PashuTham: Wildlife Intrusion Detection and Prevention in Farmlands Using Artificial Intelligence

AiProff.ai's AI-Driven Initiative in Agricultural Security and Reforms

India is an agriculture-driven country. More than half of the Indian workforce is employed directly by the primary sector. Moreover, India ranks second worldwide in terms of farm outputs, where the agriculture sector contributes nearly 20.2% to the country's GDP. However, while there is a strong focus on improving crop yield, production, and productivity in general, India's agricultural sector grapples with multifaceted challenges, including human wildlife conflict. One of them is wildlife intrusion, which leads to substantial crop damage every year. Wild animals – such as elephants, wild boars, cows, monkeys, and deer – tend to migrate to nearby agricultural fields in search of food, causing huge damage to the crops; not only by eating but also by trampling the crops by foot. In some states like Coastal Odisha, the extent of crop damage due to wildlife has been recorded to 50-60%, and sometimes even 100%.

Predominantly, regions like Uttar Pradesh, Uttarakhand, and Haryana bear witness to these challenges, with farmers facing severe economic and emotional repercussions.

The Agricultural Challenge: A Glimpse of Reality

Farmers nationwide have consistently reported extensive crop damage caused due to wildlife. This, as stated earlier, results not only in financial losses amounting to crores (10s of millions) over the years but also increases emotional tolls on the farmers. Understanding such recurrent incidents, the Indian government annually announces compensations to these farmers while also devising strategies to counteract the market shortages arising from these unforeseen agricultural disruptions.

Key statistics further underscore the gravity of the challenge at hand:

• 1.65 Cr (~200,000 USD– Compensation in 2022, in Coimbatore Region.

• 32,500 / hectare – Government declared compensation for 50% damage to crops.

• 50000+ – Incidents of crop damage by wildlife animals annually in India.

Crop damage caused by wildlife has become a significant issue, impacting agriculture and leading to substantial losses for farmers. In Pune, India, human-wildlife conflicts are on the rise, with 341 incidents of losses reported between April 2022 and March 2023. As one of the leading rice-producing regions, such damage disrupts the agricultural economy and affects the staple food supply. Wildlife intrusion not only harms crops but also creates broader challenges for communities.

To address these challenges, the government has introduced compensation measures and expanded the PM Crop Insurance Scheme to include crop losses caused by wild animal attacks. This aims to provide financial protection to farmers facing unexpected losses due to wildlife, natural disasters, pests, and diseases, mitigating the economic impact and ensuring market stability.

Introduction to AiProff.ai's Pilot Initiative

At AiProff.ai, we believe Artificial Intelligence can play a pivotal role in addressing wildlife intrusion in agriculture. Under the leadership of Senior Data Scientist Nitin Saraswat, we are pioneering PashuTham, an AI-driven solution for wildlife intrusion detection and deterrence. This initiative aims to explore the feasibility, effectiveness, and scalability of AI technologies in mitigating agricultural disruptions. PashuTham is designed to ensure agricultural security through AI-powered crop and soil analysis while preserving ecological balance and avoiding harmful interventions.

Targeting a 1-hectare agricultural plot, PashuTham operates efficiently both day and night, prioritizing economic viability and ecological sustainability. The pilot solution is scalable across diverse regions and serves as a foundation for integrating advanced AI-based image and video analytics for agricultural landscapes. By analyzing existing solutions and addressing their shortcomings, we aim to craft a robust, innovative approach to resolving human-wildlife conflict and advancing sustainable farming practices.

Addressing Limitations of Current Solutions & Crafting a Pilot Solution

The persistent issue of wildlife intrusion in agricultural regions over the years has led to the development of various solutions. Each solution has its own set of challenges and limitations. In India, the following are the three primary methodologies that are currently being employed – clutch wires with a 10V battery, barbed wire installations, and manual surveillance by farmers. However, with PashuTham, our wildlife intrusion detection and deterrent solution, we aim to overcome these challenges with advanced AI-based crop analysis and soil analysis.

