Plantation crops such as tea, coffee, coconut, and areca nut are vital to the Agricultural economy, especially in tropical and subtropical regions. However, they are often affected by late detection of foliar and systemic diseases, leading to yield losses and increased pesticide use. Manual scouting is laborious, time-consuming, and inefficient, particularly for small and marginal farmers. This research proposes to develop an integrated drone-based early disease detection and precision management system. Equipped with multispectral and thermal imaging, drones will be used for real-time crop surveillance, early symptom detection, geo-referenced mapping, and targeted application of control measures. The project will integrate image analysis, AI models, and GIS tools to create a user-friendly disease management solution. The expected outcome is a scalable, cost-effective, and eco- friendly system that improves productivity, reduces chemical use, and supports sustainable agriculture in plantation systems.
Primary Objective:
• To invent and validate a drone-assisted system for early detection and targeted
management of diseases in plantation crops.
Specific Objectives:
1. To develop drone-based imaging protocol for early diagnosis of major diseases in oil
palm and coconut using multispectral and thermal sensors.
2. To validate drone-based imaging protocol for early diagnosis of major diseases in oil
palm and coconut through field diagnosis.
3. To develop an alternative drone-assisted spraying schedule integrating fungicides and
biocontrol agents (BCA) for effective disease management.
4. To evaluate the effectiveness of drone-assisted spraying schedule through multi-location field trials across in diverse agro-climatic zones.
Background:
Traditional disease monitoring in plantation crops is limited by human inefficiency, terrain
constraints, and delayed symptom visibility. This is especially critical for smallholders, where delayed actions result in major losses. Advanced technologies such as drones, remote sensing, and AI offer new tools to overcome these challenges.
Rationale:
• Drones provide rapid and repeated coverage of large fields with minimal labour.
• Multispectral/thermal imaging captures stress signals invisible to the naked eye.
• AI and machine learning facilitate precise identification and classification of disease
symptoms.
• Targeted management reduces overuse of fungicides and promotes sustainability.
• GPS integration ensures spatial accuracy and treatment traceability.
a) Commercial Potential:
• Can serve over 3 million ha of plantation crops in India.
• Applicable to smallholder-dominated systems in India, Sri Lanka, Indonesia, Vietnam, and East Africa.
b) Additional Potential Impacts:
i) Agronomic Impact:
• Improved disease surveillance and timely response.
• Minimized yield loss and better crop recovery.
ii) Resource-Use Efficiency:
• Reduced pesticide use via spot-treatment.
• Lower input costs due to precision application.
iii) Environmental Sustainability:
• Reduced chemical runoff and resistance build-up.
• Eco-friendly, IPM-compatible management.
iv) Economic Benefits:
• Enhanced farmer profitability via higher yields and reduced expenses.
• Employment generation in drone tech and data analytics sectors.
c) Target Geography:
• Plantation belts in Telangana, Karnataka, Kerala, Assam, Tamil Nadu.
• Potential replication in similar agro-climatic zones across Southeast Asia and Africa.
| Partner | Role | Personnel required | Duration | Gap Addressed |
|---|---|---|---|---|
|
Kaveri Drone
Academy |
Drone hardware,
pilot training |
2 certified
drone pilots |
Full
project |
Imaging protocol and
pilot expertise |
|
Bharat Rohan Airborne Innovations |
AI and software development |
2 AI developers |
1 year |
Algorithm development and image processing |
|
College of Agriculture, KU |
Field trials and disease validation |
2 research assistants |
2 seasons |
Agronomic evaluation
and management strategies |
|
Farmers/ Producer Groups |
Participatory trials and feedback |
3 local farmer collaborators |
2 seasons |
On-ground implementation and social validation |
Study Area:
Location: Palm tree and Coconut nut plantations in Telangana and Andhra Pradesh.
Climate: Humid-subtropical to tropical monsoon
Soil Type: Sandy loam and red sandy loam
| Objective / Duration (months) | 0–3 | 3–6 | 6–9 | 9–12 | 12–15 | 15–18 | 18–21 | 21–24 |
|---|---|---|---|---|---|---|---|---|
| 1. To develop drone-based imaging protocol for early diagnosis of major diseases in oil palm and coconut using multispectral and thermal sensors. |
||||||||
| 2. To drone-based imaging protocol for early diagnosis of major diseases in oil palm and coconut through field diagnosis. |
||||||||
| 3. To develop an alternative drone- assisted spraying schedule integrating fungicides and biocontrol agents (BCA) for effective disease management. |
||||||||
| 4. To evaluate the effectiveness of drone-assisted spraying schedule through multi- location field trials across in diverse agro-climatic zones. |
| Particulars | Year I (INR) | Year II (INR) | Total (INR) |
|---|---|---|---|
| Capital (Drones, Imaging Tools) | ₹5,00,000 | – | ₹5,00,000 |
| Manpower (Research Staff) | ₹4,80,000 | ₹4,80,000 | ₹9,60,000 |
| Consumables (Batteries, tags, etc.) | ₹2,00,000 | ₹2,00,000 | ₹4,00,000 |
| Travel | ₹2,00,000 | ₹2,00,000 | ₹4,00,000 |
| Field Trials | – | ₹50,000 | ₹50,000 |
| Publications, IP Filing | – | ₹3,00,000 | ₹3,00,000 |
| Total (A) | ₹13,80,000 | ₹12,30,000 | ₹26,10,000 |
| Institutional Overheads (10%) | ₹2,61,000 | ||
| GST @18% on Total | ₹5,16,780 | ||
| Total Budget (A + B + C) | ₹33,87,780 | ||
Kaveri University was established as per the Telangana Private Universities (Establishment & Regulation) Act, 2018 under section 3.
