| Autor: |
Ramanjot; Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, Punjab, India., Mittal U; Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, Punjab, India., Wadhawan A; Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, Punjab, India., Singla J; School of Engineering and Technology, CT University, Ludhiana 142024, Punjab, India., Jhanjhi NZ; School of Computer Science, SCS, Taylor's University, Subang Jaya 47500, Malaysia., Ghoniem RM; Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Riyadh, Saudi Arabia., Ray SK; School of Computer Science, SCS, Taylor's University, Subang Jaya 47500, Malaysia., Abdelmaboud A; Department of Information Systems, King Khalid University, Abha 61913, Muhayel Aseer, Saudi Arabia. |
| Abstrakt: |
A significant majority of the population in India makes their living through agriculture. Different illnesses that develop due to changing weather patterns and are caused by pathogenic organisms impact the yields of diverse plant species. The present article analyzed some of the existing techniques in terms of data sources, pre-processing techniques, feature extraction techniques, data augmentation techniques, models utilized for detecting and classifying diseases that affect the plant, how the quality of images was enhanced, how overfitting of the model was reduced, and accuracy. The research papers for this study were selected using various keywords from peer-reviewed publications from various databases published between 2010 and 2022. A total of 182 papers were identified and reviewed for their direct relevance to plant disease detection and classification, of which 75 papers were selected for this review after exclusion based on the title, abstract, conclusion, and full text. Researchers will find this work to be a useful resource in recognizing the potential of various existing techniques through data-driven approaches while identifying plant diseases by enhancing system performance and accuracy. |
