Potential Phenotyping Methodologies to Assess Inter- and Intravarietal Variability and to Select Grapevine Genotypes Tolerant to Abiotic Stress

On construction of data preprocessing for real-life SoyLeaf dataset & disease identification using Deep Learning Models

The vast volumes of data are needed to train Deep Learning Models from scratch to identify illnesses in soybean leaves. However, there is still a lack of sufficient high-quality samples. To overcome this problem, we have developed the real-life SoyLeaf dataset and used the pre-trained Deep Learning Models to identify leaf diseases. In this paper, we have initially developed the real-life SoyLeaf dataset collected from the ICAR-Indian Institute of Soybean Research (IISR) Center, Indore field. This SoyLeaf dataset contains 9786 high-quality soybean leaf images, including healthy and diseased leaves. Following this, we have adapted data preprocessing techniques to enhance the quality of images. In addition, we have utilized several Deep Learning Models, i.e., fourteen Keras Transfer Learning Models, to determine which model best fits the dataset on SoyLeaf diseases. The accuracies of the proposed fine-tuned models using the Adam optimizer are as follows: ResNet50V2 achieves 99.79%, ResNet101V2 achieves 99.89%, ResNet152V2 achieves 99.59%, InceptionV3 achieves 99.83%, InceptionResNetV2 achieves 99.79%, MobileNet achieves 99.82%, MobileNetV2 achieves 99.89%, DenseNet121 achieves 99.87%, and DenseNet169 achieves 99.87%. Similarly, the accuracies of the proposed fine-tuned models using the RMSprop optimizer are as follows: ResNet50V2 achieves 99.49%, ResNet101V2 achieves 99.45%, ResNet152V2 achieves 99.45%, InceptionV3 achieves 99.58%, InceptionResNetV2 achieves 99.88%, MobileNet achieves 99.73%, MobileNetV2 achieves 99.83%, DenseNet121 achieves 99.89%, and DenseNet169 achieves 99.77%. The experimental results of the proposed fine-tuned models show that only ResNet50V2, ResNet101V2, InceptionV3, InceptionResNetV2, MobileNet, MobileNetV2, DenseNet121, and DenseNet169 have performed better in terms of training, validation, and testing accuracies than other state-of-the-art models.

Physiological and image-based phenotyping assessment of waterlogging responses of three kiwifruit rootstocks and grafting combinations

Introduction

Kiwifruit species have a relatively high rate of root oxygen consumption, making them very vulnerable to low root zone oxygen concentrations resulting from soil waterlogging. Recently, kiwifruit rootstocks have been increasingly used to improve biotic and abiotic stress tolerance and crop performance under adverse conditions. The aim of the present study was to evaluate morpho-physiological changes in kiwifruit rootstocks and grafting combinations under short-term waterlogging stress.

Methods

A pot trial was conducted at the ALSIA PhenoLab, part of the Phen-Italy infrastructures, using non-destructive RGB and NIR image-based analysis and physiological measurements to identify waterlogging stress indicators and more tolerant genotypes. Three pot-grown kiwifruit rootstocks ('Bounty 71,' Actinidia macrosperma-B; 'D1,' Actinidia chinensis var. deliciosa-D; and 'Hayward,' A. chinensis var. deliciosa-H) and grafting combinations, with a yellow-fleshed kiwifruit cultivar ('Zesy 002,' A. chinensis var. chinensis) grafted on each rootstock (Z/B, Z/D, Z/H), were subjected to a control irrigation treatment (WW), restoring their daily water consumption, and to a 9-day waterlogging stress (WL), based on substrate saturation. Leaf gas exchange, photosynthetic activity, leaf temperature, RGB, and NIR data were collected during waterlogging stress.

Results

Stomatal conductance and transpiration reached very low values (less than 0.05 mol m-2 s-1 and 1 mmol m-2 s-1, respectively) in both waterlogged D and H rootstocks and their grafting combinations. In turn, leaf temperature was significantly increased and photosynthesis was reduced (1-6 μmol m-2 s-1) from the first days of waterlogging stress compared to B rootstock and combination.

Discussion

The B rootstock showed prolonged leaf gas exchange and photosynthetic activity, indicating that it can cope with short-term and temporary waterlogging and improve the tolerance of grafted kiwi vines, which showed a decrease in stomatal conductance 5 days after the onset of stress. Morphometric and colorimetric parameters from the image-based analysis confirmed the greater susceptibility of D and H rootstocks and their grafting combinations to waterlogging stress compared to B. The results presented confirm the role of physiological measurements and enhance that of RGB and NIR images in detecting the occurrence of water stress and identifying more tolerant genotypes in kiwifruit.

Adaptation to Climate Change in Viticulture: The Role of Varietal Selection-A Review

Viticulture faces unprecedented challenges due to the rapidly changing climate, particularly in regions like the Mediterranean Basin. Consequently, climate change adaptation strategies are crucial in viticulture, with short-term strategies being widely used despite increasing concerns about their sustainability, and long-term strategies considered promising, though costly. A promising but understudied strategy is varietal selection, as grapevines exhibit vast intervarietal diversity with untapped potential for climate-resilient varieties. By integrating research across plant physiology, biochemistry, histology, and genetics, we can better understand the traits behind the grapevine's capability for adaptation. Several traits, including morphological, physiological, and molecular aspects, have been shown to be crucial in adapting to environmental stresses such as drought and heat. By studying the abundant grapevine intervarietal diversity, the potential for viticulture adaptation to climate change through varietal selection is immense. This review article focuses on the potential of varietal selection in the adaptation of viticulture to climate change. For this, we will delve into the research regarding how climate affects grapevine growth and grape quality and how the grapevine responds to stress conditions, followed by a summary of different climate change adaptation strategies of viticulture. Finally, we will focus on varietal selection, discussing and summarizing different studies surrounding grapevine variety behaviour.