Clutch Wire with 10V Battery: This approach involves installing wires that deliver electric shocks to animals upon contact. While effective to an extent, it poses significant risks, including potential harm to humans. Challenges associated with this method include the frequent need for reinstallation, escalating costs, and safety concerns, particularly for children and smaller animals.

Barbed Wire Installations: Utilising barbed wires as a restraint has its drawbacks, notably the recurring labour and material costs associated with regular installations and removals. Given the seasonal nature of agricultural activities, the constant uprooting of these barriers proves both time-consuming and labour-intensive, posing additional challenges to farmers.

Barbed Wire Installations: Utilising barbed wires as a restraint has its drawbacks, notably the recurring labour and material costs associated with regular installations and removals. Given the seasonal nature of agricultural activities, the constant uprooting of these barriers proves both time-consuming and labour-intensive, posing additional challenges to farmers.

Manual Farmer Surveillance: Perhaps the most inefficient and perilous of all solutions, this method necessitates farmers to physically monitor their fields, rendering them susceptible to wildlife encounters, adverse weather conditions, and other hazards. This not only jeopardises the safety of farmers but also lacks scalability, as it demands continuous human intervention without offering a sustainable, long-term solution.

Prevailing agricultural security methods face challenges like frequent manual interventions, high costs, safety risks, and limited scalability, such as the reinstallation of clutch wires, barbed wire setups, and manual farmer surveillance. These issues highlight the need for an innovative, sustainable solution. AiProff addresses these challenges by leveraging advanced AI technologies to redefine agricultural security.

Objectives & Methodological Overview

At the heart of our endeavour lies a steadfast commitment to strengthen the financial security of Indian farmers through innovative technological interventions.

To realise this goal, we have laid out three pivotal objectives that encapsulate the essence of our solution:

As we navigate through the subsequent phases of development and implementation, these objectives will serve as the cornerstone of our efforts.

Solution Design: Core Concept, Components and Features

In crafting this robust solution, our approach prioritises a blend of technological prowess and eco-system responsibility.

Through a combination of proprietary algorithms, specialised hardware configurations, and user-centric interfaces, we aim to deliver an unparalleled agricultural security solution that is both effective and sustainable.

Phase 1: Initial Deployment and Monitoring

The first stage focuses on the seamless deployment and vigilant monitoring of our specialised equipment.

Phase 2: Continuous Improvement and Adaptation

The next stage deals with iterative refinement and adaptive enhancement, ensuring sustained effectiveness and relevance.

• Localised AI Enhancement: The AI algorithms are continuously refined using EDGE computing, enhancing their ability to discern genuine threats from false positives, thereby ensuring reliable performance in dynamic environments.

• Adaptive Alert Mechanisms: Based on ongoing assessments and feedback, the alert parameters are dynamically adjusted to optimise effectiveness while mitigating potential deterrent habituation in targeted wildlife populations.

Implementation Overview

Navigating the complexities of solution deployment, the implementation phase is characterised by strategic planning, meticulous execution, and continuous monitoring.

1. Customised Solution Development: The initial phase focuses on designing a bespoke solution tailored to the unique challenges and requirements of the agricultural landscape, ensuring a distinct competitive advantage.
2. Strategic Deployment: Following comprehensive testing and validation, the solution is strategically deployed across select farmland areas, leveraging proprietary installation methodologies to maximise efficacy and minimise detection vulnerabilities.
3. Performance Monitoring and Iterative Refinement: Post-deployment, the system undergoes rigorous performance evaluations, with insights gleaned used to inform iterative refinements and enhancements, safeguarding our technological edge in the market.

As we navigate this multifaceted landscape of innovation and implementation, our focus extends beyond mere deployment to the critical phase of evaluation. The effectiveness of our solution hinges on its tangible impact and operational efficiency, which we meticulously measure against predefined metrics.

Let's now delve into the comprehensive metrics and methodologies employed to gauge the success of our pilot initiative, offering insights into its real-world performance and the actionable intelligence derived for future enhancements.

