Tata Consultancy Services Limited

Sanat Sarangi

Srinivasu Pappula

Prakruti Vinodchandra Bhatt

Jayantrao Mohite

Prachin Lalit Jain

Suryakant Ashok Sawant

Vaibhav Sadashiv Lonkar

Dinesh Kumar Singh

Karthik Srinivasan

Sanjay Madhukar Kimbahune

Mariappan Sakkan

Dineshkumar Singh

Avil Saunshi

Saibhargavi Gurram

Sanket Junagade

Praneet Padinchare Vazhayil

Swagatam Bose Choudhury

Ravinkumar Sivalingam

Saranya Ramanath

Nagameena Neelakantapillai

Shubhadra Varma Pusapati

Gopu Chandrasenan

Rajatkumar Shrivastav

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

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

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

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

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

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

This disclosure relates generally to estimating age of a leaf using morphological features extracted from segmented leaves. Traditionally, leaf age estimation requires a single leaf to be plucked from the plant and its image to be captured in a controlled environment. The method and system of the present disclosure obviates these needs and enables obtaining one or more full leaves from images captured in an uncontrolled environment. The method comprises segmenting the image to identify veins of the leaves that further enable obtaining the full leaves. The obtained leaves further enable identifying an associated plant species. The method also discloses some morphological features which are fed to a pre-trained multivariable linear regression model to estimate age of every leaf. The estimated leaf age finds application in estimation of multiple plant characteristics like photosynthetic rate, transpiration, nitrogen content and health of the plants.

Method and system for leaf age estimation based on morphological features extracted from segmented leaves

A system and method for achieving auto-adaptive clustering in a sensor network has been explained. The system performs a hierarchical clustering in sensor networks to maximize the lifetime of the network. The system includes a set of sensor nodes and a sink node. The clusters in sensor networks are formed automatically from a large number of deployed nodes where the cluster characteristics are driven by the measurement requirements defined by the end-user. The system also employs a clustering algorithm to achieve adaptive clustering. The processor further includes a first level clustering module for grouping the set of sensor nodes into data level clusters based on the measurements. The processor further includes a second level clustering module for grouping the set of sensor nodes in the data level clusters into the location level clusters based on location. In another embodiment, that clustering can go on to more than two levels.

Method and system for achieving auto adaptive clustering in a sensor network

The present disclosure provides methods and systems for automated identification of agro-climatic zones. The methods deal with receiving parameters pertaining to ambience and soil from various external systems for a geographical region via one or more interaction methods. The parameters may be raw data or derived from raw data, homogenized and stored in a generic and hierarchical format for easy consumption. Inference is drawn from the parameters and associated attributes by comparing with historic attributes available in a knowledge base module for a corresponding agro-climatic zone. Inferences may also be made from parameters available in encoded form such as images, videos and ontological knowledge. Based on the comparison, a score is generated that reflects the degree of compliance with pre-defined agro-climatic zones.

Methods and systems for automated identification of agro-climatic zones

This disclosure relates to precision agriculture that relies on monitoring micro-climatic conditions of a farm to make accurate disease forecasts for better crop protection and improve yield efficiency. Conventional systems face challenge in managing energy and bandwidth of transmission considering the humongous volume of data generated in a field through IoT based sensors. The present disclosure provides energy-efficient adaptive parameter sampling from the field by optimally configuring the parameter sampling rate thereby maximizing energy-efficiency. This helps reduce unnecessary traffic to a cloud while extending network lifetime.

Methods and systems for adaptive parameter sampling

The present disclosure addresses the technical problem of enabling automated inferencing of changes in spatio-temporal images by leveraging the high level robust features extracted from a Convolutional Neural Network (CNN) trained on varied contexts instead of data dependent feature methods. Unsupervised clustering on the high level features eliminates the cumbersome requirement of labeling the images. Since models are not trained on any specific context, any image may be accepted. Real time inferencing is enabled by a certain combination of unsupervised clustering and supervised classification. A cloud-edge topology ensures real time inferencing even when connectivity is not available by ensuring updated classification models are deployed on the edge. Creating a knowledge ontology based on adaptive learning enables inferencing of an incoming image with varying levels of precision. Precision farming may be an application of the present disclosure.

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Thane, India

Sanat Sarangi