A Review: Diseased Leaf Feature Extraction Using Machine Learning Classification Techniques
Keywords:
Artificial Neural Network, Convolution Neural Network, Fuzzy Logic, K-Nearest Neighbor, Machine learning, Random Forest, Support Vector MachineAbstract
The emergence of high-performance computing and big data technologies created a need for machine learning (ML) which has led to creation of new opportunities in the multidisciplinary data intensive domains. ML has increasingly become an important field in the contemporary computing world as well as our lives. A lot of researches have been done with an aim of making the computers intelligent which has had a lot of influences in different areas of study such as language processing, medicine, agriculture and computer vision. The fast advancement of machine learning models has brought about more sophisticated tools that are capable of learning image characteristics. In terms of network design, optimization functions and training methods, the models perform differently. We present a review of machine learning approaches that are applied in diseased plant classification through use of images. The research looks at different leaf plant diseases and features extracted, the techniques utilized and how they work, the data sources employed and the general acquired accuracy performance of the techniques using the authors’ metrics. The performance of the techniques is presented as well as their benefits and drawbacks. In overall, the data suggest that some techniques have excellent performance with regards to classification accuracy. However, the performance of any technique is strongly contingent on the quality of the dataset employed. Finally, potential areas and activities are suggested as future work recommendations.
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