Different Transfer Learning Approaches for Insect Pest Classification in Cotton

Abstract

Boll weevil is an important pest that affects cotton crops worldwide, causing significant economic losses. The classification of the boll-weevil population is crucial for developing effective pest management strategies. However, the low availability of data and features makes classification a challenging task. This study aimed to investigate the use of Transfer Learning (TL) techniques to improve the classification of boll weevil populations. Three types of TL techniques, instance-based, feature-based, and parameter-based, were studied to improve the classification performance of the machine learning algorithms. This work used data from two domains, one with few instances and the other with few features, to test the proposed approaches. Also, climate variables (temperature, humidity, and rainfall) were incorporated as features to predict the level of the boll-weevil attack. The most relevant results of this work are that define 1) How to measure and quantify the similarity or relationship between tasks of different domains; 2) How to select, align, or adapt the relevant features, instances, or models from the source task/domain to the target task/domain; 3) How to reuse parameter settings from the source domain; and 4) How to evaluate and validate the performance and robustness of the TL model on the target task/domain. The proposed approach achieved significant improvements in classification over previous results in the metrics of accuracy and F-measure. For example, in the case with few instances reached an accuracy of 90.79%, while in the case with few features reached an accuracy of 96.28%. Thus, the results demonstrate the effectiveness of TL techniques in improving the classification of boll-weevil populations in cotton crops when few data and/or features are available.