Lentil plant disease and quality assessment: A detailed dataset of high-resolution images for deep learning research

被引:0
作者
Mahamud, Eram [1 ]
Assaduzzaman, Md [1 ]
Sharmin, Shayla [1 ]
机构
[1] Daffodil Int Univ, Birulia, Bangladesh
关键词
Agricultural research; Computer vision; Deep learning; Lentil diseases; Sustainable agriculture;
D O I
10.1016/j.dib.2024.111224
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The Lentil, a vital legume globally cultivated, faces significant challenges from diseases like ascochyta blight, lentil rust, and powdery mildew. Ensuring optimal harvest timing and effectively discerning healthy and diseased lentil plants are crucial for maintaining crop quality and economic viability, particularly in regions such as Bangladesh. This paper introduces a comprehensive dataset comprising high-resolution images of lentil plants gathered meticulously over four months from diverse locations across Bangladesh, under expert supervision. The dataset aims to support the development of machine- learning models for precise disease detection and quality assessment in lentil cultivation. Potential applications include enhancing the accuracy of quality evaluation, and improving packaging processes, thereby enhancing overall lentil production efficiency. Agricultural researchers can utilize this dataset to advance applications of computer vision and deep learning in managing crop diseases and enhancing yield outcomes. The dataset's creation involved collaboration with domain experts to ensure its relevance and reliability for agricultural research. By leveraging this dataset, researchers can explore innovative approaches to tackle challenges in lentil farming, contributing to sustainable agricultural practices and food security. Moreover, the dataset serves as a valuable resource for training and testing machine learning algorithms tailored to agricultural settings, facilitating advancements in automated agricultural technologies. Ultimately, this initiative aims to empower stakeholders in the lentil industry with tools to mitigate disease impact and optimize production practices, paving the way for more resilient and efficient agricultural systems globally (c) 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/)
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页数:15
相关论文
共 22 条
[1]   A comprehensive cotton leaf disease dataset for enhanced detection and classification [J].
Bishshash, Prayma ;
Nirob, Asraful Sharker ;
Shikder, Habibur ;
Sarower, Afjal Hossan ;
Bhuiyan, Touhid ;
Noori, Sheak Rashed Haider .
DATA IN BRIEF, 2024, 57
[2]   Dynamic visual servo control methods for continuous operation of a fruit harvesting robot working throughout an orchard [J].
Chen, Mingyou ;
Chen, Zengxing ;
Luo, Lufeng ;
Tang, Yunchao ;
Cheng, Jiabing ;
Wei, Huiling ;
Wang, Jinhai .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2024, 219
[3]  
Cutolo F., 2017, tologie-Abhandlungen, P1, DOI [10.1007/978-3-031-23161-278, DOI 10.1007/978-3-319-08234-9_78-1, 10.1007/978-3-319-08234-9_78-1]
[4]   Plant disease detection and classification techniques: a comparative study of the performances [J].
Demilie, Wubetu Barud .
JOURNAL OF BIG DATA, 2024, 11 (01)
[5]   A biological classification of Parkinson's disease: the SynNeurGe research diagnostic criteria [J].
Hoeglinger, Guenter U. ;
Adler, Charles H. ;
Berg, Daniela ;
Klein, Christine ;
Outeiro, Tiago F. ;
Poewe, Werner ;
Postuma, Ronald ;
Stoessl, A. Jon ;
Lang, Anthony E. .
LANCET NEUROLOGY, 2024, 23 (02) :191-204
[6]   Plant leaf species identification using LBHPG feature extraction and machine learning classifier technique [J].
Jadhav, Sachin B. ;
Patil, Sanjay B. .
SOFT COMPUTING, 2024, 28 (06) :5609-5623
[7]   A comprehensive dragon fruit image dataset for detecting the maturity and quality grading of dragon fruit [J].
Khatun, Tania ;
Nirob, Md. Asraful Sharker ;
Bishshash, Prayma ;
Akter, Morium ;
Uddin, Mohammad Shorif .
DATA IN BRIEF, 2024, 52
[8]   An extensive real-world in field tomato image dataset involving maturity classification and recognition of fresh and defect tomatoes [J].
Khatun, Tania ;
Razzak, Abdur ;
Islam, Md. Shofiul ;
Uddin, Mohammad Shorif .
DATA IN BRIEF, 2023, 51
[9]   Enhancing Ischemic Brain Stroke Detection on CT Images: A Investigation of Transfer Learning Techniques of DenseNet-201 for Neuroimaging Analysis [J].
Kulathilake, Chathura D. ;
Udupihille, Jeevani ;
Senoo, Atsushi .
2024 IEEE CONFERENCE ON ARTIFICIAL INTELLIGENCE, CAI 2024, 2024, :504-509
[10]   A lightweight improved YOLOv5s model and its deployment for detecting pitaya fruits in daytime and nighttime light-supplement environments [J].
Li, Hongwei ;
Gu, Zenan ;
He, Deqiang ;
Wang, Xicheng ;
Huang, Junduan ;
Mo, Yongmei ;
Li, Peiwei ;
Huang, Zhihao ;
Wu, Fengyun .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2024, 220