Research Progress of Spectral Imaging Techniques in Plant Phenotype Studies

Spectral imaging technique has been widely applied in plant phenotype analysis to improve plant trait selection and genetic advantages. The latest developments and applications of various optical imaging techniques in plant phenotypes were reviewed, and their advantages and applicability were compared. X-ray computed tomography (X-ray CT) and light detection and ranging (LiDAR) are more suitable for the three-dimensional reconstruction of plant surfaces, tissues, and organs. Chlorophyll fluorescence imaging (ChlF) and thermal imaging (TI) can be used to measure the physiological phenotype characteristics of plants. Specific symptoms caused by nutrient deficiency can be detected by hyperspectral and multispectral imaging, LiDAR, and ChlF. Future plant phenotype research based on spectral imaging can be more closely integrated with plant physiological processes. It can more effectively support the research in related disciplines, such as metabolomics and genomics, and focus on micro-scale activities, such as oxygen transport and intercellular chlorophyll transmission.

LiDAR Is Effective in Characterizing Vine Growth and Detecting Associated Genetic Loci

The strong societal demand to reduce pesticide use and adaptation to climate change challenges the capacities of phenotyping new varieties in the vineyard. High-throughput phenotyping is a way to obtain meaningful and reliable information on hundreds of genotypes in a limited period. We evaluated traits related to growth in 209 genotypes from an interspecific grapevine biparental cross, between IJ119, a local genitor, and Divona, both in summer and in winter, using several methods: fresh pruning wood weight, exposed leaf area calculated from digital images, leaf chlorophyll concentration, and LiDAR-derived apparent volumes. Using high-density genetic information obtained by the genotyping by sequencing technology (GBS), we detected 6 regions of the grapevine genome [quantitative trait loci (QTL)] associated with the variations of the traits in the progeny. The detection of statistically significant QTLs, as well as correlations (R2) with traditional methods above 0.46, shows that LiDAR technology is effective in characterizing the growth features of the grapevine. Heritabilities calculated with LiDAR-derived total canopy and pruning wood volumes were high, above 0.66, and stable between growing seasons. These variables provided genetic models explaining up to 47% of the phenotypic variance, which were better than models obtained with the exposed leaf area estimated from images and the destructive pruning weight measurements. Our results highlight the relevance of LiDAR-derived traits for characterizing genetically induced differences in grapevine growth and open new perspectives for high-throughput phenotyping of grapevines in the vineyard.

Investigating high throughput phenotyping based morpho-physiological and biochemical adaptations of indian pennywort (Centella asiatica L. urban) in response to different irrigation regimes

Indian pennywort (Centella asiatica L. Urban; Apiaceae) is a herbaceous plant used as traditional medicine in several regions worldwide. An adequate supply of fresh water in accordance with crop requirements is an important tool for maintaining the productivity and quality of medicinal plants. The objective of this study was to find a suitable irrigation schedule for improving the morphological and physiological characteristics, and crop productivity of Indian pennywort using high-throughput phenotyping. Four treatments were considered based on irrigation schedules (100, 75, 50, and 25% of field capacity denoted by I100 [control], I75, I50, and I25, respectively). The number of leaves, plant perimeter, plant volume, and shoot dry weight were sustained in I75 irrigated plants, whereas adverse effects on plant growth parameters were observed when plants were subjected to I25 irrigation for 21 days. Leaf temperature (Tleaf) was also retained in I75 irrigated plants, when compared with control. An increase of 2.0 °C temperature was detected in the Tleaf of plants under I25 irrigation treatment when compared with control. The increase in Tleaf was attributed to a decreased transpiration rate (R2 = 0.93), leading to an elevated crop water stress index. Green reflectance and leaf greenness remained unchanged in plants under I75 irrigation, while significantly decreased under I50 and I25 irrigation. These decreases were attributed to declined leaf osmotic potential, increased non-photochemical quenching, and inhibition of net photosynthetic rate (Pn). The asiatic acid and total centellosides in the leaf tissues, and centellosides yield of plants under I75 irrigation were retained when compared with control, while these parameters were regulated to maximal when exposed to I50 irrigation. Based on the results, I75 irrigation treatment was identified as the optimum irrigation schedule for Indian pennywort in terms of sustained biomass and a stable total centellosides. However, further validation in the field trials at multiple locations and involving different crop rotations is recommended to confirm these findings.

The Combination of Both Heat and Water Stresses May Worsen Botryosphaeria Dieback Symptoms in Grapevine

(1) Background: Grapevine trunk diseases (GTDs) have become a global threat to vineyards worldwide. These diseases share three main common features. First, they are caused by multiple pathogenic micro-organisms. Second, these pathogens often maintain a long latent phase, which makes any research in pathology and symptomatology challenging. Third, a consensus is raising to pinpoint combined abiotic stresses as a key factor contributing to disease symptom expression. (2) Methods: We analyzed the impact of combined abiotic stresses in grapevine cuttings artificially infected by two fungi involved in Botryosphaeria dieback (one of the major GTDs), Neofusicoccum parvum and Diplodia seriata. Fungal-infected and control plants were subjected to single or combined abiotic stresses (heat stress, drought stress or both). Disease intensity was monitored thanks to the measurement of necrosis area size. (3) Results and conclusions: Overall, our results suggest that combined stresses might have a stronger impact on disease intensity upon infection by the less virulent pathogen Diplodia seriata. This conclusion is discussed through the impact on plant physiology using metabolomic and transcriptomic analyses of leaves sampled for the different conditions.