Computer Vision and Deep Learning-enabled Weed Detection Model for Precision Agriculture

Abstract

Presently, precision agriculture processes like plant disease, crop yield prediction, species recognition, weed detection, and irrigation can be accomplished by the use of computer vision (CV) approaches. Weed plays a vital role in influencing crop productivity. The wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are increased. Since the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity, this study presents a novel computer vision and deep learning based weed detection and classification (CVDL-WDC) model for precision agriculture. The proposed CVDL-WDC technique intends to properly discriminate the plants as well as weeds. The proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine (ELM) based weed classification. The parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization (FFO) algorithm. A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced outcomes over its recent approaches interms of several measures.

Real-Time Detection of Crops with Dense Planting Using Deep Learning at Seedling Stage

“…Zou et al [23] combined images with and without weeds to generate new weed images, and trained a semantic segmentation network called UNet, obtaining an accuracy of 92.21%. Punithavathi et al [24] proposed a detection model based on Faster RCNN for crop and weed detection and used the extreme learning machine algorithm to optimize the hyperparameters of the deep learning model to obtain a higher detection accuracy. Chen et al [25] detected weeds in sesame fields based on the YOLOv4 detection network and used local attention pooling to replace maximum pooling in spatial pyramid pooling and SEnet modules to replace logical modules in local attention pooling.…”

Contrastive Clustering for Unsupervised Recognition of Interference Signals

“…Deep learning has achieved excellent performance in natural language processing [1,2] and computer vision [3][4][5]. Therefore, many recognition algorithms based on deep learning for interference signals were proposed to solve the problems of traditional interference signal recognition algorithms whose accuracy is low and significantly affected by artificial feature selection [6][7][8][9][10][11][12].…”

Trajectory Tracking Control Method for Flexible Robot of Construction Machinery Based on Computer Vision

“…As an intelligent mechanical equipment, robot has been developing for a long time, but it is still in the primary stage due to many factors such as late start, immature technology and limited level in the research of industrial robots in China. However, with the emergence and popularization of computer vision and human interactive platform systems and the continuous enrichment and improvement of relevant theoretical achievements [1][2]. More and more important topics with high-precision motion control algorithms and methods developed in the field of computer vision to solve practical problems have become one of the new trends in recent years, among which the most typical is the research on the application of object-oriented programming technology in robots [3][4].…”