AI for Sustainable Precision: Innovation Specialist at Corteva Agriscience on Advancing Agriculture

In an exclusive interview, Divya Sarda, Innovation Specialist, discusses how AI is transforming precision agriculture through predictive modeling, optimized pesticide use, and tailored seed recommendations. She addresses key hurdles, such as harmonizing global data, ensuring data quality and farmer ownership, improving AI accessibility, and preparing for future advancements. Sarda highlights benefits like faster innovation cycles and enhanced farmer productivity, envisioning real-time advisory systems as the next breakthrough.

Overcoming Barriers to AI in Agriculture

Farmers globally face pressure to increase yields while minimizing environmental impact, but adopting AI in precision agriculture involves significant challenges. From integrating diverse data to ensuring farmer trust and usability, success requires practical solutions. We spoke with Divya Sarda, Innovation Specialist, whose expertise in AI-driven agricultural solutions drives advancements in seed and crop protection. In this interview, she outlines critical barriers to AI adoption in agriculture and shares actionable strategies, offering insights for farmers and agribusinesses aiming to scale sustainable precision farming.

Harmonizing Data Across Global Agronomic Conditions

Accurate AI models for seed placement or pesticide optimization depend on consistent data, but agriculture spans diverse regions, crops, and regulations. Variations in soil, weather, and farming practices create fragmented datasets, making it difficult to develop models that deliver localized, actionable insights. For example, a model trained on one region’s data may not support farmers elsewhere, limiting AI’s impact.

Sarda’s Solution: Divya Sarda emphasizes robust data harmonization. “As a global company, harmonizing data across regions, crops, and regulatory environments is a major challenge,” she explains. Collaborating with growers, research institutions, and R&D teams, data is collected via field trials, remote sensing, and IoT devices, with standardized analytics pipelines aligning diverse inputs.

• Global Partnerships: Collaborations provide comprehensive data from varied conditions.

• Standardized Systems: Analytics align datasets for model consistency.

• Localized Recommendations: Models deliver region-specific advice, like seed varieties for local soils.

Harmonizing data ensures AI provides precise, region-tailored insights, enhancing farmer outcomes.

Sarda’s Perspective: Sarda underscores data’s role in global AI. “Our models must be trained on diverse agronomic conditions and yet produce actionable, localized outputs,” she says. Her approach ensures farmers receive reliable recommendations, such as seed choices suited to their region, reducing environmental impact and boosting productivity. Sarda’s expertise in data integration supports AI’s ability to address diverse farming needs, advancing sustainable agriculture.

This solution enables AI to support varied operations, improving yield stability across regions. Sarda’s focus on harmonization strengthens AI’s role in global precision farming.

Ensuring High-Quality Data and Farmer Ownership

AI models rely on high-quality data for reliable predictions, but agricultural data from sensors or trials can be inconsistent. Farmers also prioritize data privacy, needing assurance that their data is secure and under their control. Poor data quality or lack of trust can deter AI adoption, reducing its potential to optimize farming practices.

Sarda’s Solution: Divya Sarda advocates for rigorous data stewardship. “We work with growers, research institutions, and internal R&D teams to gather high-quality data,” she notes. Analytics pipelines undergo strict validation to ensure reliability, and farmers retain full ownership with transparent data usage policies.

• Validated Systems: Automated checks verify data accuracy from IoT and trials.

• Farmer Control: Policies ensure growers own and manage their data.

• Transparent Usage: Farmers understand how data informs AI recommendations.

These measures ensure AI delivers actionable insights, like optimized pesticide use, while building trust.

Sarda’s Perspective: Sarda highlights trust as key. “We prioritize giving farmers ownership and visibility into how their data is used,” she says. Her leadership ensures data quality supports reliable AI outputs, enabling farmers to see benefits like reduced environmental impact from precise inputs. Sarda’s focus on stewardship empowers growers to adopt AI confidently, enhancing its impact across agriculture.

By ensuring data quality and ownership, AI delivers faster innovation and tailored recommendations. Sarda’s approach fosters farmer trust, driving wider AI adoption.

Making AI Accessible to Farmers

Complex AI models, such as those for predictive seed placement, can be challenging for farmers to use without intuitive tools. Farmers need user-friendly interfaces to translate AI outputs into practical decisions, like planting schedules or pesticide applications. Without accessibility, AI’s benefits are limited, especially for smallholder farmers.

Sarda’s Solution: Divya Sarda prioritizes farmer-centric platforms. “We face the challenge of making complex AI models accessible to farmers via intuitive interfaces,” she explains. Digital tools simplify AI outputs, presenting recommendations like seed varieties in clear, actionable formats.

• User-Friendly Platforms: Mobile apps deliver insights in accessible formats.

• Clear Recommendations: Complex models are translated into practical advice.

