Autofluorescence-spectral imaging for rapid and invasive characterization of soybean for pre-germination anaerobic stress tolerance.

The autofluorescence-spectral imaging (ASI) technique is based on the light-emitting ability of natural fluorophores. Soybean genotypes showing contrasting tolerance to pre-germination anaerobic stress can be characterized using the photon absorption and fluorescence emission of natural fluorophores occurring in seed coats. In this study, tolerant seeds were efficiently distinguished from susceptible genotypes at 405 nm and 638 nm excitation wavelengths.

Image-based phenotyping of seed architectural traits and prediction of seed weight using machine learning models in soybean.

Among seed attributes, weight is one of the main factors determining the soybean harvest index. Recently, the focus of soybean breeding has shifted to improving seed size and weight for crop optimization in terms of seed and oil yield. With recent technological advancements, there is an increasing application of imaging sensors that provide simple, real-time, non-destructive, and inexpensive image data for rapid image-based prediction of seed traits in plant breeding programs.

Phenomics based prediction of plant biomass and leaf area in wheat using machine learning approaches.

Introduction: Phenomics has emerged as important tool to bridge the genotype-phenotype gap. To dissect complex traits such as highly dynamic plant growth, and quantification of its component traits over a different growth phase of plant will immensely help dissect genetic basis of biomass production. Based on RGB images, models have been developed to predict biomass recently.

Elucidating the role of key physio-biochemical traits and molecular network conferring heat stress tolerance in cucumber.

Cucumber is an important vegetable crop grown worldwide and highly sensitive to prevailing temperature condition. The physiological, biochemical and molecular basis of high temperature stress tolerance is poorly understood in this model vegetable crop. In the present study, a set of genotypes with contrasting response under two different temperature stress (35/30°C and 40/35°C) were evaluated for important physiological and biochemical traits.

Angiotensin-converting enzyme inhibitory peptides and isoflavonoids from soybean [ (L.) Merr.].

Angiotensin-converting enzyme I (ACE I) is a zinc-containing metallopeptidase involved in the renin-angiotensin system (RAAS) that helps in the regulation of hypertension and maintains fluid balance otherwise, which results in cardiovascular diseases (CVDs). One of the leading reasons of global deaths is due to CVDs. RAAS also plays a central role in maintaining homeostasis of the CV system.

Harnessing heterosis and male sterility in soybean [ (L.) Merrill]: A critical revisit.

Soybean is a predominantly self-pollinated crop. It is also one of the important oilseed legumes. Soybean is an excellent crop having industrial, traditional, culinary, feeding, and cultural roles.

Leaf Count Aided Novel Framework for Rice ( L.) Genotypes Discrimination in Phenomics: Leveraging Computer Vision and Deep Learning Applications.

Drought is a detrimental factor to gaining higher yields in rice ( L.), especially amid the rising occurrence of drought across the globe. To combat this situation, it is essential to develop novel drought-resilient varieties.

Drought Tolerant Near Isogenic Lines of Pusa 44 Pyramided With and , Show Accelerated Recovery Response in a High Throughput Phenomics Based Phenotyping.

Reproductive stage drought stress (RSDS) is a major challenge in rice production worldwide. Cultivar development with drought tolerance has been slow due to the lack of precise high throughput phenotyping tools to quantify drought stress-induced effects. Most of the available techniques are based on destructive sampling and do not assess the progress of the plant's response to drought.

Co-cultivation Approach to Decipher the Influence of Nitrogen-Fixing Cyanobacterium on Growth and N Uptake in Rice Crop.

The present study was performed to evaluate the efficacy of selected potential nitrogen-fixing cyanobacterial strain (Anabaena sp.), isolated from rhizospheric soil of rice plants on growth, pigments, N uptake, root architecture, and image-based phenotypic traits of rice crop using co-cultivation approach under controlled sand culture conditions. We studied the beneficial interaction of cyanobacterium to rice using sensor image-based Phenomics approach as well as conventional methods.

SpikeSegNet-a deep learning approach utilizing encoder-decoder network with hourglass for spike segmentation and counting in wheat plant from visual imaging.

Background: High throughput non-destructive phenotyping is emerging as a significant approach for phenotyping germplasm and breeding populations for the identification of superior donors, elite lines, and QTLs. Detection and counting of spikes, the grain bearing organs of wheat, is critical for phenomics of a large set of germplasm and breeding lines in controlled and field conditions. It is also required for precision agriculture where the application of nitrogen, water, and other inputs at this critical stage is necessary.