A robust pest identification system using morphological analysis in neural networks

Abstract

Timely detection of pests play a major role in agriculture. There exist many pest identification systems, but almost all of them suffer from the misclassification due to lighting, background clutter, heterogeneous capturing devices as well as the pest being partially visible or in the different orientation. This misclassification may cause tremendous yield loss. To mitigate this situation, we proposed an architecture to provide high classification accuracy under the aforementioned conditions using morphology and skeletonization along with neural networks as classifiers. We have considered the crop rice as a use case as it is the staple food grain of almost the entire population of India. The amount of pesticides used is highest in rice as compared to all other food grains. This paper offers a robust technique to identify the pests in rice crops. The performance of the proposed architecture is tested with an image dataset, and the experimental results reveal that our proposed approach provides better classification accuracy than the existing pest detection approaches in the literature. Furthermore, the experimental results also provide the performance comparison among the popular classifiers.

Guess the time of implementation of residential construction projects using neural networks ANN

“…Thus, ANN cannot be fully defined based on a single scenario as it has a broader application. Briefly, the artificial neural network can be described as a computational technique used in the acquisition, representation and computational mapping from one multivariate space of data to another provided a set of data representing that mapping [9][10][11][12][13]. This technology imitates the operation of the biological neural networks applying appropriate mathematical ideals such as function, structure and processing information; thus, possess the capability to memorize, learn and recognize generalized rules.…”

“…figure 1 illustrates the neuron-mathematical where the input data (x1, x2..., xm) with the corresponding weight coefficients of (w1, w2..., wm) resemble the dendrites [1]. Similarly, the body of the neutron represents the processing unit of the signals based on the neuron activated [9]. Once there is an activation a signal is then transmitted via the output (axon) to the succeeding in the neuron in the connection.…”

“…The quantity of the neurons in the input layers affects the output of the neural network. The neurons are located in the hidden layer and are responsible for the processing of the data [9]. Moreover, the quantity of neurons affected the degree of freedom in the neural network, that significantly affect the accuracy of the modeled network.…”