Abstract
Agriculture improvement is a national economic issue that extremely depends on productivity. The explanation of disease detection in plants plays a significant role in the agriculture field. Accurate prediction of the plant disease can help treat the leaf as early as possible, which controls the economic loss. This paper aims to use the Image processing techniques with Convolutional Neural Network (CNN). It is one of the deep learning techniques to classify and detect plant leaf diseases. A publicly available Plant village dataset was used, which consists of 15 classes, including 12 diseases classes and 3 healthy classes. The data augmentation techniques have been used. In addition to dropout and weight regularization, which leads to good classification results by preventing the model from over fitting. The model was optimized with the Adam optimization technique. The obtained results in terms of performance were 98.08% in the testing stage and 99.24% in the training stage. Next, the baseline model was improved using early stopping, and the accuracy increased to 98.34% on the testing set and 99.64% on the training set. The substantial success rate makes it a valuable advisory method to detect and identify transparently.
Highlights
A range of experiments and studies demonstrated the effectiveness of image processing and analysis through several techniques and the use of deep learning
EXPERIMENT RESULTS the model results will be discussed and the results obtained from this system through deep learning techniques using the Convolutional Neural Network (CNN) algorithm will be presented
WORK The research proposes a methodology for the identification and classification of diseases by computer facilities and deep learning techniques, with accurate and quick results because of the importance of agriculture in Iraq and the existence of many plant diseases
Summary
Plants are essential elements of life that live on earth and play a significant role in our world People in this world depend mainly on plants for food, either directly or by using it as feed for different animals [1]. A traditional strategy for detecting plant leaves diseases is naked-eye observation techniques, and it is not effective, especially for an enormous harvest. Using the technique of digital image processing and deep learning, disease plant detection becomes more efficient, accurate, and less effort and time consuming [14]. In this context, a range of experiments and studies demonstrated the effectiveness of image processing and analysis through several techniques and the use of deep learning.
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