Performances Evaluation of a Low-Cost Platform for High-Resolution Plant Phenotyping

Image analysis and polyphenol profiling unveil red-flesh apple phenotype complexity

BACKGROUND

The genetic basis of colour development in red-flesh apples (Malus domestica Borkh) has been widely characterised; however, current models do not explain the observed variations in red pigmentation intensity and distribution. Available methods to evaluate the red-flesh trait rely on the estimation of an average overall colour using a discrete class notation index. However, colour variations among red-flesh cultivars are continuous while development of red colour is non-homogeneous and genotype-dependent. A robust estimation of red-flesh colour intensity and distribution is essential to fully capture the diversity among genotypes and provide a basis to enable identification of loci influencing the red-flesh trait.

RESULTS

In this study, we developed a multivariable approach to evaluate the red-flesh trait in apple. This method was implemented to study the phenotypic diversity in a segregating hybrid F1 family (91 genotypes). We developed a Python pipeline based on image and colour analysis to quantitatively dissect the red-flesh pigmentation from RGB (Red Green Blue) images and compared the efficiency of RGB and CIEL*a*b* colour spaces in discriminating genotypes previously classified with a visual notation. Chemical destructive methods, including targeted-metabolite analysis using ultra-high performance liquid chromatography with ultraviolet detection (UPLC-UV), were performed to quantify major phenolic compounds in fruits' flesh, as well as pH and water contents. Multivariate analyses were performed to study covariations of biochemical factors in relation to colour expression in CIEL*a*b* colour space. Our results indicate that anthocyanin, flavonol and flavanol concentrations, as well as pH, are closely related to flesh pigmentation in apple.

CONCLUSTION

Extraction of colour descriptors combined to chemical analyses helped in discriminating genotypes in relation to their flesh colour. These results suggest that the red-flesh trait in apple is a complex trait associated with several biochemical factors.

How to make sense of 3D representations for plant phenotyping: a compendium of processing and analysis techniques

Computer vision technology is moving more and more towards a three-dimensional approach, and plant phenotyping is following this trend. However, despite its potential, the complexity of the analysis of 3D representations has been the main bottleneck hindering the wider deployment of 3D plant phenotyping. In this review we provide an overview of typical steps for the processing and analysis of 3D representations of plants, to offer potential users of 3D phenotyping a first gateway into its application, and to stimulate its further development. We focus on plant phenotyping applications where the goal is to measure characteristics of single plants or crop canopies on a small scale in research settings, as opposed to large scale crop monitoring in the field.

Development of a Quick-Install Rapid Phenotyping System

In recent years, there has been a growing need for accessible High-Throughput Plant Phenotyping (HTPP) platforms that can take measurements of plant traits in open fields. This paper presents a phenotyping system designed to address this issue by combining ultrasonic and multispectral sensing of the crop canopy with other diverse measurements under varying environmental conditions. The system demonstrates a throughput increase by a factor of 50 when compared to a manual setup, allowing for efficient mapping of crop status across a field with crops grown in rows of any spacing. Tests presented in this paper illustrate the type of experimentation that can be performed with the platform, emphasizing the output from each sensor. The system integration, versatility, and ergonomics are the most significant contributions. The presented system can be used for studying plant responses to different treatments and/or stresses under diverse farming practices in virtually any field environment. It was shown that crop height and several vegetation indices, most of them common indicators of plant physiological status, can be easily paired with corresponding environmental conditions to facilitate data analysis at the fine spatial scale.

Current Status of Biodiversity Assessment and Conservation of Wild Olive (Olea europaea L. subsp. europaea var. sylvestris)

Oleaster (Olea europaea L. subsp. europaea var. sylvestris) is the ancestor of cultivated olive (Olea europaea L. subsp. europaea var. europaea) and it is spread through the whole Mediterranean Basin, showing an overlapping distribution with cultivated olive trees. Climate change and new emerging diseases are expected to severely affect the cultivations of olive in the future. Oleaster presents a higher genetic variability compared to the cultivated olive and some wild trees were found adapted to particularly harsh conditions; therefore, the role of oleaster in the future of olive cultivation may be crucial. Despite the great potential, only recently the need to deeply characterize and adequately preserve the wild olive resources drew the attention of researchers. In this review, we summarized the most important morphological and genetic studies performed on oleaster trees collected in different countries of the Mediterranean Basin. Moreover, we reviewed the strategies introduced so far to preserve and manage the oleaster germplasm collections, giving a future perspective on their role in facing the future agricultural challenges posed by climatic changes and new emerging diseases.

How to Choose a Good Marker to Analyze the Olive Germplasm (Olea europaea L.) and Derived Products

The olive tree (Olea europaea L.) is one of the most cultivated crops in the Mediterranean basin. Its economic importance is mainly due to the intense production of table olives and oil. Cultivated varieties are characterized by high morphological and genetic variability and present a large number of synonyms and homonyms. This necessitates the introduction of a rapid and accurate system for varietal identification. In the past, the recognition of olive cultivars was based solely on analysis of the morphological traits, however, these are highly influenced by environmental conditions. Therefore, over the years, several methods based on DNA analysis were developed, allowing a more accurate and reliable varietal identification. This review aims to investigate the evolving history of olive tree characterization approaches, starting from the earlier morphological methods to the latest technologies based on molecular markers, focusing on the main applications of each approach. Furthermore, we discuss the impact of the advent of next generation sequencing and the recent sequencing of the olive genome on the strategies used for the development of new molecular markers.

Optimization of 3D Point Clouds of Oilseed Rape Plants Based on Time-of-Flight Cameras

Three-dimensional (3D) structure is an important morphological trait of plants for describing their growth and biotic/abiotic stress responses. Various methods have been developed for obtaining 3D plant data, but the data quality and equipment costs are the main factors limiting their development. Here, we propose a method to improve the quality of 3D plant data using the time-of-flight (TOF) camera Kinect V2. A K-dimension (k-d) tree was applied to spatial topological relationships for searching points. Background noise points were then removed with a minimum oriented bounding box (MOBB) with a pass-through filter, while outliers and flying pixel points were removed based on viewpoints and surface normals. After being smoothed with the bilateral filter, the 3D plant data were registered and meshed. We adjusted the mesh patches to eliminate layered points. The results showed that the patches were closer. The average distance between the patches was 1.88 × 10-3 m, and the average angle was 17.64°, which were 54.97% and 48.33% of those values before optimization. The proposed method performed better in reducing noise and the local layered-points phenomenon, and it could help to more accurately determine 3D structure parameters from point clouds and mesh models.