Design of deep recursive CNN model for detecting and classifying peston plant

Kavita Kalambe; Snehal Awachat; Shwetal Raipure

doi:10.53730/ijhs.v6nS1.7540

Authors

Kavita Kalambe
kalambekb@rknec.edu
Department of Computer Science and Engineering, Shri. Ramdeobaba College of engineering and Management, India
Snehal Awachat Department of Computer Science and Engineering, Shri. Ramdeobaba College of engineering and Management, India
Shwetal Raipure Department of Computer Science and Engineering, Shri. Ramdeobaba College of engineering and Management, India

Keywords:

pest on plant detection, Deep Recursive-CNN, ReLU, Max pooling, feature pyramid networks (FPN)

Abstract

Automatic pest on plant detection in early stage is very essential for food quality control in the agriculture industry. However, the visual method to identify pest on every plant by human is a cumbersome process and cannot be well suited in the agriculture field, because it is time consuming, less accurate and labor intensive. Pest on Plant and plant leaf disease are the major factors responsible for reducing the quality and quantity of food production. Detection at the earlier stage of pest growth and its killing would result in reducing its effect on plant and enhance the quality of food production. Various existing ways have been used to identify and classify pest on plant, but issues have not been resolved, and there is still a scope for improvement. This paper proposes a Deep Recursive Convolutional Neural Networks (DR-CNN) to improve the average running time and achieve high accuracy. DR-CNN model is integrating the convolution, ReLU and Max pooling Layer into single unit and call recursively.

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