GreenGuard CNN-Enhanced Paddy Leaf Detection for Crop Health Monitoring

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Фев. 10, 2025

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

Язык: Английский

Techniques for load balancing throughout the cloud: a comprehensive literature analysis

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Янв. 12, 2025

Recently, "Cloud-Computing (CC)" has become increasingly common because it's a new paradigm for handling massive challenges in versatile and efficient way. CC is form of decentralized computation that uses an online network to facilitate the sharing various computational computing resources among large number consumers, most commonly referred as "Cloud-Users (CUs)”. The burdens on "Cloud-Server (CS)" could be either light or too heavy, depending how quickly volume CUs their demands are growing. Higher response times high resource usage two many issues resulting from these conditions. To address enhance CS efficiency, "Load-Balancing (LB)" approaches very effective. goal LB approach identify over-loading under-loading CSs distribute workload accordingly. Publications have employed numerous techniques broad effectiveness solutions, boost confidence end CUs, ensure effective governance suitable CS. A successful technique distributes tasks within network, thereby increasing performance maximizing utilization. Experts shown abundance engagement this issue offered several remedies over past decade. primary extensive review article examine different variables provide critical analysis current techniques. Additionally, outlines requirements explores associated with context CC. Conventional insufficient they ignore operational efficiency “Fault-Tolerance (FT)” measures. present article, bridge gaps existing research, assist academics gaining more knowledge about

Язык: Английский