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In this insightful interview, Henrique Del Papa, COO & Co-Founder of Cromai, unveils the transformative power of AI in modern agriculture. Del Papa delves into Cromai's groundbreaking weed detection system, which promises to reduce herbicide use by up to 65%. He explores the unique challenges faced in the sugarcane industry and how Cromai's AI solutions are addressing them. Del Papa also shares valuable insights on farmers' adaptation to AI-powered decision-making tools, the company's methods for ensuring diagnostic accuracy, and the fruitful partnership with Stoller. As Brazil continues to lead in agricultural innovation, Cromai's AI-driven approach offers a glimpse into a more sustainable and efficient future for farming.
Henrique Del Papa
COO & Co-Founder at Cromai
Cromai's AI-powered weed detection system claims to save up to 65% on pesticides. Can you elaborate on how this technology works and what makes it so effective?
We use artificial intelligence to locate and identify weed species, enabling more efficient management. This is achieved through orthomosaic processing, using aerial images captured by drones to create a map of the area.
Once the area is mapped and processed by the AI, a diagnostic map is generated with layers identified and grouped into narrow and broad-leaved categories to assist in decision-making. Along with this map, a spraying map with a safety buffer is created to target weeds in the field. Georeferenced infestation and spraying files are sent to the client for localized applications, resulting in pesticide savings.
Cromai's solution focuses on identifying weeds in sugarcane and soybean crops, enabling localized herbicide management and application. This approach reduces the herbicide application area (which would otherwise cover the entire field without Cromai) and consequently its quantity. Thus, in addition to improving identification and control efficiency, there is a reduction in herbicide use, minimizing environmental impact and achieving up to 65% reduction in herbicides applied in the field to control weeds.
Your company has expanded rapidly in the sugarcane industry. What unique challenges does this crop present for AI-based solutions, and how does Cromai address them?
Unique Challenges:
Difficulty in locating and classifying weeds in the field
Uncertainty in defining effective weed management
Environmental impacts from excessive herbicide use
Estimating harvest quality in real-time
How Cromai Addresses These Challenges:
Cromai sets a new standard by enabling mills and producers to digitize their weed management practices, reducing unnecessary herbicide applications and, consequently, environmental impacts. Our solution allows for localized herbicide application, resulting in savings and reduced environmental impact.
With Cromai, decisions are based on accurate data from within the client's field, facilitating management and providing reliable, secure information. Additionally, we offer a technology for sugarcane harvests that allows mills to assess the quality of raw material delivered by trucks, classifying the percentage of impurities in the harvest. This technology has significant potential for further development, providing more control and aiding decision-making for clients.
The data provided by Cromai supports the sustainability of clients' processes by reducing herbicide use and identifying impurities in the harvest. As more clients adopt Cromai technology, less herbicide is applied, contributing to a more sustainable future and preserving the environment.
Can you share some insights on how farmers are adapting to and benefiting from Cromai's AI-powered decision-making tools?
Farmers are positively adapting to Cromai's AI-powered decision-making tools and are reaping various benefits. We can highlight some insights into this adaptation:
Gradual Adoption and Value Perception: Initially, many farmers may have some hesitation in adopting new technologies in their operations. However, Cromai has invested in support and training, which facilitates adaptation and builds confidence in using these tools.
More Accurate Decisions: Cromai's AI tools provide detailed analyses, allowing farmers to make more precise decisions. This results in greater efficiency in the use of inputs such as water and herbicides, reducing costs and increasing productivity.
Increased Productivity: With the support of Cromai's solutions, farmers have observed an increase in the productivity of their crops. The ability to identify weeds early and accurately allows for quicker and more effective interventions.
Sustainability and Conservation: The adoption of these tools has also helped farmers implement more sustainable agricultural practices. By optimizing resource use and reducing environmental impact, they are able to align production with the conservation of local ecosystems.
How does Cromai ensure the accuracy and reliability of its AI-based diagnostics, especially when dealing with new crop types or environmental conditions?
Cromai ensures the accuracy and reliability of its AI-based diagnostics through a combination of robust technical approaches and continuous development practices. Here are some key points:
Training AI with Diversified Data: Cromai utilizes vast amounts of images. For each crop served, there is a database of images across various phenological stages to train its AI models, ensuring that the algorithms can recognize patterns.
Continuous Model Updates: To manage the introduction of new crop types or various weed species, Cromai's AI models are continuously updated. This updating process is driven by new images collected directly from the field, allowing the algorithms to remain accurate and relevant.
Rigorous Validation: Before implementation, the AI-based diagnostics undergo a rigorous validation process. This includes testing in different cultivation environments and comparing results with real field data.
User Feedback: Continuous feedback from farmers using Cromai’s tools is crucial. The company analyzes user responses to constantly adjust and improve the models, ensuring that the solutions offered are effective and highly trustworthy.
Can you discuss the partnership with Stoller and how it has influenced the development of Cromai's AI technologies for different crops?
The partnership between Cromai and Stoller has been crucial for the development of Cromai's artificial intelligence technologies, particularly in monitoring different agricultural crops. The project born from this collaboration demonstrates how combining knowledge and resources can lead to significant technological advancements.
The main contribution of the partnership has been the provision of an extensive database of images of corn and soybeans. These images are taken at various growth stages and under different environmental conditions. This dataset is essential for training the project's artificial intelligence, which is responsible for calculating and analyzing metrics such as the green index, leaf temperature, plant stand, and vegetation cover. With these images, the AI can learn to identify patterns and provide more accurate information.
But the benefits don't stop there. The enriched database of these images also positively impacts other Cromai technologies. For example, the AIs that detect and identify weeds in soybeans, and eventually in corn, benefit from this vast collection of images. With a more diverse and comprehensive dataset, these AIs can better identify target crops and distinguish them from weeds, resulting in more accurate weed classification, which helps manage activities and increase productivity.
Therefore, the collaboration with Stoller is not only advancing the project but also strengthening Cromai's other AI technologies. With a more robust and detailed database, Cromai can offer more advanced and precise solutions, contributing to faster and more efficient growth. In summary, this partnership is enhancing how our technologies are developed and applied, benefiting both Cromai and Stoller.
Looking ahead, what do you believe will be the next major AI-driven innovation in agriculture?
Looking ahead, the next major AI-driven innovation in agriculture is likely to be the integration of autonomous farming systems. These systems will combine AI with robotics to create fully automated farms where tasks such as planting, monitoring, irrigating, and harvesting.
This story was initially published in the 2024 Latin America Focus. Download the magazine to read more stories.
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