Tata Consultancy Services Limited

Srinivasu Pappula

Jayantrao Mohite

Sanat Sarangi

Suryakant Ashok Sawant

Sanjay Madhukar Kimbahune

Prakruti Vinodchandra Bhatt

Ankur Pandit

Pankaj Harish Doke

Sujit Shinde

Karan Bhavsar

Akhilesh Srivastava

Sylvan Lobo

Mariappan Sakkan

Vaibhav Sadashiv Lonkar

Prachin Lalit Jain

Harsh Vishwakarma

Dineshkumar Singh

Dinesh Kumar Singh

Karthik Srinivasan

Sujit Devkar

Sandika Biswas

Bhushan Gurmukhdas Jagyasi

Bhaskar Pawar

Manash Paul

Rajesh Urkude

Subhadra Varma Pusapati

Avil Saunshi

Nagameena Neelakantapillai

Praneet Padinchare Vazhayil

Shubhadra Varma Pusapati

Rishabh Agarwal

Ravinkumar Sivalingam

Saranya Ramanath

Gopu Chandrasenan

Swagatam Bose Choudhury

Rajatkumar Shrivastav

Saibhargavi Gurram

Sanket Junagade

Harshal Jain

Salim Hamsa

Pankaj Doke

Karan Rajesh Bhavsar

Shubhadra Varma Pasapati

This disclosure relates generally to assessing soil carbon sequestration. One of the driving factors for climate change and global warming is emission of greenhouse gas emissions. Of the possible ways to reduce climate change and global warming adoption, sustainable agricultural practices will enable efficient soil carbon sequestration thereby reducing the greenhouse gases as well as increasing the crop yield. The disclosure is a method and a system for real time assessing soil carbon sequestration of farm based on remote sensing. The soil carbon sequestration of farm is assessed by continuously monitoring the farm at real time based on remote sensing using a plurality of satellite data and a plurality of farming data using several techniques. The several techniques utilized for assessing soil carbon sequestration includes machine learning, a carbon sequestration estimation technique, estimating a crop health index and an adoption index and computing a set of carbon sequestration parameters.

Method and system for assessing soil carbon sequestration of a farm based on remote sensing

Coarse resolution satellite data has limited information content of land captured in the images and small area field boundary detection is technically challenging. Embodiments of the present disclosure provide a method and system for detection of field boundaries using coarse resolution satellite data and GIS. Multi-layer crop segmentation approach is used over RoI, wherein agricultural land related segments are derived from a soil layer, a temporal stack for a current season and a temporal change layer from the coarse resolution satellite data of the RoI. Segmentation based on different aspects captures various details at each segmentation layer. The segments are then combined, which aggregates all information related to the land to accurately detect the field boundaries in the RoI. The field boundaries are further refined using GIS topology operations. Baseline cadastral maps are updated using information from maps generated using refined field boundaries identified in real time for each season.

Method and system for detection of field boundaries using coarse resolution satellite data and GIS

State of the art systems used for monitoring of land (for example, agricultural land), fail to accurately assess various conditions in the land area and make predictions. The disclosure herein generally relates to agricultural systems, and, more particularly, to a method and system for micro-climate management in a land area being monitored. The system groups the different plots based on sensor trend information and based on a determined homogeneity information. A micro-climate view of the land area is accordingly generated, which in turn is used to generate micro-climate predictions.

Method and system for micro-climate management

A method and system that allows on-the-fly synthesis and evaluation of services a scale has been provided. The method and system provide a mechanism where the service offering and their price points are accessible in a machine format and allow themselves to be lent to synthesize new service offerings with budgetary constraints, within the parameters of the predefined time windows. The system allows both a service provider and a service consumer to use a shared screen environment for service synthesis and valuation with the service provider playing the role of navigator. The system comprises of re-routing and navigating a plurality of nodes in the service composition graphs based on specified optimization parameters as chosen by the service consumer and tuned by the service provider. The method comprises of generation of the graph and graph traversal algorithms for along with service composition nodes and their specifications.