Evaluation Metrics: Assessing the Success

The effectiveness of our solution will be rigorously evaluated against a set of predefined success criteria, designed to measure its performance and impact.

Additionally, we have identified key advantages inherent to our technical solution that further underscore its viability and value proposition.

The success will be evaluated based on the following metrics:

1. Precision in Threat Detection: A primary metric will be the reduction of false positives, ensuring accurate identification of wild animals and their trajectories.
2. Efficiency in Animal Deterrence: The solution's efficacy will also be measured by its ability to prompt wild animals to vacate the protected area upon alarm activation.
3. Economic Viability: A pivotal aspect of our evaluation will be the cost-effectiveness of maintaining the solution on a monthly basis, ensuring sustainable deployment without undue financial burden.

Advantages of Current Technical Solution

1. Operational Continuity: Our solution is designed to integrate seamlessly with existing farmland operations, minimising disruptions and ensuring a smooth implementation.
2. Scalability:Once deployed, the system architecture facilitates easy replication across diverse agricultural landscapes, offering scalability without compromising efficiency.
3. Sustainability:Leveraging solar power, our solution aligns with eco-friendly practices, offering a sustainable alternative to traditional energy sources.
4. Cost Efficiency at Scale:As the solution is scaled across larger areas or similar use cases, it demonstrates a compelling cost advantage, further enhancing its appeal.
5. Real-Time Monitoring and Control:The inclusion of a mobile application enables farmers to monitor the system in real-time, providing them with actionable insights and control over the security parameters.
6. Eco-Compatible Design:In alignment with environmental considerations, our solution is designed to operate in harmony with existing ecosystems, reflecting our commitment to responsible innovation.

Insights & Future Prospects

As we reflect on the journey thus far, several insights emerge that not only validate the efficacy of our current solution but also illuminate potential avenues for future innovation and expansion.

Key Insights:

• Data-Driven Decision-Making:The pilot phase has underscored the importance of data analytics in refining threat detection algorithms, highlighting the need for ongoing data collection and analysis to enhance system accuracy.
• Community Engagement:The positive reception from local farmers emphasises the importance of community involvement in shaping effective and sustainable solutions tailored to specific agricultural landscapes.
• Adaptive Technology:The dynamic nature of wildlife behaviour necessitates a flexible and adaptive technological framework, capable of evolving in response to emerging challenges and opportunities.

Future Prospects:

• Advanced AI Integration:Leveraging advanced AI capabilities, future iterations could incorporate predictive analytics to anticipate potential threats based on historical data and environmental factors.
• IoT Integration:Integration with Internet of Things (IoT) devices could further enhance real-time monitoring capabilities, providing a more comprehensive view of agricultural security metrics.
• Collaborative Partnerships:Establishing strategic partnerships with environmental agencies and research institutions could facilitate the development of eco-friendly solutions that balance agricultural needs with wildlife conservation objectives.

Conclusion

While our current solution marks a significant step forward in addressing agricultural security challenges, the path ahead is rich with opportunities to leverage emerging technologies and collaborative approaches.

AiProff.ai is confident that this initiative, with PashuTham at its core, will catalyse a transformative shift in Indian agriculture through the synergy of Artificial Intelligence and technological innovation.

By staying agile, innovative, and committed to our mission, we are well-positioned to shape a more resilient and sustainable future for agriculture, where technology and ecology coexist harmoniously.

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1. As per the Indian economic survey 2020 -21: https://www.financialexpress.com/budget/india-economic-survey-2018-for-farmers-agriculture-gdp-msp/1034266/
2. Ministry of Micro, Small and Medium Enterprises Annual Report 2022-23 : Pg 124 https://msme.gov.in/sites/default/files/MSMEANNUALREPORT2022-23ENGLISH.pdf
3. https://www.business-standard.com/india-news/compensation-for-crop-damage-to-farmers-goes-up-in-madhya-pradesh-123042701024_1.html
4. https://www.pnas.org/doi/10.1073/pnas