Prediction of plant pest detection using improved mask FRCNN in cloud environment

Author:

Deepika P.,Arthi B.

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Mechanics of Materials,Electronic, Optical and Magnetic Materials

Reference34 articles.

1. Recognizing wheat aphid disease using a novel parallel real-time technique based on mask scoring RCNN;Kukreja,2022

2. Automatic segmentation of overlapped poplar seedling leaves combining mask R-CNN and DBSCAN;Liu;Comput. Electron. Agric.,2020

3. A systematic review on image processing and machine learning techniques for detecting plant diseases;Gobalakrishnan,2020

4. Implementation of automated annotation through mask RCNN object detection model in CVAT using AWS EC2 instance;Guillermo,2020

5. IoT and deep learning based smart greenhouse disease prediction;Pothuganti,2021

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1. The Power of Vision Transformers and Acoustic Sensors for Cotton Pest Detection;IEEE Open Journal of the Computer Society;2024

2. Prediction of leaf disease and pest detection using deep learning;AIP Conference Proceedings;2024

3. Deep Reinforcement Learning with Fuzzy Inference System for Prediction of Crop-sowing Windows;2023 2nd International Conference on Futuristic Technologies (INCOFT);2023-11-24

4. Detection of Whiteflies in Plants using Deep Learning;2023 International Conference on Circuit Power and Computing Technologies (ICCPCT);2023-08-10

5. YOLO-IP;Artificial Intelligence Tools and Technologies for Smart Farming and Agriculture Practices;2023-06-30

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