How Efficient Data Preprocessing Drives AI?
Modern agriculture often revolves around gathering vast amounts of data through cutting-edge technologies like satellites, drones, and sensors. These tools capture everything from soil moisture levels to crop health indicators and climate patterns.
However, the true value of this data is only realised when it is processed and analysed through advanced artificial intelligence (AI) and machine learning (ML) algorithms. This process requires that the data is not just collected but also structured and refined to be AI-ready through rigorous data preprocessing.
Unfortunately, this is where the gap begins. Despite the abundance of data, much of it remains underutilised due to challenges in preparing it for AI analysis, leaving farmers without the valuable insights that could help optimise their operations. Effective data preprocessing is crucial to bridging this gap.
In this blog, we will explore the root causes behind the scarcity of agricultural text data and how this gap hinders the full utilisation of AI and ML technologies, leaving many valuable insights untapped.
Tackling the Underdevelopment of Text Data in Agriculture
Some of the challenges regarding AI analysis are related to data quality and consistency. These issues stem from biases in data collection, varying formats, and incomplete datasets, all of which complicate the integration of AI systems. Data quality problems can distort AI predictions and insights, leading to inefficiencies rather than improvements.
We have already discussed these topics in more detail in earlier articles: Data Quality: What Are the Key Strategies for Making Your Data Useful? and Data Bias Explained: Insights into 9 Types Affecting Agriculture. In these articles, data preprocessing is highlighted as a crucial step for ensuring data quality.
While visual data—collected through satellite imaging and remote sensing—has become increasingly abundant, another crucial data type remains largely underdeveloped: text data. This is particularly true when it comes to addressing complex issues like food contamination, allergen identification, and the associated health risks. The scarcity of agricultural text data has posed challenges to the implementation of AI-driven solutions in agriculture.
Unlike visual data, which provides a broad overview of fields and crops, text data offers deeper insights into food safety incidents, regulatory reports, and research findings. However, the scarcity of agricultural text data has posed challenges to the implementation of AI-driven solutions in agriculture.
To truly understand the nature of food hazards, we need a rich foundation of text-based information that can help detect patterns, classify hazards, and identify potential risks in food products.
Building Robust AI Models through Data Preprocessing
Food safety data often exists in separate silos across different sectors like agriculture, health, and food production, with much of it still undigitised. Consolidating this data into a digital format and integrating it from various sources could create richer datasets that AI models could leverage for better predictions.
For instance, mycotoxins, which are toxic substances produced by moulds, are influenced by environmental conditions like temperature and humidity. Including climate data in AI models can help us understand how these conditions impact mycotoxin levels in food. Proper data preprocessing can enhance the accuracy of such models.
By incorporating diverse data sources, it becomes possible to uncover connections that otherwise might have been missed with just a narrow set of information, enabling AI to build more accurate models and reveal hidden patterns that help address food safety challenges. However, caution is essential—ensuring the data is high-quality, relevant, and free from biases is key to producing reliable and effective models.
Addressing Agricultural Challenges with Innovative Solutions
This is where the STELAR project steps in. The STELAR project is dedicated to addressing various challenges in agriculture by designing, developing, and evaluating a Knowledge Lake Management System (KLMS). This system is designed to facilitate smart agriculture and enhance food safety applications.
Through the efforts of partners like Agroknow, one facet of the broader STELAR project is the development and enrichment of agricultural text data. Agroknow’s work ensures that this data is made accessible and valuable for various applications, including AI-driven hazard classification and bias identification in food safety reports.
Conclusion
The scarcity of agricultural text data poses significant challenges, hindering advancements in AI applications and food safety. Effective data preprocessing is essential to overcoming these challenges and unlocking the full potential of AI.
To stay updated on how the STELAR project is tackling these issues, follow our Blog and connect with us on LinkedIn.
Reference
Gbashi, S., & Berka Njobeh, P. (2024). Enhancing food integrity through artificial intelligence and machine learning: A comprehensive review. Applied Sciences, 14(8), 3421. https://doi.org/10.3390/app14083421
