Abstract: This disclosure relates generally to system and method to determine crop growth stage nutrient deficiencies. Diagnosing correct nutrient deficiencies in the plant is very challenging based on plant image analysis during the cropping season. The method of the present disclosure enables assessing nutrient deficiency using image processing techniques according to current crop growth stage. The method receives from an image capturing device a plurality of crop images of one or more crop fields. Further, trained single shot deep learning network determines a crop growth stage from a plurality of crop growth stages for each crop by extracting a plurality of morphological features. Then, the health state of the crop is determined based on a balanced plant nutrition index (BPNI) value. Further, a plurality of nutrient deficiencies corresponding to the current crop growth stage of the unhealthy crop. Further, a total nutrient deficiency score for deficient nutrients of the unhealthy crop.

Abstract: Precise estimation of effective pest-disease severity is a challenge due to their ability of quick spread, resistance to pesticide development, quality, and systematic data. Conventionally, an inter-dependency of sequence of operations and impact of the inter-dependency on data collection has not been considered. Embodiments of the present disclosure provide a system and method for estimation of effective pest-disease severity score using a personalized crop protocol driven unmanned scouting vehicle. The system of the present disclosure intelligently makes use of the personalized crop protocol and feedback from users on a farm for determining optimal sampling locations from the farm. Further, a standardized and systematic way of optimal data capture from precise plant positions in the farm is provided using selected sensors at appropriate time. The standardized and systematic way of optimal data capture considers the impact of various pests, disease, and natural enemies available at a given instance.

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: A method and system is provided for integrated crop quality management and crop certification comprises of selecting a geographical area for growing a crop, auto assisting a user to select a crop certification of the selected geographical area, generating a customized crop protocol and training calendar, enabling implementation of the steps involved in growing the crop, adapting to crop compliance of the crop in the selected geographical area, generating a plurality of periodic compliance alerts for different stages of the crop for growth monitoring, tracking a plurality of action items depicting goals on the generated periodic alerts to maintain a crop compliance business logic of the crop, generating a crop certification compliance score and index for the crop compliance certificate of the crop, transferring the crop certification compliance score and index to the certifying agency for validating and generating a crop compliance certificate.