Machine learning techniques are revolutionizing multiple industries, various
researches have been put forward as regards mitigating pest and disease effect on food
production. The ability to identify plant disease on time can help reduce the level of destruction
caused by the diseases. This paper proposes the use of Deep Convolutional Neural Network
(DCNN) as classification technique using keras and tensorflow python machine learning
libraries to build a model deployed on a hand-held raspberry pi device for on-site plant disease
classification. Convolutional Neural Networks (CNN) can automatically recognize interesting
areas in images which reduces the need for image processing, training images were gotten from
plantvillage.org and split into training, testing and validation sets, the training images were
augmented and fed into a DCNN model for training the model was then tested on the test set to
check against overfitting before finally used to detect disease on the validation set which
showed very positive results. Results from this research shows that DCNN and the framework
in this paper can be used to develop highly efficient plant disease detection models