Abstract: In this world, there are lot of diseases that affecting the Plants These causes loss in the yield and quantity of the agricultural products. It is very difficult to monitor  the plant diseases manually. Therefore the use of computer Vision to detect plant diseases is becoming increasingly important in agricultural automation. However most existing models are designed to identify diseases in a specific type of plant using convolutional Neural Network (CNN) algorithm based on Machine Learning. But it is time consuming and less accuracy in  detecting disease  to overcome this problem  , we proposed  a new approach for identifying plant disease that can be applied to multiple plant species using CNN algorithm with VGG16 architecture which we train and test on a newly collected dataset consisting of images of healthy and diseased leaves. Our results demonstrate the potential of deep learning for effective plant disease detection which could help to reduce economic losses and promote Sustainable agriculture.

Keywords: Plant disease, agricultural automation, computer vision, multilabel classification method, convolutional neural network (CNN) architectures

PDF | DOI: 10.17148/IJARCCE.2023.124137

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