Prediction of Cotton and Tomato Leaf Disease using Ensemble Learning Algorithm
DOI:
https://doi.org/10.26438/ijcse/v12i8.1017Keywords:
Cotton and Tomato leaves, Disease Prediction, Digital Image Processing, CNN, Transfer LearningAbstract
Agriculture, one of the primary and basic need for living, plays a vital role in the global economy. With growth in newer technology, plants are also more susceptible to new and divergent type of diseases. This type of disease affects the plants leaves and ultimately decreases its yield. This research paper focuses on industrial crop Cotton and food crop Tomato diseased leaf prediction by the framers. It classifies six varieties of cotton leaf diseases and ten varieties of tomato leaf diseases. The approach leverages image processing techniques, transfer learning with CNN techniques and ensemble techniques to classify images of cotton and tomato plant leaves. The main motivation of this research work is to help the farmers predict healthy and infected plant leaves in their farm land with the motivation of implementing sensors in their field. It also encourages future generations to be aware of such diseases in plant leaves and help to eradicate such fungal and viral disease in plants.
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