• Global Usability: Tools support diverse languages and offline access for remote farms.

These solutions make AI usable, enabling farmers to enhance productivity without technical barriers.

Sarda’s Perspective: Sarda emphasizes usability. “Farmers need tools that feel intuitive, not overwhelming,” she says. Her work ensures platforms act as trusted advisors, guiding decisions like planting or pesticide use. Sarda’s expertise ensures AI benefits farmers of all scales, from small plots to large operations, advancing sustainable practices across agriculture.

This approach delivers benefits like improved productivity from tailored recommendations. Sarda’s focus on accessibility ensures AI supports diverse farming needs.

Scaling AI Adoption in Agricultural Operations

Implementing AI across global farming operations requires overcoming logistical challenges, such as integrating diverse data and ensuring model reliability. Without effective scaling, AI’s ability to accelerate innovation and reduce environmental impact is constrained, slowing adoption among farmers and agribusinesses.

Sarda’s Solution: Divya Sarda focuses on robust analytics and collaboration. “AI plays a role in nearly every part of our innovation pipeline,” she notes. AI-driven platforms model disease resistance, optimize pesticide dosages, and recommend seed varieties, supported by global data integration and validated systems.

• Advanced Analytics: AI streamlines innovation and agronomic advice.

• Data Integration: Diverse inputs ensure scalable, reliable models.

• Farmer Support: Platforms deliver tailored recommendations, enhancing adoption.

This approach ensures AI scales effectively, delivering faster innovation and practical benefits.

Sarda’s Perspective: Sarda stresses scalability’s role. “Our AI must work across global operations, from R&D to fields,” she says. Her leadership drives outcomes like shorter innovation cycles and reduced environmental impact. Sarda’s expertise ensures AI solutions are scalable, supporting farmers worldwide in adopting precision agriculture.

By scaling AI, agriculture sees benefits like enhanced yield stability and sustainability. Sarda’s strategic approach strengthens AI’s industry-wide impact.

Envisioning AI’s Future in Agriculture

As agriculture faces climate variability and food security challenges, the next AI breakthrough must address complex decision-making while remaining cost-effective. Without a clear vision, AI investments risk falling short of delivering long-term value for farmers and global supply chains.

Sarda’s Solution: Divya Sarda envisions real-time advisory systems as transformative. “AI-powered real-time advisory systems that integrate genetics, weather, pest forecasts, and sustainability goals will transform how decisions are made,” she shares. Scalable platforms are being developed to support these advancements.

• Real-Time Insights: AI simulates future outcomes for proactive decisions.

• Integrated Data: Genetics, weather, and sustainability drive holistic advice.

• Risk Management: Predictive tools enhance food security and resilience.

This vision ensures AI evolves to meet future agricultural needs, supporting sustainable growth.

Sarda’s Perspective: Sarda’s forward-thinking shapes AI’s future. “We’re moving from tools that analyze past performance to systems that can simulate and optimize the future,” she says. Her expertise positions AI to address global challenges, delivering solutions for risk management and food security. Sarda’s vision ensures AI remains relevant, supporting farmers over the next five years.

By prioritizing real-time systems, AI redefines precision farming, enhancing resilience and sustainability. Sarda’s leadership aligns innovations with farmers’ needs.

Divya Sarda, Innovation Specialist at Corteva Agriscience, provides a practical framework for AI-driven precision agriculture in this exclusive interview. By addressing hurdles like data harmonization, quality, accessibility, and future-proofing with solutions such as validated pipelines and intuitive platforms, she outlines a path to sustainable farming. Her vision for real-time advisory systems and benefits like faster innovation highlight her expertise.

FAQ: Exploring AI in Precision Agriculture

Q: How does AI transform precision agriculture?
A: AI enables predictive modeling for seed placement and pesticide optimization, per Divya Sarda, improving yield stability and sustainability.

Q: What benefits has AI delivered in agricultural operations?
A: AI speeds up innovation and tailors seed recommendations, boosting productivity and reducing environmental impact, as Sarda notes.

Q: How is data quality ensured for AI models?
A: Validated analytics pipelines verify data from trials and IoT devices, ensuring reliable AI outputs, per Sarda.

Q: Who owns and controls farm data in AI systems?
A: Farmers own and control their data, with transparent usage policies, as Sarda explains.

Q: What challenges hinder AI implementation in agriculture?
A: Harmonizing global data and ensuring accessibility are key hurdles, addressed by standardized pipelines and intuitive platforms, per Sarda.

Q: What’s the next major AI breakthrough in agriculture?
A: Real-time advisory systems integrating genetics and weather will enhance risk management, as Sarda envisions.

Q: How does AI become accessible to farmers?
A: Intuitive digital platforms simplify AI outputs, delivering actionable advice via mobile apps, per Sarda.