Abstract
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.
Highlights
Modern technology is giving farmers methods of producing more food, food production is still quite low
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 tested on the test set to check against overfitting before used to detect disease on the validation set which showed very positive results
Summary
Modern technology is giving farmers methods of producing more food, food production is still quite low. The Food and Agriculture Organization (FAO) of the United Nations (UN) estimates a raise in global population to about 9.6 billion people by 2050 with need to match that figure in food production to increase by 70 percent by 2050 [1]. Food production is being reinforced with smart computing technology from environment control, disease detection, to disease prediction. Machine learning techniques are championing the trend of smart farming [2]. Plant leaves are very vital parts of a plant, being the major channel for photosynthesis which is a major source of nutrition and growth for plants. This research is directed towards detecting two tomato
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
