Mechanistic Interpretability for Agricultural AI

As AI systems become increasingly prevalent in agriculture from automated harvesting to crop monitoring understanding how these systems make decisions becomes critical. Mechanistic interpretability offers a path forward: instead of treating neural networks as black boxes, we can reverse-engineer their internal computations to understand what they've actually learned.

Why Interpretability Matters for Agricultural AI

Agricultural automation carries real stakes. When a robotic system decides which plants to trim, which areas to irrigate, or which produce to harvest, errors can mean lost crops, wasted resources, or damaged equipment. Operators need to trust these systems, and trust requires understanding.

Consider a vision model trained to detect plant pots in a nursery. The model achieves 95% accuracy but what happens with the other 5%? Without interpretability, we can't answer critical questions:

• What visual features trigger false positives?
• Does the model rely on spurious correlations (like pot color) that might not generalize?
• Which conditions cause the model to fail?
• Can we predict when the model will be uncertain?

What is Mechanistic Interpretability?

Mechanistic interpretability is an approach to understanding neural networks by identifying the specific computations performed by individual neurons, layers, and circuits. Rather than just observing inputs and outputs, we look inside the model to understand its internal representations.

Key techniques include:

• Feature visualization: Generating images that maximally activate specific neurons
• Activation patching: Measuring how model outputs change when we modify internal activations
• Circuit analysis: Identifying minimal subnetworks responsible for specific behaviors
• Probing: Training simple classifiers on intermediate representations to understand what information they encode

Applying Interpretability to Vision Models

In our research, we apply these techniques to vision models like SAM and Faster R-CNN that have been adapted for agricultural tasks. Our goal is to answer questions like:

What do individual channels represent?

Vision models like SAM have thousands of channels in their feature extractors. By analyzing activation patterns, we can identify channels that respond to specific agricultural concepts: pot edges, soil texture, plant foliage, shadows, and more. Some channels are "monosemantic" responding to a single concept while others are "polysemantic" activating for multiple, seemingly unrelated features.

How do representations change during fine-tuning?

When we fine-tune a foundation model on agricultural data, which representations change? Do we see new features emerge, or do existing features get repurposed? Understanding these dynamics helps us design more efficient fine-tuning strategies.

What circuits implement object detection?

Using activation patching, we can identify the minimal set of channels and connections responsible for detecting specific objects. This "circuit extraction" reveals the computational structure the model has learned, separate from the vast majority of parameters that may be unused for any given task.

Practical Benefits

Interpretability isn't just academic it has practical benefits for deployed systems:

• Debugging: When models fail, interpretability helps identify why and suggests fixes
• Data efficiency: Understanding what models need to learn guides data collection
• Trust: Operators are more likely to trust systems they can understand
• Safety: Identifying failure modes before deployment prevents costly mistakes

The Road Ahead

Mechanistic interpretability for vision models is still in its early days, especially for domain-specific applications like agriculture. Much of the foundational work has focused on language models, and adapting these techniques to vision requires new methods and tools.

Our research aims to bridge this gap by developing interpretability tools specifically for agricultural AI systems. By understanding what these models learn, we can build more trustworthy, reliable, and effective automation systems for the future of farming.

This work draws on the "200 Concrete Problems in Interpretability" framework by Neel Nanda, adapted for the unique challenges of vision models in agricultural domains.