Flowering and grain-filling stages are highly sensitive to heat and drought stress exposure, leading to significant loss in crop yields. Therefore, phenotyping to enhance resilience to these abiotic stresses is critical for sustaining genetic gains in crop improvement programs. However, traditional methods for screening traits related to these stresses are slow, laborious, and often expensive. Remote sensing provides opportunities to introduce low-cost, less biased, high-throughput phenotyping methods to capture large genetic diversity to facilitate enhancement of stress resilience in crops. This review focuses on four key physiological traits and processes that are critical in understanding crop responses to drought and heat stress during reproductive and grain-filling periods. Specifically, these traits include: (i) time of day of flowering, to escape these stresses during flowering; (ii) optimizing photosynthetic efficiency; (iii) storage and translocation of water-soluble carbohydrates; and (iv) yield and yield components to provide in-season yield estimates. Moreover, we provide an overview of current advances in remote sensing in capturing these traits, and discuss the limitations with existing technology as well as future direction of research to develop high-throughput phenotyping approaches. In the future, phenotyping these complex traits will require sensor advancement, high-quality imagery combined with machine learning methods, and efforts in transdisciplinary science to foster integration across disciplines.

Background: Rice is a major staple food crop for more than half the world’s population. As the global population is expected to reach 9.7 billion by 2050, increasing the production of high-quality rice is needed to meet the anticipated increased demand. However, global environmental changes, especially increasing temperatures, can affect grain yield and quality. Heat stress is one of the major causes of an increased proportion of chalkiness in rice, which compromises quality and reduces the market value. Researchers have identified 140 quantitative trait loci linked to chalkiness mapped across 12 chromosomes of the rice genome. However, the available genetic information acquired by employing advances in genetics has not been adequately exploited due to a lack of a reliable, rapid and high-throughput phenotyping tool to capture chalkiness. To derive extensive benefit from the genetic progress achieved, tools that facilitate high-throughput phenotyping of rice chalkiness are needed. Results: We use a fully automated approach based on convolutional neural networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM) to detect chalkiness in rice grain images. Specifically, we train a CNN model to distinguish between chalky and non-chalky grains and subsequently use Grad-CAM to identify the area of a grain that is indicative of the chalky class. The area identified by the Grad-CAM approach takes the form of a smooth heatmap that can be used to quantify the degree of chalkiness. Experimental results on both polished and unpolished rice grains using standard instance classification and segmentation metrics have shown that Grad-CAM can accurately identify chalky grains and detect the chalkiness area. Conclusions: We have successfully demonstrated the application of a Grad-CAM based tool to accurately capture high night temperature induced chalkiness in rice. The models trained will be made publicly available. They are easy-to-use, scalable and can be readily incorporated into ongoing rice breeding programs, without rice researchers requiring computer science or machine learning expertise.

Many agronomic traits have been bred into modern wheat varieties, but wheat (Triticum aestivum L.) continues to be vulnerable to heat stress, with high night-time temperature (HNT) stress shown to have large negative impact on yield and quality. Global mean temperature during the day is consistently warming with the minimum night temperature increasing at a much quicker pace. Currently, there is no system or method that allows crop scientists to impose HNT stress at key developmental stages on wheat or crops in general under field conditions, involving diverse genotypes and maintaining a dynamic temperature differential within the tents compared to the outside. Through implementation of a side roll up and a top ventilation system, heaters, and a custom cyber-physical system using a Raspberry Pi, the heat tents were able to consistently maintain an elevated temperature through the night to differentiate heat stress impact on different genotypes. When the tents were placed in their day-time setting they were able to maintain ambient day-time temperature without having to be removed and replaced on the plots. Data averaged from multiple sensors over three consecutive weeks resulted in a consistent but small temperature difference of 0.25 °C within the tents, indicating even distribution of heat. While targeting a temperature differential of 4 °C, the tents were able to maintain an average differential of 3.2 °C consistently throughout the night-time heat stress period, compared to the outside ambient conditions. The impact of HNT stress was confirmed through a statistically significant yield reduction in eleven of the twelve genotypes tested. The average yield under HNT stress was reduced by 20.3% compared to the controls, with the highest reduction being 41.4% and a lowest reduction of 6.9%. Recommendations for fine-tuning the system are provided. This methodology is easily accessible and can be widely utilized due to its flexibility and ease of construction. This system can be modified and improved based on some of the recommendations and has the potential to be used across other crops or plants as it is not reliant on access to any hardwired utilities. The method tested will help the crop community to quantify the impact of HNT stress, identify novel donors that induce tolerance to HNT and help the breeders develop crop varieties that are resilient to changing climate.

A diverse panel of 320 winter wheat ( Triticum aestivum L.) genotypes was evaluated for yield‐related parameters under control and high night‐time temperature (HNT) stress using custom‐designed, movable field‐based heat tents over 2 years. Genome‐wide association mapping identified several significant single nucleotide polymorphisms (SNPs) across environments, with SNPs under HNT conditions, based on absolute trait values and stress tolerance indices, grouped into 19 HNT quantitative trait loci (QTLs). Additionally, significant SNPs identified under both (control and HNT) conditions contributed to seven condition‐independent QTLs. Thirteen haplotype blocks, derived from some of these QTLs, significantly influenced traits, with nine specific to HNT stress and four shared across conditions. Candidate gene mining identified 17 high‐confidence genes involved in the HNT response, including those encoding ATP‐dependent RNA helicase, GCN5‐related N‐acetyltransferase, oxidoreductase FAD/NAD(P)‐binding, RNA recognition motif, and sugar/inositol transporter. Gene‐based haplotype analysis identified four haplotypes with significant differences under HNT conditions and one block with differences under both conditions. For instance, Hap1 ^{TraesCS1A02G305700} exhibited higher biomass and spike number under HNT, Hap1 ^{TraesCS2B02G599800} showed higher biomass under HNT, and Hap2 ^{TraesCS4B02G264300} demonstrated higher biomass under both conditions. This study elucidates the genetic control of traits contributing to HNT stress responses in wheat, supporting the selection of lines with favorable alleles for crosses and enabling marker‐assisted and genomic selections for HNT stress tolerance. The identified candidate genes and haplotypes provide valuable targets for functional validation, breeding strategies, and gene‐editing approaches to enhance HNT resilience in wheat.

This article studies the optimal design approach for a portable nuclear magnetic resonance (NMR) system for use in non-destructive flow measurement applications. The mechanical and electromagnetic design procedures were carried out using the Ansys Maxwell finite-element analysis (FEA) software tool. The proposed procedure considered homogeneity and strength constraints while ensuring the desired functionality of the intended device for a given application. A modified particle swarm optimization (MPSO) algorithm was proposed as a reference design framework for optimization stages. The optimally designed NMR tool was prototyped, and its functionality was validated via several case studies. To assess the functionality of the prototyped device, Larmor frequency for hydrogen atom was captured and compared with theoretical results. Furthermore, the functionality and accuracy of the prototyped NMR tool is compared to the off-the-shelf NMR tool. Results demonstrated the feasibility and accuracy of the prototyped NMR tool constrained by factors, such as being lightweight and compact.