Cropin Launches akṣara: A Micro Language Model (µ-LM) for Climate Smart Agriculture, Targeting Underserved Farmers in the Global South

April 18, 2024: Cropin Technology, a global Agtech company enabling intelligent agriculture, today announced the launch of ‘akṣara’, the sector’s first purpose-built open-source (Apache 2.0 License and with no restrictions) Micro Language Model (µ-LM) for climate smart agriculture, built on Mistral’s foundation model. ‘Akṣara’ is designed to address the problems faced by the underserved farming communities in the Global South by removing barriers to knowledge, and empowering anyone in the agriculture ecosystem to build frugal and scalable AI solutions for the sector. With the goal of democratizing access to digital technologies and modernizing agriculture for the 21st century, Cropin aims to empower agricultural stakeholders, developers, and researchers to tackle global challenges like food security, climate change, resource conservation – water and soil, regenerative agriculture practices amongst others by providing access to contextual, factual and actionable information.

The first version of akṣara will cover nine crops viz. paddy, wheat, maize, sorghum, barley, cotton, sugarcane, soybean, and millets for 5 countries in the Indian subcontinent. These food crops collectively account for a substantial portion of the world’s food requirements and are the staple food for the population in the global south. Developed by Cropin and hosted on Hugging Face, akṣara is a frugal and scalable µ-LM built and fine-tuned on top of the Mistral-7B-v0.2 model. Recognizing the environmental impact of running large language models (LLMs), Cropin has meticulously compressed ‘akṣara’ into 4-bit from 16-bit by using QLoRA or Quantization and Low-Rank Adapters (LoRA). The model seems to be more relevant than GPT-4 Turbo by almost 40% on randomly selected test dataset as measured by the ROUGE or the Recall-Oriented Understudy for Gisting Evaluation scoring algorithm.The model ensures that the responses are factually relevant and brief while minimizing the compute and storage resource requirement.

It was fine-tuned with more than 5,000 high-quality question-response pairs specific to agriculture and more than 160k tokens in the context. These numbers are expected to increase as we add more crops, geographic locations and use cases. To ensure that the model remains faithful to the question, it is grounded by using techniques like RAG or Retrieval Augmented Generation by cross referencing an authoritative subject matter experts’ knowledge base.

In today’s complex agricultural landscape, farmers and others involved in agriculture are struggling to meet the world’s increasing demand for sufficient, safe and nutritious food. This challenge is especially critical in the Global South. Farmers in this region are increasingly vulnerable to changing weather conditions. Climate change is disrupting conventional agricultural practices, making existing knowledge impractical in the era of global warming. Factors like irregular or extreme rainfall, unpredictable heatwaves, and increased pest and disease attacks affect farmers’ practices and reduce agricultural yield, productivity, and profitability. Cropin aims to bridge this gap with akṣara by harnessing the power of GenAI to provide insights into modern farming practices, accurate information, and farm advisories. For example, it can suggest which inputs to use for crops like rice or maize under specific agro-climatic conditions or thousands of climate smart agri advisories and other topics.

This initiative also demonstrates Cropin’s commitment to sharing knowledge, and ethical and responsible use of AI for agriculture. The Cropin Al team used Google’s People + AI Guidebook and discussions with Google’s Responsible AI team to help guide the model’s design process. The aim was to ensure the model’s alignment with key responsible AI principles, reduce biases, promote the use of AI for sustainable agricultural practices (practices involving biological controls, soil conservation, water conservation, companion planting, preserving beneficial plants and insects), and ensure the equitable distribution of benefits across farming communities in the Global South.

This open-source initiative aims to support agronomists, agri-scientists, field staff, and extension workers and gradually extend the services to farmers in multiple languages, considering the need for local language support. Cropin believes that transforming global food systems requires equipping industry think tanks and researchers with the best decision-making tools and information. This knowledge should then be disseminated at the grassroots level.

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