In the past few years, we have witnessed remarkable growth in the agriculture sector, thanks to Artificial Intelligence (AI) and machine learning technologies. AI/ML models can significantly improve yield prediction, optimize resource management, slow down disease outbreaks in crops, and enhance the productivity of agricultural operations.However, the foundation of an AI-based AgriTech product relies heavily on data. It is crucial for product development, meeting regulatory standards, exploring novel agricultural solutions, and educating the market on the optimal use of agricultural inputs and practices.
However, acquiring high-quality, real-world data is a resource-intensive and time-consuming exercise. Hence, the landscape is witnessing a transformation with the introduction of synthetic data. Synthetic data is derived from real-world data generated by a model that maintains identical statistical properties and connections between different parameters in actual datasets. These datasets can be entirely synthetic or partially synthetic, with the latter filling in gaps in real-world data.
Synthetic data is not necessarily a substitute for original data. Instead, it acts as a supplementary source. This secondary source has the potential to significantly reduce the time, cost, and effort involved in obtaining original data for streamlining the process and investment required to bring new agricultural products to market.
How Synthetic Data Enhance AI Precision
As discussed before, synthetic data is artificial information generated using algorithms. It is generated when real-world data is unavailable or must be kept private due to compliance risks. Synthetic data generation has emerged as a powerful solution that enables the creation of vast, customized datasets, overcoming limitations that come with real-world agriculture data. It can overcome challenges and boost precision in several ways:
Address Data Scarcity & Imbalance
The real-world datasets for agriculture space may lack diverse scenarios, rare events, specific environments, and more, leading to uneven data distribution. Synthetic data fills this gap by generating realistic data for scenarios like rare diseases, unique weather patterns, etc, enabling AI models to generalize better.
Enhance Traning Efficiency
Collecting real-world data requires manual labeling and annotation, which is time-consuming and expensive. Synthetic data reduces dependency on real-world data and rapidly generates pre-labeled data, saving resources and time.
Improves Model Performance
Due to data scarcity, AI models are trained on limited data and often need help to handle complicated and unforeseen situations, impacting their effectiveness. Synthetic data can create diverse scenarios for training, enabling AI models to adapt to various scenarios and enhance accuracy in real-world applications. It brings precision in areas like:
- Detecting Diseases: Synthetic data help AI models identify rare diseases in crops and improve the accuracy of early detection of crop diseases.
- Classifying Weed: Synthetic data train AI models distinguish different weeds from crops with higher accuracy.
- Predict Yield: Accurately estimate the crop yields under various weather conditions.
- Optimize Irrigation and Machinery: Suggest ideal settings based on different field conditions and the performance of machinery.
Training AI models often involves using fully synthetic data for validation purposes. Instead of conducting real-world experiments to train AI, synthetic data helps identify early correlations and assess model validity before investing in extensive, real-world data collection. Once the AI demonstrates the expected performance, further validation is conducted through real-world trials.
Synthetic Data and Digital Twins
Synthetic data extends its utility to R&D, particularly in creating “digital twins.” In this scenario, a computer uses real-world data to maintain statistical correlations and generates synthetic data, emulating real-life conditions. In agriculture, a digital twin of a field trial can test variables like soil types and weather conditions, impacting regulatory approval for crop protection companies and aiding seed companies in improving genetics.
Digital twins also address data gaps. In cases of missing data due to equipment errors or sensor failures, synthetic data based on statistical models can fill these gaps, providing a comprehensive study picture. Additionally, for regions lacking data due to limited research facilities, synthetic data can compensate for the absence.
Crop and Weed Detection AI Trained on Synthetic Data
iMerit has developed a specialized Crop and Weed Detection AI Solution to identify and categorize crops, weeds, and grass. Leveraging human-in-the-loop (HiTL) teams, this AI solution streamlines pre-labeling processes, contributing to improved model accuracy. The solution incorporates synthetic data generation and augments crop images- a task efficiently carried out by our annotation teams. These datasets play a crucial role in creating ground truth data, ultimately enhancing the precision and reliability of the AI model.
Read about iMerit’s crop and weed detection AI and data labeling solution for precision agriculture here.
The Future of Synthetic Data in Agriculture
While the potential of synthetic data is promising, concerns about its effectiveness and reliability are natural. There are more questions about trusting synthetic data and ensuring its accuracy as a representation of real-world data.
However, privacy concerns, a significant obstacle in obtaining real-world data, are mitigated through synthetic data. It enables companies to remove personal and confidential information from a dataset while retaining the original data’s correlations and relations. It fosters greater collaboration and confidence in data sharing, especially as agribusinesses increasingly collaborate.
Gartner’s prediction that synthetic data will surpass real-world data in AI models by 2030 indicates a transformative shift. The potential with data is boundless, presenting numerous opportunities. Agriculture companies investing in AI must now focus on building their data, establishing connections, standardizing information, creating and validating models for interpretation, and complementing their datasets with synthetic data.
