Comparative assessment of Pest damage identification of coconut plant using damage texture and color analysis

Utpal Barman; Chhandanee Pathak; Nirmal Kumar Mazumder

doi:10.1007/s11042-023-14369-2

Comparative assessment of Pest damage identification of coconut plant using damage texture and color analysis

Multimed Tools Appl. 2023 Jan 25:1-23. doi: 10.1007/s11042-023-14369-2. Online ahead of print.

Authors

Utpal Barman¹, Chhandanee Pathak², Nirmal Kumar Mazumder³

Affiliations

¹ Department of CSE, The Assam Kaziranga University, Jorhat, Assam India.
² Department of CSE, GIMT, Guwahati, Assam India.
³ Department of Plant Pathology, BN College of Agriculture, AAU, Biswanath Chariali, Assam India.

Abstract

Coconut cultivation is a promising agricultural activity. But to keep the coconut plants pest-free, the detection of various pest damage in coconut plants is of utmost importance for the cultivators. The processes that the cultivators use to detect pest damage in coconut plants are conventional methods, experts' views, or some laboratory techniques. But these procedures are not adequate in the detection of coconut damage identification. In this study, 16 different color and texture features are reported for 1265 coconut pest damage images by extracting the color and texture features of the damage images in the color and grey domain after the damage segmentation using the thresholding technique. The Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM) techniques are applied to extract the texture features of the damages and two Artificial Neural Network (ANN) architectures are reported to classify the extracted data features of the damages into 5 different classes such as Eriophyid_Mite, Rhinoceros_Beetle, Red_Palm_Weevil, Rugose_Spiraling_White_fly, and Rugose_in_Mature with an average testing accuracy of almost 100% respectively. To compare the results with the other machine learning techniques, the Support Vector Machine(SVM), Decision Tree (DT), and Naïve Bayes (NB) are also introduced for damage identification where the SVM methods also report almost 100% accuracy on the fuse features of GLCM and GLRLM. The results of the ANN and SVM are compared by finding the confusion matrix, precision, recall, and f-1 score of the ANN model with the DT and NB classifier. The ANN and SVM outperform in all matrices and they can be used as the base model for further study of coconut pest damage identification using deep learning techniques.

Keywords: ANN; Coconut Pest; Decision tree; GLCM; Image segmentation; Naïve Bayes; SVM.

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