A Review on Sensing Technologies for High-Throughput Plant Phenotyping

Ma, Zhenyu; Rayhana, Rakiba; Feng, Ke; Liu, Zheng; Xiao, Gaozhi; Ruan, Yuefeng; Sangha, Jatinder Singh

doi:10.1109/ojim.2022.3178468

Cited by 13 publications

(3 citation statements)

References 119 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In field studies, plant communities demonstrate variability within the population while in a controlled environment this variability is reduced because plants were studied as single specimens for observing individual changes. With the rise of ground cover fraction (GCF) analysis from visible spectrum image sensors, remote sensing has gained a technological edge as a phenotyping tool with the potential to save time and money [1]. Remote sensing plant populations is advantageous because data can be collected non-invasively over large areas with less time and less labor inputs than in traditional methods [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Canopeo app as image-based phenotyping tool in controlled environment utilizing Arabidopsis mutants

Hale,

Yuan,

Mendu

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

Canopeo app was developed as a simple, accurate, rapid, and free tool to analyze ground cover fraction (GCF) from red-green-blue (RGB) images and videos captured in the field. With increasing interest in tools for plant phenotyping in controlled environments, the usefulness of Canopeo to identify differences in growth among Arabidopsis thaliana mutants in a controlled environment were explored. A simple imaging system was used to compare Arabidopsis mutants based on the FLAVIN-BINDING, KELCH REPEAT, F-BOX-1 (FKF1) mutation, which has been identified with increased biomass accumulation. Two FKF1 lines such as null expression (fkf1-t) and overexpression (FKF1-OE) lines were used along with wild type (Col-0). Canopeo was used to phenotype plants, based on biomass estimations. Under long-day photoperiod, fkf1-t had increased cellulose biosynthesis, and therefore biomass. Resource partitioning favored seedling vigor and delayed onset of senescence. In contrast, FKF1-OE illustrated a determinative growth habit where plant resources are primarily allocated for seed production. This study demonstrates the use of Canopeo for model plants and highlights its potential for phenotyping broadleaved crops in controlled environments. The value of adapting Canopeo for lab use is those with limited experience and resources have access to phenotyping methodology that is simple, accessible, accurate, and cost-efficient in a controlled environment setting.

show abstract

Section: Introductionmentioning

confidence: 99%

Canopeo app as image-based phenotyping tool in controlled environment utilizing Arabidopsis mutants

Hale,

Yuan,

Mendu

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…In a review by Ma et al (2022), they explored the current progress in high-throughput plant phenotyping for crop monitoring, covering sensors, communication protocols, data management, and platforms. Their analysis highlighted the need to address a gap in achieving a communication protocol that is fast, wide-ranging, energy-efficient, and cost-effective for high-quality crop monitoring systems.…”

