Banana plant farmers are facing severe problems of uprooting and breakage of their fully grown plants due to climatic (wind or other) effects. They find it difficult to safeguard their plants from such natural forces owing to their ignorance as to how and where failure could get started. In order to predict failure pattern of banana plants through a Finite Element Analysis (FEA), the mechanical properties of the plants are essential. Isotropic behavior is assumed for the banana plants and the Young’s modulus of elasticity, Poisson’s ratio, compressive strength and flexural strength of the material of stems of banana plants were determined.

A system and a method for assisting a deaf person in responding to a situation in a surrounding environment within a premise are provided. The proposed system is devices for helping deaf people understand sounds in the surrounding. It helps deaf people to do day to day activities more easily by getting alerts about different sounds that occur in the surroundings. An object of the present invention is to provide a system that alert deaf people when some specified sound occurs in the surroundings. The system includes video capturing device that are configured to capture a sequence of images and generate at least one video capturing the sequence of images to cover the plurality of appliances on detecting a sound or on determining a change in the state of the at least one appliance. When a sound is detected, a video is captured of that place and sent to a wearable device.

The automated irrigation system comprises a data preparation processing unit 102 for collecting meteorological data, constructing a soil and crop database thereby storing in a cloud 104; an ETo forecasting model construction processing unit 106 for calculating hourly ETo value for each row of meteorological dataset thereby performing hourly ETo forecast; an ETc estimation processing unit 110 for generating time series ETc database; an irrigation processing unit 112 consisting a soil moisture sensing unit 202 for measuring volumetric water to obtain total available water, a threshold setting unit 204 for defining minimum threshold value and maximum threshold value to irrigate field, and a rainfall sensing unit for sensing rainfall thereafter abruptly stopping irrigation; and a user interface 116 for enabling a user to monitor status of field remotely and to feed soil and crop inputs to activate an irrigation process by one of a manual mode or an automated mode..

The secure smart farming system comprises a physical layer 102 for promoting physical connectivity with a plurality of sensors, drones, autonomous tractors, and an irrigation unit; an edge layer 104 for receiving a real time information of field and crops, irrigation unit, and autonomous tractor status for promoting security monitoring, prediction and a real-time decision support to give permission for a third-party agronomy analytics upon analyzing edge level data through a biometric authentication; a network layer 106 for establishing the edge layer 104 and physical layer connectivity; a cloud layer 108 for storing data in encrypted form, wherein the stored data is decrypted and read only by a registered user associated with the biometric authentication for preventing the data leakages in smart farming and to ensure proper data usage; and a control unit 110 for actuating plurality of sensors, autonomous tractors and automatic irrigation unit.

The present invention relates to the field of computer science, inclusive education, and artificial intelligence technology, in particular to dysgraphia or handwriting disability detection based on language biomarkers in the Indian language system. The system (102) works as a screening, intervention, and recommendation model in orthographically complex languages based on biomarkers. The system (102) collects handwriting samples (304) and handwriting parameters (306). The system (102) further acquires at least heartbeat, blood pressure, and brain activity data pertaining to handwriting activities (306). Further, the system (102) processes the collected samples and data to identify biomarkers that indicate some biological state or condition. In the present disclosure, the biomarkers are introduced with natural language processing and machine learning for detecting dysgraphia. Upon detection of dysgraphia, the system (102) provides feedback (318) to improve the handwriting of the user (104).