Synopsis:
This blog post delves into the innovative concept of Agri-Informed Neural Networks (AINN), a cutting-edge approach that integrates agricultural knowledge into model training. We explore how AINN can be leveraged to train machine learning models even when there is limited data and boost model performance and accuracy.
Artificial intelligence (AI) is transforming agriculture. However, limited data, regional variations, and other complexities of real-world farming pose significant challenges in training AI models. Agri-Informed Neural Networks (AINN) is a groundbreaking approach that integrates deep learning with expert agricultural knowledge. By incorporating crucial agri-information, AINN models overcome data limitations and deliver improved accuracy in yield prediction, disease detection, precision irrigation, etc. This blog post explores the principles behind AINN, its key benefits, and how it’s poised to transform the future of sustainable and efficient agriculture.
Overcoming the challenges of agricultural data
Innovation often springs from inspiration. Just as birds inspired humans to develop aircraft, the concept of neural networks was born from observing the human brain.
What are neural networks?
Neural networks (NNs) are a type of machine-learning model that mimics the human brain. They are composed of interconnected nodes, similar to the neurons in our brains, where complex processing operations occur. These networks excel at learning patterns from data, enabling them to perform tasks like image recognition, language understanding, and information classification.
However, neural networks can often overfit when limited data is available for training. This means they perform well on the training data but struggle to make accurate predictions on new, unseen data. This presents a significant challenge in agriculture and food technology, where data is often abundant but may not be consistently curated or labeled. Furthermore, regional variations can introduce biases into the data.
What is agri-informed neural networks or AINN?
“Agri-Informed Neural Networks” (AINN) has emerged to address these challenges. These are specialized neural networks specifically designed and trained to encapsulate agri-specific nuances into building Deep Neural Networks or AI models. This allows AINN models to make more accurate predictions and classifications relevant to farming practices.
A study published in ACM Transactions on Internet of Things, conducted by Agriculture and Agri-Food Canada, demonstrated that AINN models outperform single neural network models in predicting nitrous oxide emissions.
Let’s delve deeper into the day-to-day challenges faced when deploying agri-intelligence, particularly those related to limited data. We’ll also explore how AINN can significantly improve the accuracy of agri-intelligence.
Crop identification: a data - driven challenge
Deep learning models use a combination of spectral, temporal, and spatial data to identify crops. However, even with this wealth of information, challenges remain.
For example, the NDVI (Normalized Difference Vegetation Index) of Maize and cotton in Madhya Pradesh, India is very similar, making it difficult for models to distinguish between them.
Figure 1: Noise canceled Time Series NDVI of digitized farm in Madhya Pradesh, India.
Furthermore, the NDVI of crops like spring barley, spring peas, winter rapeseed, and winter wheat grapes varies significantly between Denmark and France due to differences in growing conditions, environmental factors and development.
Figure 2: NDVI time series for crops from two different Sentinel-2 tiles in Europe, indicating growth of four crops.
Deep learnings models are confused in identifying crops when
- Different Crops in the Same Region Have Same Spectral Signatures
- Same Crop in Different Regions Have Different Spectral Signatures
These variations arise from several factors:
- Soil and Reflectance: Each pixel in a satellite image at current resolution captures information about the crop of interest, along with the influence of surrounding factors, like the underlying soil, which can impact NDVI signatures.
- Regional Planting Practices: Minor changes in planting practices, such as spacing or orientation, can subtly alter the reflectance patterns. Building a generalizable model that can handle these regional planting practices can be very challenging.
The result? The model has lower performance. It is not generalized and does not work in diverse contexts.
Currently, researchers often attempt to address this difference in spectral signatures by automatically adjusting the data through a process called “time match.” However, this approach treats the signal as a generic signal, blindly trying to solve the matching problem. This can overcomplicate the issue.
The Cropin approach: agri-informed neural networks
At Cropin, we believe a more effective solution lies in understanding the root causes of these data variations. Simply increasing the amount of training data may not be feasible, as every region and every farm has unique practices, and collecting data on a global scale is impractical.
Instead of solely relying on increasing the volume of training data, we focus on incorporating agricultural knowledge into the model development process. By understanding how factors like spacing, mulching practices, and other regional variations influence crop signatures, we are developing robust and accurate AINN models. This knowledge-driven approach allows us to build more intelligent models that can effectively generalize and adapt to diverse agricultural environments.
For example, knowing that mulching is commonly used in a specific region during a particular month before a certain crop is planted can help the model identify that crop by recognizing the unique pattern of mulching in satellite imagery. This approach allows us to build effective models even with limited training data.
