International Journal of Electronics and Computer Applications

Volume: 1 Issue: 1

  • Open Access
  • Original Article

Pest Classification using Morphological Processing in Deep Learning

Sanjyot Thuse1,2,* , Meena Chavan1

1 Department of Electronics and Communication Engineering, Bharati Vidyapeeth (Deemed To Be University) College of Engineering, Pune, India
2 Department of Electronics and Telecommunication, Progressive Education Society’s Modern College of Engineering, Pune, India

*Corresponding author email: [email protected]
 

Year: 2024, Page: 20-25,

Received: Feb. 15, 2024 Accepted: May 18, 2024 Published: May 22, 2024

Abstract

Agriculture relies heavily on the prompt detection of pests. There are numerous technologies for identifying pests, but almost all of them are susceptible to misclassification due to inadequate lighting, background distractions, a diversity of collection techniques. Thus, pests that are only partially visible or oriented differently. This misclassification could result in a significant yield loss. We presented an architecture that would use skeletonization together with neural networks as classifiers to give excellent classification accuracy under the aforementioned parameters in order to alleviate this problem. The paper compares the performance of CNN. CNN with VGG16 and the proposed system on accuracy metric. The results obtained are on disoriented images. From the findings it is observed that for data augmented dataset CNN gives 80% accuracy, CNN with VGG16 gives 95% accuracy, and CNN with feature extractor, skeletonization achieved good accuracy which is up to 98%. A web app and an android app is also developed to classify the pest which will help the farmers to identify the name of the pest without going into technical details. This framework will surely help farmers in identifying pest names instantly which will later help in identifying the name and quantity of pesticide.

Keywords: Pest Classification using Morphological Processing in Deep Learning

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Cite this article

Thuse S, Chavan M. (2024). Pest Classification using Morphological Processing in Deep Learning. International Journal of Electronics and Computer Applications. 1(1): 20-25.

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