Method and system for service synthesis and evaluation using patterns

Agriculture is impacted due to various parameters that results into heavy losses to the crops. Existing methods rely on manual inventories from fields and statistical results are prone to errors thus classification of crops may not be accurate. Present disclosure provides systems and methods for detection and prediction of disguised and potential non-performing assets (NPAs) in agriculture. Disguised NPAs detection involves, obtaining satellite data from sources and natural calamity, detecting of crop for past years, crop growth, estimating yields, market prices for individual years, AC zones, etc. wherein a generate performance score is generated for each field for classifying crop as disguised NPA. Predicting potential NPAs includes obtaining current season satellite data, data received from the fields via sensors, determining crop protocol being followed, forecast weather, estimating cumulative growth, feedback received from fields, projected yield, estimating crop losses, and then performance score is generated for predicting crops as potential NPA.

Systems and methods for detecting disguised non-performing assets (NPAs) and predicting potential NPAs in agriculture

Monitoring of animals in a herd has various challenges. A method and a system for monitoring and measuring the behavior of a plurality of animals in a herd chamber is provided. The disclosure provides a method to develop a model to identify the animal interactions between a given set of herds of two or more animals. The system uses image, RFID sensors, animal phenotyping information such as species, breed, age and reproduction cycle information to generate an animal profile. This profile is used to quantify the interaction into an aggression score to the animals in the given herd. The aggression score over a given duration can help herd manager to put the animals with similar aggression score together. This will also help analyze the temporal aspects of the aggression score for a given animal and take preventive measures.

Method and system for monitoring and measuring the behavior of a plurality of animals

This disclosure relates generally to a system and method for monitoring performance of low-cost sensors plied in a field for soil moisture measurement. The low-cost sensors are calibrated to give useful derived parameters to support farming such as volumetric water content (VWC) of the soil. Further, the steps are being incorporated to de-noise their response to derive stable measurements similar to expensive rugged sensors. The calibration of the low-cost sensor and normalization of incoming values from the low-cost sensor are based on values determined through rugged sensors for soil moisture measurement. The normalization involves finding a minimum value and maximum value of soil moisture. Performance of the low-cost sensors are analyzed based on a range of values of the soil moisture. Finally, the performance analysis provides degradation stages and based on the degradation stages evaluated recommendations to modify the sensor are shared with the user.

System and method for monitoring health of low-cost sensors used in soil moisture measurement

The disclosure herein relates to identification of agro-phenological zones. Further the disclosed method and system also shares techniques for updating of the identified/existing agro-phenological zones. In a diverse geographical domain (like India), in order to maximize the crop production (avoid crop failures) from the available resources and prevailing diverse climatic conditions it necessary to use the resources and technology to infer the best agriculture approach on an individual location. The invention enables identification of the agro-phenological zones based on satellite image, weather data, soil data and cloud free historical satellite data using several techniques that includes machine learning, time series analysis, heuristic time series analysis technique and clustering. Further the invention also discloses techniques to update the identified/existing agro-phenological zones using historic data of agro-phenological zones of satellite image.

Method and system for identification of agro-phenological zones and updation of agro-phenological zones

State of art techniques mostly rely of computationally intensive, time consuming Neural Networks. Embodiments provide a method and system for identification and classification of different grain and adulterant types for grain grading analysis. The method analyzes input image of grain sample of elements to determine morphological features of elements, using dynamically determined calibration factor from reference object in the image. Variation in perimeter of elements is used to perform classification of elements into target grain size, low size adulterants and higher size adulterants. The aspect ratio of target grain determines grain variety and adulterants determine adulteration percentage. Elements are classified into grain colored and non-grain colored adulterants. Grain colored adulterants are further classified as Grain Like Impurities and non-GLI, using predefined ranges of standard deviation of perimeter metric. Weight of grain colored adulterants and non-grain colored adulterant is obtained using mapping of predefined weights to the aspect ratio.