It is absolutely safe to say a mulching signature across the globe indicates farmland
This “agri-informed” approach makes crop detection more tractable by addressing the underlying agricultural factors that drive data variations. AINN models are trained using specific agricultural information for a crop and by comparing historical NDVI patterns in the region with current NDVI observations. This improves the efficiency of models, enhancing accuracy in crop detection and acreage estimation of a specific crop in a region. This knowledge can further be used to estimate yield, predict harvest dates, and more.
Here are some examples of agri-information that can be used to better interpret NDVI signature and identify a specific crop in a particular region:
- Periodic Polyhouse Use: If polyhouses are used for a specific period every year, this information can be valuable for model training.
- Perennial Crops: If a particular perennial crop has been observed in a location for the past five years, it is likely to be present in the sixth year.
By incorporating this type of agricultural knowledge, AINN models become hyper-contextualized, leading to significant improvements in accuracy and efficiency.
Predicting the quality of produce: importance of crop age
Knowing the lifespan of a perennial crop is crucial for predicting both the quantity and quality of yield.
Often, contractors supply produce to retail companies based on contractual agreements, but they may not disclose critical information like the area of procurement or the age of the plants. The age of a plant is a significant indicator of its productivity and the quality of its produce.
For example, consider a perennial crop “X” with a lifespan of 15 years. This crop may not start producing fruit until its fourth year. By focusing on plants that are at least four years old, yield estimation accuracy can be significantly improved.
Furthermore, the quality of produce from this crop may peak between years 7 and 13. By segmenting plants based on age, we can gain valuable insights into the expected quality of the produce from different regions. This information can be used strategically to source high-quality produce from specific locations.
Leveraging AINN for quality prediction
AINN models can be effectively trained to incorporate this knowledge of crop age and its impact on yield and quality.
Here’s some vital information that can be used to train such models:
- Nursery Age: How old is a plant when procured from the nursery and planted in the field?
- Gestation Period: What is the gestation time between planting and the onset of fruit production for a specific crop?
By incorporating this information into the AINN model, we can significantly improve the accuracy of yield estimates and quality assessments, enabling more informed and efficient procurement decisions.
Developing active learning models: leveraging limited data
When limited data is available for model training, agricultural knowledge can be effectively leveraged to improve model performance.
One approach is to employ “active learning” techniques.
- Identifying High-Confidence Plots: Towards the end of a growing season, just before harvest, we can identify plots where the crop maturity is high
- Ground Truth Verification: These high-confidence plots can be used for ground truth verification.
- Model Refinement: The model can be retrained using these verified data points, significantly improving its accuracy. This approach could potentially increase model accuracy from 60% to maybe even 80%.
Key agri-information for active learning:
- Crop Maturity: Information regarding crop maturity in a specific region is crucial for identifying suitable plots for ground truth verification.
- Sowing Timeframe: Knowledge of the sowing season or timeframe helps determine the expected maturity stage of crops within a given region.
By incorporating this agricultural knowledge into the active learning process, we can effectively utilize limited data and continuously improve the accuracy of our models.
Pushing the boundaries of innovation at Cropin
At Cropin, we are committed to maximizing per-acre value for farmers, customers, and stakeholders. We are actively leveraging Agri-Informed Neural Networks (AINN) to achieve this. We harness the domain expertise of agronomists, earth observation scientists, data scientists, and many others in building our Agri-Informed Neural Networks (AINNs).
This uncharted innovative approach allows us to:
- Overcome data limitations: AINN addresses the challenge of data scarcity by effectively utilizing available agricultural knowledge.
- uncertainties with confidence: By incorporating the guardrail of agricultural knowledge, AINN models can better handle the uncertainties inherent in agriculture.
- Deepen our understanding of regional farming nuances: AINN helps us better understand the unique characteristics of different farming regions.
Key benefits of AINN
- Reduced Data Requirements: AINN can be used to train highly accurate models with significantly less data.
- Improved Model Efficiency: AINN enhances the efficiency of various AI models, including crop disease identification, yield prediction, irrigation optimization, and soil analysis.
Essentially, AINN represents a paradigm shift in how we approach agricultural intelligence. By leveraging the power of neural networks and integrating them with deep agricultural knowledge, we can unlock new levels of efficiency and sustainability in agriculture. AINN can improve model accuracy by incorporating agricultural knowledge into its learning process; essentially, it’s a neural network that leverages agricultural information to make informed decisions in the farming sector.
