A Review of Plant Disease Prediction Methods for Agricultural Applications
Open Access
- 30 October 2022
- journal article
- review article
- Published by Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP in International Journal of Engineering and Advanced Technology
- Vol. 12 (1), 98-103
- https://doi.org/10.35940/ijeat.a3856.1012122
Abstract
Due to the decrease in plant quality and productivity, plant diseases seem to be responsible for significant economic losses in the world. As a result, farmers nowadays consider plant disease prediction to be an important area of research. To help an accurate prediction of plant disease, numerous techniques have been detailed in the literature. To highlight the many issues with current approaches for problem-solving predictions, we will evaluate various literary works that are focused on plant disease prediction in the agricultural industry. Based on several variables, including different datasets, year of publication and journals, performance metrics, and other considerations, the analyses of various approaches are enhanced in this case, and include the advantages and disadvantages based on the analysis of the methods. Finally, the paper concludes by discussing future research areas and difficulties in improving prediction performance for the plant disease prediction techniques used in the growing agricultural process.Keywords
This publication has 36 references indexed in Scilit:
- Benefits of hyperspectral imaging for plant disease detection and plant protection: a technical perspectiveJournal of Plant Diseases and Protection, 2017
- LeafNet: A computer vision system for automatic plant species identificationEcological Informatics, 2017
- Apple disease classification using color, texture and shape features from imagesSignal, Image and Video Processing, 2015
- Image Processing for Soybean Disease Classification and Severity EstimationPublished by Springer Science and Business Media LLC ,2015
- Automatic recognition of quarantine citrus diseasesExpert Systems with Applications, 2013
- Detecting powdery mildew of winter wheat using leaf level hyperspectral measurementsComputers and Electronics in Agriculture, 2012
- Image recognition of plant diseases based on principal component analysis and neural networksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2012
- The potential of automatic methods of classification to identify leaf diseases from multispectral imagesPrecision Agriculture, 2011
- Machine learning techniques in disease forecasting: a case study on rice blast predictionBMC Bioinformatics, 2006
- A generalized regression neural network and its application for leaf wetness prediction to forecast plant diseaseChemometrics and Intelligent Laboratory Systems, 1999