Sensing Technologies for Precision Phenotyping in Vegetable Crops: Current Status and Future Challenges

Tripodi, Pasquale; Massa, Daniele; Venezia, A.; Cardi, Teodoro

doi:10.3390/agronomy8040057

Cited by 75 publications

(48 citation statements)

References 113 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A promising approach for the rapid and periodic assessment of crop N is the use of proximal optical sensors [ 25 ]. They could also be used for frequent assessment where fertigation is used [ 13 , 24 , 26 ]. Proximal optical sensors are a form of remote sensing in which the sensors are positioned either in contact with or close to the crop [ 13 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

Proximal Optical Sensors for Nitrogen Management of Vegetable Crops: A Review

Padilla

Gallardo

Peña-Fleitas

et al. 2018

Sensors

132

130

View full text Add to dashboard Cite

Optimal nitrogen (N) management is essential for profitable vegetable crop production and to minimize N losses to the environment that are a consequence of an excessive N supply. Proximal optical sensors placed in contact with or close to the crop can provide a rapid assessment of a crop N status. Three types of proximal optical sensors (chlorophyll meters, canopy reflectance sensors, and fluorescence-based flavonols meters) for monitoring the crop N status of vegetable crops are reviewed, addressing practical caveats and sampling considerations and evaluating the practical use of these sensors for crop N management. Research over recent decades has shown strong relationships between optical sensor measurements, and different measures of crop N status and of yield of vegetable species. However, the availability of both: (a) Sufficiency values to assess crop N status and (b) algorithms to translate sensor measurements into N fertilizer recommendations are limited for vegetable crops. Optical sensors have potential for N management of vegetable crops. However, research should go beyond merely diagnosing crop N status. Research should now focus on the determination of practical fertilization recommendations. It is envisaged that the increasing environmental and societal pressure on sustainable crop N management will stimulate progress in this area.

show abstract

Section: Introductionmentioning

confidence: 99%

Proximal Optical Sensors for Nitrogen Management of Vegetable Crops: A Review

Padilla

Gallardo

Peña-Fleitas

et al. 2018

Sensors

132

130

View full text Add to dashboard Cite

show abstract

“…Characterization of fruit morphology is a classical approach for varietal identification and creation of varietal groups [27,28] and well suited for analysis of genetic diversity in regard to the effects of breeding and plant genetic resources conservation and utilization [29]. Application of high-throughput tools to characterize whole plant diversity in vegetables is still at the proof of concept stage [30], though they are invariably successful in most field crops [31]. Lack of affordable and accessible high-throughput tools appear to hinder the efficacy of most vegetable breeding programs.…”

Section: Introductionmentioning

confidence: 99%

Tomato Phenotypic Diversity Determined by Combined Approaches of Conventional and High-Throughput Tomato Analyzer Phenotyping

Nankar

Tringovska

Grozeva

et al. 2020

Plants

View full text Add to dashboard Cite

Morphological variation in vegetative and fruit traits is a key determinant in unraveling phenotypic diversity. This study was designed to assess phenotypic diversity in tomatoes and examine intra- and intervarietal groups’ variability using 28 conventional descriptors (CDs) and 47 Tomato Analyzer (TA) descriptors related to plant and fruit morphometry. Comprehensive phenotyping of 150 accessions representing 21 countries discerned noticeable variability for CD vegetative traits and TA quantified fruit features, such as shape, size, and color. Hierarchical cluster analysis divided the accessions into 10 distinct classes based on fruit shape and size. Multivariate analysis was used to assess divergence in variable traits among populations. Eight principal components with an eigenvalue >1 were identified by factor analysis, which contributed 87.5% variation to the total cumulative variance with the first two components contributing 32.0% and 18.1% variance, respectively. The relationship between vegetative and fruit descriptors was explained by respective CD and TA correlation networks. There was a strong positive correlation between fruit shape and size whereas negative correlations were between fruit shape index, internal eccentricity, and proximal end shape. The combined approach of CD and TA phenotyping allowed us to unravel the phenotypic diversity of vegetative and reproductive trait variation evaluated at pre- and post-harvest stages.

show abstract

“…Therefore, a better correlation between plant performance, environmental response, and gene function is possible through advanced phenotyping technologies. Sensing devices in agriculture provide a wide range of applications: from the control of indoor and outdoor cultivation conditions to the understanding of main physiological changes in plants because of external stress [58]. In the last decade, there has been a rapid increase of published research in the field of "sensing technologies and phenotyping of crops", aiming to cover the gap with genomic science (Figure 1).…”

Section: Machine Learning For Plant Phenomics and Smart Agriculturementioning

confidence: 99%

Applications and Trends of Machine Learning in Genomics and Phenomics for Next-Generation Breeding

Esposito¹,

Carputo

Cardi³

et al. 2019

Plants

Self Cite

View full text Add to dashboard Cite

Crops are the major source of food supply and raw materials for the processing industry. A balance between crop production and food consumption is continually threatened by plant diseases and adverse environmental conditions. This leads to serious losses every year and results in food shortages, particularly in developing countries. Presently, cutting-edge technologies for genome sequencing and phenotyping of crops combined with progress in computational sciences are leading a revolution in plant breeding, boosting the identification of the genetic basis of traits at a precision never reached before. In this frame, machine learning (ML) plays a pivotal role in data-mining and analysis, providing relevant information for decision-making towards achieving breeding targets. To this end, we summarize the recent progress in next-generation sequencing and the role of phenotyping technologies in genomics-assisted breeding toward the exploitation of the natural variation and the identification of target genes. We also explore the application of ML in managing big data and predictive models, reporting a case study using microRNAs (miRNAs) to identify genes related to stress conditions.

show abstract

Sensing Technologies for Precision Phenotyping in Vegetable Crops: Current Status and Future Challenges

Cited by 75 publications

References 113 publications

Proximal Optical Sensors for Nitrogen Management of Vegetable Crops: A Review

Proximal Optical Sensors for Nitrogen Management of Vegetable Crops: A Review

Tomato Phenotypic Diversity Determined by Combined Approaches of Conventional and High-Throughput Tomato Analyzer Phenotyping

Applications and Trends of Machine Learning in Genomics and Phenomics for Next-Generation Breeding

Contact Info

Product

Resources

About