Faster-PestNet: A Lightweight Deep Learning Framework for Crop Pest Detection and Classification

Abstract

One of the most significant risks impacting crops is pests, which substantially decrease food production. Further, prompt and precise recognition of pests can help harvesters save damage and enhance the quality of crops by enabling them to take appropriate preventive action. The apparent resemblance between numerous kinds of pests makes examination laborious and takes time. The limitations of physical pest inspection are required to be addressed, and a novel deep-learning approach called the Faster-PestNet is proposed in this work. Descriptively, an improved Faster-RCNN approach is designed using the MobileNet as its base network and tuned on the pest samples to recognize the crop pests of various categories and given the name of Fatser-PestNet. Initially, the MobileNet is employed for extracting a distinctive set of sample attributes, later recognized by the 2-step locator of the improved Faster-RCNN model. We have accomplished a huge experimentation analysis over a complicated data sample named the IP102 and acquired an accuracy of 82.43%. Further, a local crops dataset is also collected and tested on the trained Faster-PestNet approach to show the generalization capacity of the proposed model. We have confirmed through analysis that the presented work can tackle numerous sample distortions like noise, blurring, light variations, and size alterations and can accurately locate the pest along with the associated class label on the leaf of numerous types and sizes. Both visual and stated performance values confirm the effectiveness of our model.

AI-Enabled Crop Management Framework for Pest Detection Using Visual Sensor Data

“…We conducted extensive experiments on a medium-scale dataset, identifying five agricultural pests: ants, grasshoppers, palm weevils, shield bugs, and wasps. Our thorough experimental analysis demonstrates superior performance compared to various Faster-RCNN [ 41 , 42 ] and YOLO model versions [ 43 , 44 ].…”

“…Compared with existing methodologies, our model demonstrates competitive performance. For instance, while Faster-PestNet [ 41 ] achieves an accuracy of 82.43% on the IP102 dataset. Similarly, Pest-YOLO [ 43 ] and PestLite [ 44 ] achieve mean average precision scores of 73.4% and 90.7%, respectively.…”

Multi-Features and Multi-Deep Learning Networks to identify, prevent and control pests in tremendous farm fields combining IoT and pests sound analysis

“…This was done to improve pest detection accuracy. The improved Fatser-PestNet method classifies and identifies agricultural pests [27]. MobileNet architecture extracts sample attributes.…”

An effective segmentation and attention‐based reptile residual capsule auto encoder for pest classification

“…10 Entomologists often classify insects by hand, which is time-consuming, complex, and necessitates extensive knowledge. 11 The use of automatic pest classification has lately expanded due to the elimination of the necessity for costly, continuing monitoring. Experts have also employed a variety of computer-aided classification techniques to address these challenges.…”

“…However, identifying insects is challenging due to their complicated architecture and similarities among different insect species 10 . Entomologists often classify insects by hand, which is time‐consuming, complex, and necessitates extensive knowledge 11 . The use of automatic pest classification has lately expanded due to the elimination of the necessity for costly, continuing monitoring.…”