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Cropin is releasing its first Micro Language Model (µ-LM), akṣara, under the Apache 2.0 license without any restrictions. This removes barriers to knowledge and empowers anyone in the agriculture ecosystem to build frugal and scalable AI solutions for the sector.
The major focus countries are India, Bangladesh, Nepal, Pakistan, and Sri Lanka. The major focus crops are Paddy, Wheat, Maize, Sorghum, Barley, Cotton, Sugarcane, Soybean, and Millets.
This question-answering system is based on the Mistral-7B instruct-tuned model, and akṣara is fine-tuned on a dataset from the above focus countries and crops and then compressed from 16-bit to 4-bit, using QLoRA, for minimal resource consumption during inferencing while still giving ~40% more relevant ROUGE score than GPT-4 Turbo on randomly selected test datasets. The knowledge domain of the model is specific to the agricultural best practices, including climate-smart agricultural practices (CSA) and regenerative agricultural practices (RA) for the above-mentioned focus countries and crops. More geographies and crops will be added later. The model is trained on a database containing information from seed sowing to harvesting, covering every phenological stage of the crop growth cycle and different aspects like crop health management, soil management, disease control, and others. The end-to-end pipeline incorporates various aspects of Responsible AI (RAI), like considering local features and preventing harmful content or misinformation.
In Albert Einstein’s words: “You can’t solve a problem on the same level that it was created. You have to rise above it to the next level.” In Agtech, where there’s no precedent, we always start fresh when working on new projects or problems. One challenge we are passionate about solving is the impact of climate risks on agriculture. No matter how many solutions we develop, use cases we explore, or AI models we introduce, the enormity of this problem is such that climate change continues to present new challenges.
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Synopsis: This blog explores solutions for overcoming the adoption barriers in Agtech, particularly for underserved smallholder farmers. It outlines Cropin’s innovative response: ‘akṣara (Akshara),’ an open-source Micro Language Model. Here, we discover the potential of akṣara to dismantle knowledge barriers and empower smallholder farmers to adopt Climate Smart Agriculture and Regenerative Agricultural practices and thrive. It further discusses this innovation and explains how akṣara, tailored for the agriculture domain, is more factually relevant than globally available general Large Language Models.
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Synopsis: Achieving sustainable agriculture requires a significant change from traditional methods. Here, we discuss the change management roadmap, which covers – driving transformation at the grassroots level, leveraging tools and platforms for digital agriculture adoption, rethinking the business model, and revamping communication practices. Solutions like Cropin Cloud provide user-friendly interfaces and collect data to inform decision-making. Additionally, communication channels must be improved to facilitate real-time information exchange between all stakeholders. By prioritizing these aspects of change management, the agricultural sector can transition to more sustainable practices.