain and dispersed walnut plantations.

Benefit: Enhances production of quality inshelled walnuts from 3,07,000 tonnes in 2024 by 56% in 2035.

Objective 2

First AI-enabled non-destructive segregation based on shell thickness and internal kernel integrity.
Multi-sensor fusion approach using image processing, acoustic response, and weight-density analysis.

Benefit: Accurate differentiation of shrivelled vs. fully developed walnuts leading to higher grading accuracy.

Objective 3

High-precision machine vision system for consistent colour and size-based grading beyond human visual capability.

Benefit: Improvement in colour and size grading accuracy by 40-50%, reducing human error and ensuring consistent quality standards.

Project Team

Activity 1 Team

Mechanical harvesting, smart segregation and grading intelligence are supported by a broad field and engineering collaboration.

Principal Investigator

Dr. Syed Zameer Hussain

Professor, SKUAST, Kashmir

Lead Collaborator

Mr. Prakash Vinod

Joint Director

Project Manager

Dr. Subhankar Mukherjee

Automation and implementation coordination

SKUAST, Kashmir

Quality assessment, food science and agricultural engineering expertise for smart walnut industry development.

Dr. Sajad Mohd Wani

Professor, Division of FST

Dr. Tawheed Amin

Assistant Professor, Division of FST

Dr. Quraazah Akeemu Amin

Assistant Professor, Division of FST

Dr. Syed Rameem Zahra

Assistant Professor, CAIML

C-DAC / CMTI Collaboration

Sensor fusion, machine design and grading intelligence for harvesting, segregation and kernel quality assessment.

Mr. Girish Kumar M

Scientist-E

Mr. Gopi Krishna S

Scientist-D

Mr. Narendra Reddy T

Scientist-D

Mr. Vithun S N

Scientist-D

Mr. Harikrishna S T

Scientist-D

Mr. Manjunath M A

Scientist-D

Mr. Setti Suresh

Scientist-B

Dr. Souvik Pal

Senior Project Engineer

Mr. Arindam Niyogi

Senior Research Scholar

Mr. Soumyadeb Bhattacharyya

Senior Research Scholar

Mr. Koustuv Ghosh

Research Engineer

Mr. Prasanta Das

Software Developer

Walnut Activity 2

Portable aflatoxin detection for safer walnut trade

Rapid neurotoxin screening designed for on-site use, faster compliance checks and stronger export readiness.

5-10 minutes per test

On-site screening without lab dependency

1550 MT export growth target by 2030

Objective

Upgradation and commercialization of neurotoxin detection system developed by C-DAC Kolkata for rapid aflatoxin detection in walnut kernels.

Walnut activity 2 methodology — Methodology

Portable sensing and analytics workflow for on-site neurotoxin screening of walnut kernels.

Deliverables

Portable ready-to-use neurotoxin (aflatoxin) detection technology.
On-site testing capability, eliminating dependency on laboratory infrastructure.
Rapid colorimetric sensing system providing results within 5 to 10 minutes.
Affordable per-test cost, suitable for large-scale commercial screening.
Integrated digital reporting and traceability, compatible with export compliance systems.

Benefits

Boost export potential of walnuts from 1069 MT in 2022 to 1550 MT in 2030.
Ensures food safety compliance with national and international standards (FSSAI, Codex).
Reduces contamination-related losses by up to 30-40% through early detection.
Improves market value and export acceptability of walnuts.
Minimizes health risks associated with aflatoxin exposure in consumers.
Scalable technology that can be adapted to other mycotoxin-affected crops like groundnut.

Project Team