Location-Specific Geo-Adapted Crop-type Classification Models (LGCCM)

We always adapt then why not now?

India, with its vast and diverse agricultural landscape, poses unique challenges and opportunities for modern technological interventions. From the terraced fields of the Western Ghats and Northeast India to the arid plains of Rajasthan , the diversity in climate, soil types, and crop varieties demands a new aways of thinking which is location specific and geographically adapative. Bringing location specificy and geographical adaptivity is key for activity like Crop-type classification.

Crop-type classification has seen significant advancements with machine learning and deep learning techniques. However, the "one-size-fits-all" approach often fails to capture the regional diversity and geographic dependency. This article introduces the concept of Location-Specific Geo-Adapted Croptype Classification Models (LGCCM), an innovative framework tailored to India's diverse agricultural regions. This is the first time LGCCM has been coined specifically to meet needs of Indian diversity.

Note: I am introducing LGCCM framework and there is no literature on the same. I would be expanding it as a project in coming months with implementation of deep learning models to classify crop-types using satellite data

The Need for Geography-Specific Models

Deep learning has revolutionized crop type classification through its ability to process large volumes of satellite imagery. It can extract, analyse and predict complex patterns. Despite its successes, models created based on data from US or Europe can not be used for predicting crop types in India. The effectiveness of a single model across varied geographies is limited.

In India, from north to south and east to west, there are significant variations in the types of crops grown, methods of tillage, and practices like intercropping and crop rotation. Factors such as water availability, soil composition, environmental conditions, and elevation differ across regions, shaping agricultural practices and outputs. While deep learning models can capture these variations and predict the type of crop grown using satellite data, a model designed to generalize across all these differences might not achieve the high level of accuracy needed for specific applications. Balancing generalization and accuracy remains a key challenge.

The Concept of LGCCM

The LGCCM framework aims to address these challenges by combining the precision of location-specific models with the adaptability of geo-tuned algorithms. LGCCM’s design philosophy revolves around three pillars:

Location-Specific Customization:

LGCCM develops distinct models tailored to specific geographic regions, ensuring they are fine-tuned to the unique attributes of each administrative area. This approach provides governments with choice and control over their models, effectively promoting technological self-reliance and democratization of technology at the administrative level. Each region-specific model can be geo-adapted to local needs.

For example, in Karnataka, one or more deep learning models could be developed independently or collaboratively with neighboring states. Within Karnataka, croptype classification could be handled by different models: a coastal area model, a semi-arid area model, a Mysore region model, and a Western Ghats model. These models should be designed to adapt to the specific geographic and agricultural attributes of each region, enabling precise and localized agricultural monitoring.

Geo-Adaptability:

While location-specific models define areas based on crop patterns, geo-adaptability enhances this by incorporating parameters such as elevation, climate zones, biophysical characteristics, long-term evapotranspiration, humidity distribution, and other geographic factors. These additional inputs enable the model to adapt to specific geographies and improve its accuracy in identifying crops. For instance, a model designed to detect tea plantations in Assam could, with adaptation, effectively identify coffee plantations in the Nilgiris. This adaptability ensures that models can be reused across regions with distinct agricultural and environmental characteristics while maintaining precision.

Data Integration:

Data integration is a cornerstone of LGCCM, requiring the use of multi-source satellite datasets. These include data from the Sentinel series, IRS series, and private datasets such as those from Planet. In addition to satellite data, the integration of crop labels collected by administrative authorities is essential. Administrative data serves as a foundation for the development of LGCCM, enabling accurate mapping and classification.

As crop data sharing between states may depend on building mutual confidence, this framework provides states the autonomy to develop their own models. States can then share data with a central authority to adapt the model for broader applications, ensuring both localized control and nationwide collaboration.

Advantages of LGCCM

• Improved Accuracy: By focusing on regional characteristics, LGCCM framework will allow models to achieve higher classification accuracy compared to generalized models.
• Resource Efficiency: Transfer learning minimizes the need for extensive data collection and computation in new regions.
• Data Utilisation: Regional variations in data availability pose challenges for global models. Regional government could be encouraged to develop their own model using their own data. This would eventually lead to struture that will encourage local data collection.
• Scalability: The modular approach of LGCCM ensures seamless scalability across India’s diverse landscapes.
• Computational Demands: Training location-specific models for a vast country like India is resource-intensive. However as one model developed over one region (similar regions) could be adapted using transfer learning, computational demand could be optimised or even reduced.
• Real-World Impact: Accurate crop classification supports government policies, optimizes supply chain logistics, and enhances farmers’ decision-making.
• It would also push for more regionalisation of technology moving away from classical technology centralisation.

How it could go from here?

The LGCCM framework offers a hierarchical and federal approach to the implementation of deep learning techniques for crop-type classification. This framework is similar to agent-based systems, which are popularly known as multi-agent systems. The difference here is that all the agents (learning algorithms) will focus on crop-type classification but will specialize in specific regions of India. This represents a horizontal expansion of LGCCM. If the models become experts in one crop group, such a system could be considered a vertical expansion of LGCCM.

Currently, I am working on a transformer-based crop-type classification model for a specific region of Karnataka. My first outcome will be a small step towards LGCCM. I envision an Indian central portal with the LGCCM framework, where region-specific models are shared as dropdowns, allowing researchers or agriculturists to download and execute them using satellite data. This would enable them to identify the crops grown in their regions, even at the field level. It will also be the first step toward the democratization of models.

Location-Specific Geo-Adapted Croptype Classification Models (LGCCM) is way forward.