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Home > Archives > Volume 20, No 8 (2022) > Article

DOI: 10.14704/nq.2022.20.8.NQ44427

ATTEMPTING AGRICULTURE OBSERVATIONS EMPLOYING IMAGE AND TIME SEQUENCES USING MACHINE LEARNING APPROACH

KARTHIKEYAN N, LEKHAA TR, KARTHIK S, SUMATHI P

Abstract

Based on the yearly differences the escalation in opposition for natural vegetation’s and climatic modification collisions for plantations, observing universal cultivations along with natural crop situations are increasingly pertinent especially for food anxious regions. The information from remote image sequences at increased sequential and minimized spatial resolution could aid in supporting the observation as they offer initial data over the closest real time immense regions. The SORUSITS software is a self-motivated toolbox designed for observing the surroundings especially creating clear and proof based data for crop yield and choice making. It comprises immense count of tools with the intention of mining plantation highlights from image and time sequences assessing the crucial influence of variances over crop yields and distributing the data with various gatherings. SORUSITS provides a combined and bendable assessment environment using a user friendly graphical interface permitting linear processes and increased level of computerization for operating sequences. It is liberally scattered for non – profitable usage and wide reporting

Keywords

SORUSITS, Remote Sensing, GUI, Crop Yields and Climatic Conditions

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