Modified Metaheuristics with Transfer Learning Based Insect Pest Classification for Agricultural Crops

Crop insect detection becomes a tedious process for agronomists because a substantial part of the crops is damaged,and due to the pest attacks,the quality is degraded.They are the major reason behind crop quality degradation and diminished crop productivity.Hence,accurate pest detection is essential to guarantee safety and crop quality.Conventional identification of insects necessitates highly trained taxonomists to detect insects precisely based on morphological features.Lately,some progress has been made in agriculture by employing machine learning(ML)to classify and detect pests.This study introduces a Modified Metaheuristics with Transfer Learning based Insect Pest Classification for Agricultural Crops(MMTL-IPCAC)technique.The presented MMTL-IPCAC technique applies contrast limited adaptive histogram equalization(CLAHE)approach for image enhancement.The neural architectural search network(NASNet)model is applied for feature extraction,and a modified grey wolf optimization(MGWO)algorithm is employed for the hyperparameter tuning process,showing the novelty of the work.At last,the extreme gradient boosting(XGBoost)model is utilized to carry out the insect classification procedure.The simulation analysis stated the enhanced performance of the MMTL-IPCAC technique in the insect classification process with maximum accuracy of 98.73%.

Automated garden-insect recognition using improved lightweight convolution network

Automated recognition of insect category,which currently is performed mainly by agriculture experts,is a challenging problem that has received increasing attention in recent years.The goal of the present research is to develop an intelligent mobile-terminal recognition system based on deep neural networks to recognize garden insects in a device that can be conveniently deployed in mobile terminals.State-of-the-art lightweight convolutional neural networks(such as SqueezeNet and ShuffleNet)have the same accuracy as classical convolutional neural networks such as AlexNet but fewer parameters,thereby not only requiring communication across servers during distributed training but also being more feasible to deploy on mobile terminals and other hardware with limited memory.In this research,we connect with the rich details of the low-level network features and the rich semantic information of the high-level network features to construct more rich semantic information feature maps which can effectively improve SqueezeNet model with a small computational cost.In addition,we developed an off-line insect recognition software that can be deployed on the mobile terminal to solve no network and the timedelay problems in the field.Experiments demonstrate that the proposed method is promising for recognition while remaining within a limited computational budget and delivers a much higher recognition accuracy of 91.64%with less training time relative to other classical convolutional neural networks.We have also verified the results that the improved SqueezeNet model has a 2.3%higher than of the original model in the open insect data IP102.