Crop disease detection

In the crop disease detection use case, language models are used to analyze data from sensors or images (e.g., from drones or smartphones) to detect diseases in crops. Both small language models (SLMs) and large language models (LLMs) can be utilized for this purpose, but their efficiency, speed, and practical implementation vary significantly based on resource constraints and requirements.

Use Case: Real-Time Crop Disease Detection Using Drone or Mobile Images

Scenario

Farmers use a drone or smartphone to capture images of crops, and the system uses a language model to identify possible diseases based on these images. The language model processes descriptions of the visual data, matches them to known disease symptoms, and provides recommendations. In this case, we compare the performance of an SLM and an LLM in analyzing and diagnosing crop diseases.

Key Metrics for Comparison

Latency: Time to analyze data and provide disease diagnosis.
Memory Usage: RAM required to run the model on local devices (e.g., edge processing on a drone or mobile device).
Power Consumption: Energy impact of running the model on mobile devices or drones.
Model Size: Number of parameters determining memory and computational needs.
Accuracy: Correctness of disease identification from images or descriptions.

Metric

Model Size
Latency (average)
Memory Usage (RAM)
Power Consumption
Disease Detection Accuracy
Hardware Requirements

Small Language Model (SLM)

50M parameters
50 ms/image
200 MB
Low (3% per operation)
85%
Low-power CPU (on-device)

Large Language Model (LLM)

1.2B parameters
2,000 ms/image
8 GB
High (20% per operation)
95%
High-end GPU or cloud-based

Technical Insights

Latency and Processing: In crop disease detection, real-time identification is crucial for immediate action, especially when using drones that scan large fields. The SLM processes images with a latency of only 50 ms per image, allowing for near-instantaneous disease identification, and enabling farmers to act quickly (e.g., applying pesticides to affected areas). In contrast, the LLM takes 2,000 ms (2 seconds) per image, which may be too slow for real-time drone-based detection across large agricultural areas, especially when thousands of images are processed.
Memory Usage and Hardware: The SLM requires just 200 MB of RAM, making it suitable for on-device processing on drones, mobile devices, or edge computing units deployed in the field. LLMs, with a memory requirement of around 8 GB, typically require more powerful hardware, such as cloud-based servers or specialized GPUs. This reliance on external hardware increases both the cost and the latency due to network transmission, which is impractical for real-time, offline environments where internet access is limited.
Power Efficiency: In drones or mobile devices, power consumption is a significant constraint. The SLM’s low energy usage (around 3% per operation) ensures the device can last longer in the field without needing frequent recharges. On the other hand, the LLM’s high power consumption (up to 20% per operation) would drastically reduce battery life, limiting the time a drone could spend detecting diseases in large fields.
Accuracy Trade-offs: While the LLM achieves a slightly better accuracy at 95%, the SLM still delivers an impressive 85% accuracy. In practice, this is often sufficient for identifying common crop diseases, especially when combined with data-driven improvements like retraining based on local data or employing complementary sensor information (e.g., humidity, temperature). The slight trade-off in accuracy is outweighed by the SLM’s ability to deliver faster, more efficient results in the field.
Deployment Flexibility: SLMs can be deployed entirely on-device, eliminating the need for internet connectivity, which is critical in rural or remote agricultural areas. LLMs, due to their large size, often require cloud-based processing or high-end hardware, which not only adds latency but also requires constant connectivity, increasing operational complexity for farmers.

Business Insights

Real-time Detection for Immediate Action: In agriculture, real-time detection of diseases can mean the difference between containing an issue and allowing it to spread, reducing crop yields. The faster analysis provided by SLMs (50 ms/image) allows farmers to act instantly, applying pesticides or quarantining infected areas. This quick turnaround is critical for minimizing crop losses and ensuring sustained yields. LLMs, while slightly more accurate, are too slow (2 seconds/image) to be practical for large-scale, real-time detection.
Cost-Effective Implementation: SLMs require minimal hardware resources, making them cost-effective to implement in affordable drones or smartphones. Farmers, especially in developing regions, can use these low-cost devices to monitor their crops efficiently. LLMs, on the other hand, demand more expensive hardware and cloud infrastructure, driving up costs and limiting accessibility for smaller-scale farmers or regions with limited connectivity.
Extended Device Life and Power Efficiency: One of the most critical aspects of using drones or mobile devices in agriculture is battery life. The low power consumption of SLMs means drones can fly for longer periods and cover larger areas without needing to recharge, which is essential for large farms. LLMs, with their higher power needs, would require frequent recharging or a more powerful drone, leading to higher operational costs and downtime during critical farming periods.
Scalability and Accessibility: For agricultural enterprises looking to scale disease detection across multiple farms or regions, SLMs offer a scalable solution due to their lower hardware costs and ease of deployment in offline environments. LLMs would require a substantial investment in cloud infrastructure, limiting their accessibility and scalability in rural areas.
Practical Accuracy for Field Conditions: While LLMs are more accurate in laboratory settings, the 85% accuracy of SLMs is usually sufficient for field conditions, where quick action is often more valuable than pinpoint precision. The slight reduction in accuracy is offset by the ability to respond quickly, making SLMs the better choice for day-to-day agricultural management.

Benchmarking Example

Consider a scenario where a drone captures 10,000 images over a large field for disease detection.

SLM Processing Time: 50 ms/image → 500 seconds (8.3 minutes) for all images.
LLM Processing Time: 2,000 ms/image → 20,000 seconds (5.5 hours) for all images.

In this real-world scenario, the SLM can process all images in just 8.3 minutes, enabling real-time responses, whereas the LLM takes over 5.5 hours to complete the same task, which could render the results obsolete in a fast-moving disease outbreak. This highlights the practicality of SLMs for real-time agricultural operations.

Conclusion

For the crop disease detection use case, small language models (SLMs) offer several distinct advantages over large language models (LLMs), particularly for real-time, on-device processing:

Real-time processing: SLMs offer far faster analysis (50 ms vs. 2,000 ms per image), making them ideal for real-time detection in the field.
Power and memory efficiency: SLMs are well-suited for devices with limited computational power and battery life, such as drones and smartphones, while LLMs require more energy and memory, reducing operational efficiency.
Cost-effective and scalable: SLMs can be deployed on affordable devices, making them accessible to a broad range of farmers, whereas LLMs require expensive hardware and cloud processing, driving up costs and limiting scalability.
Adequate accuracy: While LLMs are slightly more accurate (95% vs. 85%), the trade-off in speed and efficiency makes SLMs more practical for large-scale deployment where immediate action is crucial.

SLMs provide an efficient, cost-effective, and scalable solution for agriculture where real-time disease detection is essential to prevent crop loss and ensure sustainable farming operations.