Pest Detection and Classification in Peanut Crops Using CNN, MFO, and EViTA Algorithms

Abstract

The growth of vision transformer (ViT) methods have been quite enormous since its features provide efficient outcome in image classification, and identification. Inspired of this beneficial, this paper propose an Enhanced vision transformer architecture (EViTA) model for pest identification, segmentation, and classification. The as of late found that, compare to machine learning, Convolutional neural network algorithms the ViT has providing trusted results on image classification. Motivated by this, in this paper, we concentrate on the best way to learn dual barch segment representations in ViT models for image arrangement. Based upon its features, here propose a double layer transformer encoder to integrate pest image segments of various sizes of pest images to create more grounded image highlights. The current study uses, three pest datasets that affects peanut crops such as Aphids (IP102 Dataset), Wireworm (IP102 Dataset), and Gram Caterpillar collected from public available repository. Our methodology processes small segment and huge segment of tokens with two separate parts of various computational intricacy also, these tokens are then combined simply by consideration numerous times to complete one another. The taken datasets' are preprocessed utilizing the characteristic by using moth flame optimization (MFO), and flatten the images by using linear projector methodology to enhance the missing quality in pest images, and afterward normalization methods are executed to switch it over completely in to mathematical arrangement. This processed information is standardized further utilizing the self attention in StandardScaler procedures are carried out for choosing the ideal highlights in the dataset accordingly having huge effect towards affecting pest image predictions. These ideal highlights are at last taken care of into the EViTA model and the outcomes created are considered in contrast to the cutting edge models which at last legitimize the predominance of the proposed EViTA+PCA+MFO model in pest image prediction with high accuracy rate. Broad trials show that our methodology performs better compared to or on standard with a few simultaneous deals with vision transformer, notwithstanding productive CNN models.INDEX TERMS Pest, peanut, moth flame optimization, CNN, vision transformer.

Enhanced Tomato Pest Detection via Leaf Imagery with a New Loss Function

“…Research on deep learning in pest detection can be categorized into two approaches: pest detection and classification and pest-induced leaf infestation feature detection. The research on pest detection and classification mainly focuses on improving the classical deep learning methods, such as P. Venk et al [10], which achieved good results on pest datasets of three peanut crops by integrating VIT, PCA, and MFO; Pattnaik G et al [11], which feature extraction of pests by HOG and LBP, and the extracted feature maps are fed into SVM [12] classifiers for training; In contrast, leaf damage caused by insect pests can be detected in two ways: by quantifying the extent of insect damage to the leaf and by detecting the location of the insect-damaged leaf. For example, Liang et al [13] developed polynomial and logistic regression models for leaf extraction to estimate leaf damage; Da Silva et al [14] used image segmentation to preserve the leaf region, augmented the dataset with a synthesis technique, and trained the network with a model for detecting pest-induced damage to leaves; Fang et al [15], Zhu R et al [16], Zhu L et al [17], and others used the improved YOLO series of models to identify pest-induced leaf damage and achieved good detection results.…”