Development of Enhanced Weed Detection System with Adaptive Thresholding and Support Vector Machine

Saha, Dheeman; Hanson, Austin; Shin, Sung Y.

doi:10.1145/2987386.2987433

Cited by 19 publications

(9 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To execute precision farming, it is necessary for these tools to be able to process and make inferences from collected data, like an expert in the fields. Image processing integrated with machine learning is extensively applied in precision agriculture and has gained wide attention in the field of detecting plant diseases [2][3][4][5][6], weeds [7][8][9][10][11], and pests [12][13][14][15][16]. Ernest et al [2] employed a linear support vector classifier and the k-nearest neighbor algorithm to diagnose diseases from plant images.…”

Section: Introductionmentioning

confidence: 99%

A Smartphone-Based Application for Scale Pest Detection Using Multiple-Object Detection Methods

et al. 2021

View full text Add to dashboard Cite

Taiwan’s economy mainly relies on the export of agricultural products. If even the suspicion of a pest is found in the crop products after they are exported, not only are the agricultural products returned but the whole batch of crops is destroyed, resulting in extreme crop losses. The species of mealybugs, Coccidae, and Diaspididae, which are the primary pests of the scale insect in Taiwan, can not only lead to serious damage to the plants but also severely affect agricultural production. Hence, to recognize the scale pests is an important task in Taiwan’s agricultural field. In this study, we propose an AI-based pest detection system for solving the specific issue of detection of scale pests based on pictures. Deep-learning-based object detection models, such as faster region-based convolutional networks (Faster R-CNNs), single-shot multibox detectors (SSDs), and You Only Look Once v4 (YOLO v4), are employed to detect and localize scale pests in the picture. The experimental results show that YOLO v4 achieved the highest classification accuracy among the algorithms, with 100% in mealybugs, 89% in Coccidae, and 97% in Diaspididae. Meanwhile, the computational performance of YOLO v4 has indicated that it is suitable for real-time application. Moreover, the inference results of the YOLO v4 model further help the end user. A mobile application using the trained scale pest recognition model has been developed to facilitate pest identification in farms, which is helpful in applying appropriate pesticides to reduce crop losses.

show abstract

Section: Introductionmentioning

confidence: 99%

A Smartphone-Based Application for Scale Pest Detection Using Multiple-Object Detection Methods

et al. 2021

View full text Add to dashboard Cite

show abstract

“…According to the external characteristics of weeds, threshold segmentation method based on machine vision realizes image segmentation and weed recognition through using the threshold segmentation model. The recognition rate was about 85% [24] . The weed recognition method based on genetic algorithm can identify weeds quickly and accurately, and reduce the occurrence of local optimal value.…”

Section: Discussionmentioning

confidence: 98%

Farmland Weed Species Identification Based on Computer Vision

Liu

Wang²,

Liu

et al. 2019

Computer and Computing Technologies in Agriculture XI

View full text Add to dashboard Cite

In order to alleviate the difficulties in collecting indexes for the analysis of farmland weed communities, we implemented a computer vision technology-based method for the identification of farmland weeds at the species level. By using the super-green and maximum interclass difference methods to obtain a green vegetation binary image, we were able to separate weeds from cultivated crops through multiple etching and the removal of small areas. A BP (back propagation) neural network was used for weed recognition, and the morphological characteristics of the weeds and each region were selected following etching to construct the input matrix of the recognition model for training and testing the BP network. After experimenting with the computational vision method for the identification of five weed species, we discovered that the recognition accuracy rate reached 96%. The results showed that the computer vision method could quickly and accurately extract a weed community analysis index, thereby providing a reference for the intelligent analysis of weed communities.

show abstract

“…P = Dimensi Data. = Dimensi i dan k. = Dimensi j dan k. Untuk menentukan pusat yang paling sesuai sebagai upaya merepresentasikan posisi dari sebuah kelompok data terhadap kelompok data lainnya dilakukan sebuah proses perulangan [10].…”

Section: = √∑{ − } =1unclassified

Segmentasi K-Means Clustering Pada Citra Menggunakan Ekstrasi Fitur Warna dan Tekstur

Premana¹,

Bhakti²,

Prayogi³

2020

j.ilm.intech

View full text Add to dashboard Cite

Segmentasi citra menjadi landasan utama pada proses analisa dan pengenalan citra digital. Segmentasi membagi citra digital kedalam beberapa wilayah yang unik berdasarkan piksel yang homogeny. Segmentasi citra mengelompokkan piksel yang homogeny berdasarkan beberapa fitur seperti warna, tekstur dan bentuk. Warna mengandung banyak informasi dan manusia dapat melihat berjuta-juta kombinasi dan intensitas warna, dibandingkan dengan ke abu-abuan (greyscale) atau hitam putih (binary). Metode yang diterapkan adalah metode clustering. Fitur citra digital yang akan diekstraksi adalah tekstur dan warna. Untuk tekstur menggunakan filter gabor sedangkan untuk ekstraksi warna menggunakan vector ruang L*a*b. namun filter gabor mempunyai kelemahan yaitu ketika citra yang disegmentasi banyak tekstur makro, sehingga mempengaruhi akurasi dalam segmentasi citra digital. Sebagai pendukung dalam meningkatkan akurasi dalam ekstraksi tekstur makro digunakan metode k-means. Penelitian penggunaan fitur tekstur meningkat menjadi 17,5% dan ekstraksi warna keabu-abuan meningkat 16,24%. Sedangkan fitur filter gabor dapat meningkatkan akurasi segmentasi citra digital 2% pada ekstrksi warna pada ruang warna L*a*b meningkat 0.3%

show abstract

Development of Enhanced Weed Detection System with Adaptive Thresholding and Support Vector Machine

Cited by 19 publications

References 15 publications

A Smartphone-Based Application for Scale Pest Detection Using Multiple-Object Detection Methods

A Smartphone-Based Application for Scale Pest Detection Using Multiple-Object Detection Methods

Farmland Weed Species Identification Based on Computer Vision

Segmentasi K-Means Clustering Pada Citra Menggunakan Ekstrasi Fitur Warna dan Tekstur

Contact Info

Product

Resources

About