Abstract: This disclosure relates generally to system and method for providing personalized explainable response by generating personalized context-aware prompt using contextual information. Agriculture faces critical challenges such as adverse climate, pest/disease outbreak, soil, degradation and the like due to which personalized, contextual and explainable advisories are needed to achieve sustainable and optimal agricultural productivity. The method receives a multimedia user query to provide personalized explainable response using initial validation and a personalized context generation. Further, a personalized context aware prompt is generated to obtain response from a plurality of LLM's. Simultaneously, a contextually relevant user profile is composed to generate a personalized explainable response. Additionally, the method is capable of providing the personalized explainable response in multilingual and multimodal content.

Abstract: This disclosure relates generally to system and method for estimating crop water requirement using multi-sensor data fusion. Increasing global population is imparting pressure on both agriculture for food demand and limited freshwater resources for consumption. Estimating crop water requirement reduces water demand for crop production. The method divides soil and crop into multiple vertical and horizontal profiles to estimate water balance thereby reducing the errors in estimation of crop water requirement. Additionally, the method has capability to interlink the multiple data sets such as satellite based earth observations, weather observations from IoT sensors, Weather forecasts from global circulation models, and crop knowledge base for crop water requirement estimation.

Abstract: This disclosure relates generally to system and method for providing personalized explainable response by generating personalized context-aware prompt using contextual information. Agriculture faces critical challenges such as adverse climate, pest/disease outbreak, soil, degradation and the like due to which personalized, contextual and explainable advisories are needed to achieve sustainable and optimal agricultural productivity. The method receives a multimedia user query to provide personalized explainable response using initial validation and a personalized context generation. Further, a personalized context aware prompt is generated to obtain response from a plurality of LLM's. Simultaneously, a contextually relevant user profile is composed to generate a personalized explainable response. Additionally, the method is capable of providing the personalized explainable response in multilingual and multimodal content.

Abstract: The present disclosure provide a system and method for personalized recommendation and transaction of non-fungible tokens of livestock embryos. There is a challenge in uniquely identifying livestock embryos and tracking them authentically for their ownership and all the associated details about the donor male, female, their lineage and/or the like. The present disclosure leverages blockchain technology to create unique digital assets representing the physical livestock embryos and identified by a unique token. By utilizing smart contracts on a blockchain platform, secure creation, transfer, and ownership tracking of livestock embryo NFTs is enabled. Further, a machine learning based approach for personalized recommendation of the livestock embryos with associated costs to the user based on the available NFT data, domain knowledge about the breeding policy and needs of the user is provided. A marketplace transaction system for fair trading of the livestock embryo NFTs is disclosed.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a computing device, receiving clickstream information of a customer visiting a website of an organization, the clickstream information is indicative of an active web session of the customer on the website, and predicting, using a machine learning (ML) model, a propensity of the customer performing a particular action during the active web session on the website. The method also includes, by the computing device, providing proactive customer support to the customer based on the predicted propensity of the customer performing the particular action. The website may be a support website of the organization. The particular action may include contacting technical support of the organization and/or making a purchase during the active web session of the customer on the website.