GreenGuard CNN-Enhanced Paddy Leaf Detection for Crop Health Monitoring
DOI:
https://doi.org/10.22399/ijcesen.1027Keywords:
Agri Vision, Green Lens, Farm Eye, Growth Guard, Harvest GuardAbstract
The GreenGuard: CNN-Enhanced Paddy Leaf Detection for Crop Health Monitoring initiative will create multiple future-oriented results. The processing of agricultural imagery becomes revolutionized through the combination of median filtering and Exponential Tsallis entropy Gaussian Mixture model (ExTS-GMM) advanced techniques initially. The essential preprocessing operation delivers better quality data to the Convolutional Neural Network (CNN) classifier which results in optimal performance outcomes. The simple integration of CNN classifiers will launch an innovative age that delivers more accurate and efficient paddy leaf detection for agricultural images. Deep learning features of a CNN enable it to uncover complex structural details found in both normal and sick paddy leaf specimens. The classifier's aptitude creates an efficient pathway to execute precise assessment and group data into appropriate categories while processing extended agricultural database information rapidly. Effective implementation of "GreenGuard" will reshape conventional paddy field crop health monitoring systems into modern standards. Modern agricultural stakeholders can make precise choices about pest management along with disease control and irrigation schedules because of timely crop health assessments from the implemented system. The new capabilities generated from this empowerment system will create major crop yield growth and enhance food safety protocols as well as promote sustainable farming throughout paddy farms globally.
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