## 4 Discussion  
### Comparison Results Summary  
Automated entomology has been around for decades. Machine learning-based image processing models have been developed for accurate classification and identification of crop pests as traditional manual identification of insects is typically time-consuming, labor-intensive, and inefficient [19]. The classification accuracy mainly depends on input features. For early models such as support vector machine (SVM), neural nets, decision trees, k-nearest neighbor, all extracted features (e.g., shape, geometry, color, texture) often need to be redesigned and handcrafted, hence, image segmentation and feature extraction process needs to be integrated to transform the raw data into a suitable internal representation or feature vector, which increases the computational complexity of models [23; 24; 25; 26; 27; 28; 42; 43]. Further, in many studies [30; 31; 32; 44; 45], the classification accuracy improved by applying deep learning, which performs automatic feature extraction from raw data that reduces the challenges of handcrafting features and can be effective in solving more complex problems. In [PERSON] et al. [29] research, the classification of 16 kinds of common pests was performed by applying a deep learning model. The accuracy of most deep learning models is higher than 80%. In [PERSON] et al. [45], an improved pyramidal stacked de-noising auto-encoder architecture is proposed to build a deep neural network for moth identification with identification accuracy of 96.9%. [PERSON] et al. [32] proposes a multiple-kernel learning method for the classification of 24 common pest species of field crops and produces a high accuracy of approximately 97.20%. [PERSON] et al. [30] propose an efficient deep CNN model to classify insect species on three publicly available insect datasets [31; 32] which achieves an accuracy of 96.75%, 97.47%, and 95.97% on the three datasets, respectively. However, most of these studies take common pests (e.g., phalanenae, beetles, galleriidae) except locust of specific crops or vegetables (e.g., rice, maize, rape, soybean) as research objects. There are relatively few studies that involve the identification of locusts with different instars. According to our results, the identification accuracy of adult AM locust, rice locust, and cotton locust can reach 93.80%, 93.60%, and 97.80%, respectively, which are comparable with the above research results [29; 30; 31; 32; 33; 45]. It is worthy of note that our sample images were directly collected in the field with diverse backgrounds, without employing lab-based settings (e.g., pheromone traps).  
The identification accuracy of third-instar AM locusts is relatively low (77.20%). This is mainly because larvae and adults of AM locust are similar in morphological characteristics. Because recognizing AM locusts at an early stage is important in preventing their spread, the ResNet-Locust-BN shows a significant advantage in the identification of third-instar AM locust compared with AlexNet, GoogLeNet, ResNet 18, ResNet 50 and VggNet. [PERSON] and [PERSON] [42] propose the use of Grab Cut method, principal component analysis to design and extract eight features from 73 optional features of locust and then input the extracted features to SVM models (SVM type: C-support vector classification) to classify _Lecusta migratoria manilensis_ (AM locust) and _Oedaleus decorus asiaticus_ with different instars. From the results they obtained, identification accuracy of fourth-instar and fifth-instar AM locust reached 95.63% and 95.78%, respectively. Nevertheless, complicated image segmentation and feature extraction need to be executed for each input sample, which makes it difficult to achieve fully automatic identification of locusts and instars using this method. Therefore, the ResNet-Locust-BN is more practical for classifying locusts with different species and instars than previously proposed methods.