Plant Disease Classification Using Mobile-Captured Images: A Deep Learning Approach
Keywords:
Image processing, Deep Learning, Application, Rice disease, Classification.Abstract
The farming community's top priority is the early diagnosis of plant diseases. Plant disease can be detected with great accuracy thanks to the availability of modern cell phones and digital cameras with enhanced picture acquisition capabilities. This study classified 14 rice illnesses and signs of nutrient inadequacy using 2500 smartphone photos of various rice plant components organised into different groups as well as 500 real-time validation images. Affected areas were segmented using a variety of picture segmentation approaches, such as foreground extraction. Model and technique optimisation for applications on smartphones with offline functioning capabilities has also been discussed. Additionally, in order to improve classification performance, a dynamic framework that changes the model when it drops below a specified threshold level has been created and demonstrated. To choose the optimal method for transfer learning, several image classification models were compared using a wide range of supporting metrics. The deep belief network model-based Android app "Farmer" was tested for the ability to detect several instances of sickness in a single capture. More research is needed in order to test the programme on smartphones with various configurations.
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