Seed Yield and Lodging Assessment in Red Fescue (<i>Festuca rubra</i> L.) Sprayed with Trinexapac-Ethyl

Red fescue (Festuca rubra) is used in seed mixtures for lawns and pastures. It is prone to lodge at flowering, and plant growth regulators (PGRs) are used to prevent lodging, ensuring sufficient pollination. Seed yield and lodging were studied over three years in a red fescue field established with four seeding rates (2, 4, 6 and 8 kg ha&minus;1) and sprayed each year with three doses of the PGR trinexapac-ethyl (250 g L&minus;1) (0, 0.3, 0.6 and 1.2 L ha&minus;1). Half of each plot was sprayed with the PGR and the other half was left unsprayed as control. The degree of lodging was assessed by analysing drone images in the second year of the experiment and using a 10-point scale for scoring lodging at the ground. Generally, application of PGR increased the seed yield but the effect varied between years. There was no interaction between the PGR dosage and seeding rate. We found a positive correlation between the blue intensity of the images and lodging. PGR dosage significantly affected lodging evaluated by visual ranking and the blue intensity of the images, while the seeding rates did not affect lodging. Lodging affected seed yield negatively

Automatic late blight lesion recognition and severity quantification based on field imagery of diverse potato genotypes by deep learning

The plant pathogen Phytophthora infestans causes the severe disease late blight in potato, which can result in huge yield loss for potato production. Automatic and accurate disease lesion segmentation enables fast evaluation of disease severity and assessment of disease progress. In tasks requiring computer vision, deep learning has recently gained tremendous success for image classification, object detection and semantic segmentation. To test whether we could extract late blight lesions from unstructured field environments based on high-resolution visual field images and deep learning algorithms, we collected∼500 field RGB images in a set of diverse potato genotypes with different disease severity (0%–70%), resulting in 2100 cropped images. 1600 of these cropped images were used as the dataset for training deep neural networks and 250 cropped images were randomly selected as the validation dataset. Finally, the developed model was tested on the remaining 250 cropped images. The results show that the values for intersection over union (IoU) of the classes background (leaf and soil) and disease lesion in the test dataset were 0.996 and 0.386, respectively. Furthermore, we established a linear relationship (R2=0.655) between manual visual scores of late blight and the number of lesions detected by deep learning at the canopy level. We also showed that imbalance weights of lesion and background classes improved segmentation performance, and that fused masks based on the majority voting of the multiple masks enhanced the correlation with the visual disease scores. This study demonstrates the feasibility of using deep learning algorithms for disease lesion segmentation and severity evaluation based on proximal imagery, which could aid breeding for crop resistance in field environments, and also benefit precision farming

The tmRDB and SRPDB resources

Maintained at the University of Texas Health Science Center at Tyler, Texas, the tmRNA database (tmRDB) is accessible at the URL with mirror sites located at Auburn University, Auburn, Alabama () and the Royal Veterinary and Agricultural University, Denmark (). The signal recognition particle database (SRPDB) at is mirrored at and the University of Goteborg (). The databases assist in investigations of the tmRNP (a ribonucleoprotein complex which liberates stalled bacterial ribosomes) and the SRP (a particle which recognizes signal sequences and directs secretory proteins to cell membranes). The curated tmRNA and SRP RNA alignments consider base pairs supported by comparative sequence analysis. Also shown are alignments of the tmRNA-associated proteins SmpB, ribosomal protein S1, alanyl-tRNA synthetase and Elongation Factor Tu, as well as the SRP proteins SRP9, SRP14, SRP19, SRP21, SRP54 (Ffh), SRP68, SRP72, cpSRP43, Flhf, SRP receptor (alpha) and SRP receptor (beta). All alignments can be easily examined using a new exploratory browser. The databases provide links to high-resolution structures and serve as depositories for structures obtained by molecular modeling

High-throughput phenotyping of physiological traits for wheat resilience to high temperature and drought stress

