Review: New sensors and data-driven approaches—A path to next generation phenomics

Roitsch, Thomas; Cabrera‐Bosquet, Llorenç; Fournier, Antoine; Ghamkhar, Kioumars; Berni, J.A.J.; Pinto, Francisco; Ober, Eric S.

doi:10.1016/j.plantsci.2019.01.011

Cited by 144 publications

(104 citation statements)

References 93 publications

(109 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Furthermore, this study shows that crop trait monitoring can be done throughout the season, using the same model trained on data from the whole season. Moreover, this research showed that under uncontrolled conditions, relevant biomass and plant height estimations can be made, which is marked as a bottleneck in the review paper of [26]. When using UAV-LiDAR for high-throughput estimation of plant height and biomass [27], time should be invested in creating a detailed and structured flight plan.…”

Section: Discussionmentioning

confidence: 99%

“…This research furthermore showed that there are limitations to the biomass estimation for certain crops and that models developed for a specific crop cannot directly be used for other crops, and generic models should be used with care. For a fully operational approach, an effort should be made towards combining LiDAR with hyperspectral data, as mentioned by [22,23,26], so models can be trained for a range of crops. This will increase both the accuracy and general applicability of high-throughput biomass estimation models.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Biomass and Crop Height Estimation of Different Crops Using UAV-Based Lidar

2019

View full text Add to dashboard Cite

Phenotyping of crops is important due to increasing pressure on food production. Therefore, an accurate estimation of biomass during the growing season can be important to optimize the yield. The potential of data acquisition by UAV-LiDAR to estimate fresh biomass and crop height was investigated for three different crops (potato, sugar beet, and winter wheat) grown in Wageningen (The Netherlands) from June to August 2018. Biomass was estimated using the 3DPI algorithm, while crop height was estimated using the mean height of a variable number of highest points for each m 2 . The 3DPI algorithm proved to estimate biomass well for sugar beet (R 2 = 0.68, RMSE = 17.47 g/m 2 ) and winter wheat (R 2 = 0.82, RMSE = 13.94 g/m 2 ). Also, the height estimates worked well for sugar beet (R 2 = 0.70, RMSE = 7.4 cm) and wheat (R 2 = 0.78, RMSE = 3.4 cm). However, for potato both plant height (R 2 = 0.50, RMSE = 12 cm) and biomass estimation (R 2 = 0.24, RMSE = 22.09 g/m 2 ), it proved to be less reliable due to the complex canopy structure and the ridges on which potatoes are grown. In general, for accurate biomass and crop height estimates using those algorithms, the flight conditions (altitude, speed, location of flight lines) should be comparable to the settings for which the models are calibrated since changing conditions do influence the estimated biomass and crop height strongly. good correlation with in situ field measurements of plant height. Sun et al. [4] published an R 2 of 0.98 for cotton plants, [2] published an R 2 of 0.99 for wheat, and [7] showed an R 2 of 0.90 for wheat. These studies show the capability of LiDAR to measure basic phenotypes such as plant height. However, LiDAR systems mounted on tractors can be unsuitable for labour-intensive crops such as rice or in orchards [8], for example, due to compaction of the soil [9]. A UAV equipped with a LiDAR system can overcome those limitations.Earlier research on the relation between plant height and biomass was based on varying approaches for plant height measurements. Madec et al. [7] found an R 2 of 0.88 for the correlation between plant height and field-measured biomass, using a tractor based LiDAR system. Bendig et al.[10] used a structure from a motion (SfM) technique on UAV acquired imagery to derive plant height and found an R 2 of 0.81 between field-measured height and SfM derived height.In the last few years, LiDAR systems have been miniaturized, resulting in lower weights and reduced dimensions, and as a result, can be operated from UAVs. This development opens the way towards high throughput derived, more complex products like biomass and yield, thus, improving the speed and frequency at which these plant traits can be acquired in the field in an undisturbed way.LiDAR-based biomass estimates of agricultural crops can be derived in different ways. Based on the Lambert-Beer LAI model of [11], the authors of [2] developed a biomass prediction model called the 3-Dimensional Profile Index (3DPI). Where the LIDAR 3D point cloud is divided into layers...