Method and system for identification and classification of different grain and adulterant types

State of the art authentication mechanisms have limitations when used by Basic Emergent User (BEU). Embodiments herein provide method and system for recognition based multi-skill-multi-sensory passwords with dynamic identification of sensor combination based on end user skill, specifically for the BEU. The method disclosed generates user profile using a task-based technique to determine skill and capability of the user associated with each of a plurality of sensory categories used for generating a multi-skill-multi-sensory password. Each of the plurality of sensory categories are graded is descending order based on user's skill and capability and user is offered to select each password entry from a particular sensor category in accordance with the identified grade in each iteration. Thus, the entropy of password generation in the context of user is split among the sensor categories. Furthermore, recognition-based out of sequence approach is used during user authentication instead of more challenging memorizing-based approach.

Recognition based multi-skill-multi-sensory passwords with dynamic identification of sensor combination based on end user skill

Crop loss estimation allows a user to monitor and estimate damage to the crops due to various natural events/factors. State of the art systems used for the crop loss estimation have the disadvantage that they do not convey to the users extent of damage. In addition to this, the existing methods do not take into account the recovery factor of the crops due to multiple factors and end up in overestimating the loss. The disclosure herein generally relates to crop monitoring, and, more particularly, to a method and system for crop loss estimation. In this method, crop loss is assessed based on real-time weather parameters and remote sensing data collected and processed, and crops are classified as being in one of a repairable damage class and a permanent damage class. The system also quantifies the crop loss, which allows the user to understand magnitude of the crop loss.

Method and system for crop loss estimation

Plant health estimation is required to be performed so as to detect any health issues in early stages, so as to take counter measures. Existing systems for the plant health estimation perform the health estimation by considering data obtained from satellite images of the plants being monitored. However this alone may not be much effective as the satellite images fail to provide information on many parameters which have direct or indirect impact on health of the plants. Disclosed herein are a method and a system for plant health estimation, wherein the system performs health estimation at a macro level and a micro level. The macro level health estimation is done using satellite images of the plants as inputs, whereas the micro level health estimation is done by collecting and processing sensor data with respect to various parameters that affect health of a plant.

Method and system for plant health estimation

Machine Learning models to be created for crop mapping for any region, require huge volumes of ground truth data requiring manual effort in generating region specific training dataset. Method and system for providing generalized approach for crop mapping across regions with varying characteristics is disclosed. The method provides automatic generation of a labelled pixel dataset representing cropping pattern of a Region of Interest (ROI) for building a ML crop mapping model for the ROI. The generated labelled pixel dataset captures regional dependency and localized phenological indicators for the ROI. ML crop mapping model is updated using a database, regularly updated for the set of crops and the plurality of features associated with each of the set of crops and corresponding the set of crops.

Method and system for providing generalized approach for crop mapping across regions with varying characteristics

Existing techniques in precision farming comprise supervised event detection and need labeled training data which is tedious considering the large number of crops, differences therein and even larger number of diseases and pests. The present disclosure provides an unsupervised method and uses images of any size captured in an uncontrolled environment. The methods and systems disclosed find application in automatically localizing and classifying events, including health state and growth stage and also estimating an extent of manifestation of the event. Information of spatial continuity in pixels and boundaries in a given image is used to update the feature representation and label assignment to every pixel using a fully convolutional network. Back propagation of the pixel labels modified according to the output of a graph based method helps the neural network to converge and provide a time efficient solution.

Automated unsupervised localization of context sensitive events in crops and computing extent thereof

A processor implemented method of detecting a concentration of plurality of chemical residue in an agricultural produce is provided. The method include (a) receiving, by a hyper spectral device, a data set associated with one or more reflectance measurements of the agricultural produce; (b) determining, data associated with a plurality of bands; (c) dynamically reiterating, the steps (a) and (b) at predetermined time interval to obtain a trained dataset; (d) determining, relevant wavelengths among the selected trained data sets based on a feature selection technique to form an array of emitters; (e) calibrating, by the identified array of emitters, to emit light on the detecting region of one or more sample of the agricultural produce to obtain data associated with reflectance and transmittance; and (f) calculating, a calibration index with a de-multiplication flag to detect presence or absence of the plurality of chemical residue in the agricultural produce.

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Hyderabad Telangana, India

Srinivasu Pappula