Interannual and local fluctuations in wheat crop yield are majorly explained by abiotic constraints. Heatwaves and drought, which are among the top stressors, commonly co-occur, and their frequency is increasing with global climate change. High-throughput methods were optimised to phenotype wheat plants under controlled water deficit and high temperature, with the aim to identify phenotypic traits conferring adaptative stress responses. Wheat plants of 10 genotypes were grown in a fully automated plant facility under 25/18ºC day/night for 30 days, and then the temperature was increased for seven days (38/31ºC day/night) while maintaining half of the plants well irrigated and half at 30% field capacity. Thermal and multispectral images and pot weights were registered twice daily. At the end of the experiment, key metabolites and enzyme activities from the carbohydrate and antioxidant metabolisms were quantified. Regression machine learning models were successfully established to predict plant biomass using image-extracted parameters. Evapotranspiration traits expressed significant genotype-environment interactions (GxE) when acclimatization to stress was continuously monitored. Consequently, transpiration efficiency was essential to maintain the balance between water-saving strategies and biomass production in wheat under water deficit and high temperature. Stress tolerance included changes in the carbohydrate metabolism, particularly in the sucrolytic and glycolytic pathways, and in the antioxidant metabolism. The observed genetic differences in sensitivity to high temperature and water deficit can be exploited in breeding programs to improve wheat resilience to climate change. [Abstract copyright: © The Author(s) 2022. Published by Oxford University Press on behalf of the Society for Experimental Biology.

Computer Vision and Less Complex Image Analyses to Monitor Potato Traits in Fields

Field phenotyping of crops has recently gained considerable attention leading to the development of new protocols for recording plant traits of interest. Phenotyping in field conditions can be performed by various cameras, sensors and imaging platforms. In this chapter, practical aspects as well as advantages and disadvantages of above-ground phenotyping platforms are highlighted with a focus on drone-based imaging and relevant image analysis for field conditions. It includes useful planning tips for experimental design as well as protocols, sources, and tools for image acquisition, pre-processing, feature extraction and machine learning highlighting the possibilities with computer vision. Several open and free resources are given to speed up data analysis for biologists. This chapter targets professionals and researchers with limited computational background performing or wishing to perform phenotyping of field crops, especially with a drone-based platform. The advice and methods described focus on potato but can mostly be used for field phenotyping of any crops

An Analysis of Spatial-Spectral Dependence in Hyperspectral Autoencoders

Hyperspectral imaging is central for remote sensing, and much research has been done on analysis methods for land surveillance using space- and air-borne imaging systems. Proximal hyperspectral imaging is also widely used in plant and agriculture science. It allows the remote capturing of leaf reflectance information in order to determine and classify plant health and disease. With the high information density in hyperspectral images, it becomes increasingly important to apply sophisticated feature extraction in order to reduce image dimensionality while retaining useful information. Autoencoders are one of the primary methods for deep learning-based feature extraction in hyperspectral images. We investigate different setups of autoencoders to encode the spatial and spectral dimensions in different orders and ways. To our surprise, the best turns out to be a 3D CNN, where the spectral dimension is treated in the same way as the spatial dimensions.</p

In-field classification of the asymptomatic biotrophic phase of potato late blight based on deep learning and proximal hyperspectral imaging

Effective detection of potato late blight (PLB) is an essential aspect of potato cultivation. However, it is a challenge to detect late blight in asymptomatic biotrophic phase in fields with conventional imaging approaches because of the lack of visual symptoms in the canopy. Hyperspectral imaging can capture spectral signals from a wide range of wavelengths also outside the visual wavelengths. Here, we propose a deep learning classification architecture for hyperspectral images by combining 2D convolutional neural network (2D-CNN) and 3D-CNN with deep cooperative attention networks (PLB-2D-3D-A). First, 2D-CNN and 3D-CNN are used to extract rich spectral space features, and then the attention mechanism AttentionBlock and SE-ResNet are used to emphasize the salient features in the feature maps and increase the generalization ability of the model. The dataset is built with 15,360 images (64x64x204), cropped from 240 raw images captured in an experimental field with over 20 potato genotypes. The accuracy in the test dataset of 2000 images reached 0.739 in the full band and 0.790 in the specific bands (492 nm, 519 nm, 560 nm, 592 nm, 717 nm and 765 nm). This study shows an encouraging result for classification of the asymptomatic biotrophic phase of PLB disease with deep learning and proximal hyperspectral imaging