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Biomass and Crop Height Estimation of Different Crops Using UAV-Based Lidar

2019

View full text Add to dashboard Cite

show abstract

“…yeast) can also be applied to microalgae to measure phenotype data from highthroughput algae culture formats (agar plates, microplates, etc.) using standard microbiology sensors such as fluorescence and absorption spectrophotometers (Fernandez-Ricaud et al, 2005;Fernandez-Ricaud et al, 2016), hyperspectral cameras (Roitsch et al, 2019), and flow cytometers (Cagnon et al, 2013). Even morphological phenotypes can be automatically digitized via machine learning (ML) approaches such as image processing with Support Vector Machines (SVNs) and Convoluted Neural Networks (CNNs), as demonstrated in Mohanty et al (2016) and Sladojevic et al (2016).…”

Section: Phenomicsmentioning

confidence: 99%

Emerging Technologies in Algal Biotechnology: Toward the Establishment of a Sustainable, Algae-Based Bioeconomy

et al. 2020

View full text Add to dashboard Cite

Mankind has recognized the value of land plants as renewable sources of food, medicine, and materials for millennia. Throughout human history, agricultural methods were continuously modified and improved to meet the changing needs of civilization. Today, our rapidly growing population requires further innovation to address the practical limitations and serious environmental concerns associated with current industrial and agricultural practices. Microalgae are a diverse group of unicellular photosynthetic organisms that are emerging as next-generation resources with the potential to address urgent industrial and agricultural demands. The extensive biological diversity of algae can be leveraged to produce a wealth of valuable bioproducts, either naturally or via genetic manipulation. Microalgae additionally possess a set of intrinsic advantages, such as low production costs, no requirement for arable land, and the capacity to grow rapidly in both large-scale outdoor systems and scalable, fully contained photobioreactors. Here, we review technical advancements, novel fields of application, and products in the field of algal biotechnology to illustrate how algae could present high-tech, low-cost, and environmentally friendly solutions to many current and future needs of our society. We discuss how emerging technologies such as synthetic biology, highthroughput phenomics, and the application of internet of things (IoT) automation to algal manufacturing technology can advance the understanding of algal biology and, ultimately, drive the establishment of an algal-based bioeconomy.

show abstract

“…One scientific challenge in phenomics, i.e., the systematic study of phenotypes, is to analyze and reconstruct automatically the geometry and topology of thousands of plants in various conditions observed from various sensors [16]. For this purpose, we developped the OpenAlea Phenomenal software package [3].…”

Section: Use Case In Plant Phenotypingmentioning

confidence: 99%

Adaptive Caching for Data-Intensive Scientific Workflows in the Cloud

Heidsieck

Oliveira²,

Pacitti

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

0000−0003−2577−4275] , Daniel de Oliveira 4[0000−0001−9346−7651] , Esther Pacitti 1[0000−0003−1370−9943] , Christophe Pradal 1,2[0000−0002−2555−761X] , François Tardieu 3[0000−0002−7287−0094] , and Patrick Valduriez 1[0000−0001−6506−7538]Abstract. Many scientific experiments are now carried on using scientific workflows, which are becoming more and more data-intensive and complex. We consider the efficient execution of such workflows in the cloud. Since it is common for workflow users to reuse other workflows or data generated by other workflows, a promising approach for efficient workflow execution is to cache intermediate data and exploit it to avoid task re-execution. In this paper, we propose an adaptive caching solution for data-intensive workflows in the cloud. Our solution is based on a new scientific workflow management architecture that automatically manages the storage and reuse of intermediate data and adapts to the variations in task execution times and output data size. We evaluated our solution by implementing it in the OpenAlea system and performing extensive experiments on real data with a data-intensive application in plant phenotyping. The results show that adaptive caching can yield major performance gains, e.g., up to 120.16% with 6 workflow re-executions.

show abstract

Review: New sensors and data-driven approaches—A path to next generation phenomics

Cited by 144 publications

References 93 publications

Biomass and Crop Height Estimation of Different Crops Using UAV-Based Lidar

Biomass and Crop Height Estimation of Different Crops Using UAV-Based Lidar

Emerging Technologies in Algal Biotechnology: Toward the Establishment of a Sustainable, Algae-Based Bioeconomy

Adaptive Caching for Data-Intensive Scientific Workflows in the Cloud

Contact Info

Product

Resources

About