Deep neural network based Rider-Cuckoo Search Algorithm for plant disease detection

Agriculture is the main source of wealth, and its contribution is essential to humans. How
ever, several obstacles faced by the farmers are due to different kinds of plant diseases.
The determination and anticipation of plant diseases are the major concerns and should be
considered for maximizing productivity. This paper proposes an effective image process
ing method for plant disease identification. In this research, the input image is subjected to
the pre-processing phase for removing the noise and artifacts present in the image. After
obtaining the pre-processed image, it is subjected to the segmentation phase for obtaining
the segments using piecewise fuzzy C-means clustering (piFCM). Each segment under
goes a feature extraction phase in which the texture features are extracted, which involves
information gain, histogram of oriented gradients (HOG), and entropy. The obtained tex
ture features are subjected to the classification phase, which uses the deep belief network
(DBN). Here, the proposed Rider-CSA is employed for training the DBN. The proposed
Rider-CSA is designed by integrating the rider optimization algorithm (ROA) and Cuckoo
Search (CS). The experimental results proved that the proposed Rider-CSA-DBN outper
formed other existing methods with maximal accuracy of 0.877, sensitivity of 0.862, and
the specificity of 0.877, respectively.