A Forecasting Technique for Powdery Mildew Disease Prediction in Tomato Plants

[1]  Hesham Hefny,et al.  Tomato leaves diseases detection approach based on Support Vector Machines , 2015, 2015 11th International Computer Engineering Conference (ICENCO).

[2]  Anuradha Chug,et al.  Application of extreme learning machine in plant disease prediction for highly imbalanced dataset , 2020, Journal of Statistics and Management Systems.

[3]  N. Rajpoot,et al.  Automatic Detection of Diseased Tomato Plants Using Thermal and Stereo Visible Light Images , 2015, PloS one.

[4]  Anuradha Chug,et al.  Recent Advancements in Multimedia Big Data Computing for IoT Applications in Precision Agriculture: Opportunities, Issues, and Challenges , 2019, Intelligent Systems Reference Library.

[5]  Sotiris B. Kotsiantis,et al.  Decision trees: a recent overview , 2011, Artificial Intelligence Review.

[6]  Amit Prakash Singh,et al.  Hybrid SVM-LR Classifier for Powdery Mildew Disease Prediction in Tomato Plant , 2020, 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN).

[7]  L. Plümer,et al.  Original paper: Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance , 2010 .

[8]  P. John Clarkson,et al.  Early detection of diseases in tomato crops: An Electronic Nose and intelligent systems approach , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[9]  S. Thomson,et al.  Source of Inoculum, Yield, and Quality of Tomato as Affected by Leveillula taurica , 1987 .

[10]  Amit Prakash Singh,et al.  Application of convolutional neural networks for evaluation of disease severity in tomato plant , 2020, Journal of Discrete Mathematical Sciences and Cryptography.

[11]  Amit Prakash Singh,et al.  Exploring capsule networks for disease classification in plants , 2020 .

[12]  Virendra P. Vishwakarma,et al.  A novel non-linear modifier for adaptive illumination normalization for robust face recognition , 2020, Multimedia Tools and Applications.

[13]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[14]  Amit Prakash Singh,et al.  Prediction Models for Identification and Diagnosis of Tomato Plant Diseases , 2018, 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[15]  Amit Prakash Singh,et al.  Deep Learning-Based Mobile Application for Plant Disease Diagnosis , 2019, Advances in Environmental Engineering and Green Technologies.

[16]  Sang Cheol Kim,et al.  A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition , 2017, Sensors.