Section: Introductionmentioning

confidence: 99%

Scalable agritech growbox architecture

Kirwan,

Abbas,

Atmosukarto

et al. 2023

Front. Internet Things

View full text Add to dashboard Cite

Introduction: Urban farming has gained prominence in Singapore, offering opportunities for automation to enhance its efficiency and scalability. This study, conducted in collaboration with a leading Singaporean urban farming company, introduces an IoT-based automated farming system. This system incorporates an agnostic growbox and a web application dashboard for intelligent monitoring of crop growth. The presented approach provides an open-source and cost-effective solution for a scalable urban farming architecture. The agnostic growbox system offers both accessibility and scalability, utilizing cost-effective and modular hardware components with open-source software, thereby increasing customizability and accessibility compared to commercial growbox products. The authors anticipate that this approach will find diverse applications within the realm of urban farming, streamlining, and improving the efficiency of urban farming through automation.Methods: The study employs an integrated solution that incorporates an image analytics approach for the proficient and accurate classification of crop disease phenotypes, specifically targeting chlorosis and tip burn in lettuce crops. This approach is designed to be hardware- and software-efficient, obviating the necessity for extensive image datasets for model training. The image analytics approach is compared favourably with a machine learning approach, evaluating the accuracy of categorization using the same dataset. Additionally, the approach is assessed in terms of time and cost efficiency in comparison to machine learning techniques.Results: The image analytics approach demonstrated notable scalability, time efficiency, and accuracy in the detection of crop diseases within urban farming. Early detection, particularly of chlorosis and tip burn, proves critical in mitigating crop wastage. The results indicate that the integrated solution provided a reliable and effective means of disease classification, with significant advantages over traditional machine learning approaches in terms of time and cost efficiency.Discussion: The presented IoT-based automated farming system, incorporating the agnostic growbox and image analytics approach, holds promise for revolutionizing urban farming practices. Its open-source nature, coupled with cost-effectiveness and scalability, positions it as a practical solution for urban farming architecture. The system's ability to efficiently detect and classify crop diseases, particularly chlorosis and tip burn, offers a substantial contribution to reducing wastage and enhancing crop yield. Overall, this approach paves the way for a more efficient and sustainable future for urban farming through the integration of automation and advanced analytics. Further exploration and implementation of this technology in diverse urban farming settings is warranted.

show abstract

“…teknologi penginderaan, GIS, IoT, robotika, dan teknik drone, sangat penting untuk pemantauan tanaman skala besar dan akuisisi data8. Teknologi canggih ini membantu meningkatkan produksi pertanian sekaligus mengurangi biaya tenaga kerja dan meminimalkan dampak lingkungan (Ma et al, 2022).…”

unclassified

Pemanfaatan Teknologi Informasi dalam Optimalisasi Produksi Tanaman Pangan: Studi Bibliometrik Skala Nasional

Swasono,

Dianta Mustofa K.,

Muthmainah

2023

JMWS

View full text Add to dashboard Cite

Integrasi teknologi informasi (TI) ke dalam produksi tanaman telah muncul sebagai pendekatan transformatif untuk meningkatkan produktivitas pertanian, efisiensi sumber daya dan keberlanjutan. Analisis bibliometrik ini mengeksplorasi lanskap penelitian yang berkembang di bidang ini, dengan fokus pada pemanfaatan TI untuk mengoptimalkan produksi tanaman. Analisis ini mencakup tren publikasi, penulis berpengaruh, jaringan penulisan bersama, pola kutipan, dan tema yang muncul. Hasilnya menunjukkan peningkatan yang stabil dalam aktivitas penelitian, yang menyoroti meningkatnya pengakuan potensi TI dalam mengatasi tantangan ketahanan pangan global. Penulis terkemuka dan kolaborasi interdisipliner menggarisbawahi sifat multifaset lapangan. Makalah utama menjelaskan pentingnya konsep seperti Internet of Things, manajemen rantai pasokan, dan pembangunan berkelanjutan. Munculnya kata kunci seperti "pertanian presisi", "data besar" dan "ekonomi sirkular" menandakan eksplorasi pendekatan inovatif untuk meningkatkan praktik pengelolaan tanaman. Analisis ini menawarkan wawasan bagi para peneliti, pembuat kebijakan, dan praktisi. Temuan ini menjelaskan arah penelitian utama dan area di mana TI dapat mendorong dampak positif, mulai dari optimalisasi sumber daya hingga pengurangan limbah. Selain itu, penelitian ini mengidentifikasi kesenjangan dan tren yang muncul, memandu pengembangan strategi untuk memanfaatkan potensi TI dalam optimalisasi produksi tanaman.

show abstract

A Review on Sensing Technologies for High-Throughput Plant Phenotyping

Cited by 13 publications

References 119 publications

Canopeo app as image-based phenotyping tool in controlled environment utilizing Arabidopsis mutants

Canopeo app as image-based phenotyping tool in controlled environment utilizing Arabidopsis mutants

Scalable agritech growbox architecture

Pemanfaatan Teknologi Informasi dalam Optimalisasi Produksi Tanaman Pangan: Studi Bibliometrik Skala Nasional

Contact Info

Product

Resources

About