Rice (Oryza sativa L.) is one of the most important cereals, which provides 20% of the world's food energy. However, its productivity is poorly assessed especially in the global South. Here, we provide a first study to perform a deep-learning-based approach for instantaneously estimating rice yield using red-green-blue images. During ripening stage and at harvest, over 22,000 digital images were captured vertically downward over the rice canopy from a distance of 0.8 to 0.9 m at 4,820 harvesting plots having the yield of 0.1 to 16.1 t·ha^-1 across 6 countries in Africa and Japan. A convolutional neural network applied to these data at harvest predicted 68% variation in yield with a relative root mean square error of 0.22. The developed model successfully detected genotypic difference and impact of agronomic interventions on yield in the independent dataset. The model also demonstrated robustness against the images acquired at different shooting angles up to 30° from right angle, diverse light environments, and shooting date during late ripening stage. Even when the resolution of images was reduced (from 0.2 to 3.2 cm·pixel^-1 of ground sampling distance), the model could predict 57% variation in yield, implying that this approach can be scaled by the use of unmanned aerial vehicles. Our work offers low-cost, hands-on, and rapid approach for high-throughput phenotyping and can lead to impact assessment of productivity-enhancing interventions, detection of fields where these are needed to sustainably increase crop production, and yield forecast at several weeks before harvesting.

Under field environments, fluctuating light conditions induce dynamic photosynthesis, which affects carbon gain by crop plants. Elucidating the natural genetic variations among untapped germplasm resources and their underlying mechanisms can provide an effective strategy to improve dynamic photosynthesis and, ultimately, improve crop yields through molecular breeding approaches. In this review, we first overview two processes affecting dynamic photosynthesis, namely (i) biochemical processes associated with CO₂ fixation and photoprotection and (ii) gas diffusion processes from the atmosphere to the chloroplast stroma. Next, we review the intra- and interspecific variations in dynamic photosynthesis in relation to each of these two processes. It is suggested that plant adaptations to different hydrological environments underlie natural genetic variation explained by gas diffusion through stomata. This emphasizes the importance of the coordination of photosynthetic and stomatal dynamics to optimize the balance between carbon gain and water use efficiency under field environments. Finally, we discuss future challenges in improving dynamic photosynthesis by utilizing natural genetic variation. The forward genetic approach supported by high-throughput phenotyping should be introduced to evaluate the effects of genetic and environmental factors and their interactions on the natural variation in dynamic photosynthesis.

In this paper, we discuss change of read range between a UHF passive RFID tag and reader when a tag is close to another tag. The read range is determined by the strength of forward link and reverse link, which are represented by received power at a tag and reader, respectively. Approaching of tags causes electromagnetic interference between antennas of tags and results in change in impedance matching condition between an antenna and IC of a tag. This change in impedance matching condition decreases in read range. Therefore, we measure the change in received power of a tag and reader experimentally so as to identify which link limits read range and to restrict the existence range of IC impedance. In addition, a technique for extending read range when tags are close is proposed.

Photosynthesis occurs mainly in plant leaves and is a fundamental process in the global carbon cycle and in crop production. The exploitation of natural genetic variation in leaf photosynthetic capacity is a promising strategy to meet the increasing demand for crops. The present study reports the newly developed photosynthesis measurement system ‘MIC-100,’ with a higher throughput for measuring instantaneous photosynthetic rate in the field. MIC-100 is established based on the closed system and directly detects the CO2 absorption in the leaf chamber. The reproducibility, accuracy, and measurement throughput of MIC-100 were tested using soybean (Glycine max L. (Merr.)) and rice (Oryza sativa L.) grown under field conditions. In most cases, the coefficient of variance (CV) for repeated-measurements of the same leaf was less than 0.1. The photosynthetic rates measured with the MIC-100 model showed a significant correlation (R2 = 0.93–0.95) with rates measured by a widely used gas-exchange system. The measurement throughput of the MIC-100 is significantly greater than that of conventional open gas-exchange systems under field conditions. Although MIC-100 solely detects the instantaneous photosynthetic rate under a given environment, this study demonstrated that the MIC-100 enables the rough evaluation of leaf photosynthesis within the large-scale plant populations grown in the field.

Above-ground biomass (AGB) is an important indicator of crop productivity. Destructive measurements of AGB incur huge costs, and most non-destructive estimations cannot be applied to diverse cultivars having different canopy architectures. This insufficient access to AGB data has potentially limited improvements in crop productivity. Recently, a deep learning technique called convolutional neural network (CNN) has been applied to estimate crop AGB due to its high capacity for digital image recognition. However, the versatility of the CNN-based AGB estimation for diverse cultivars is still unclear. We established and evaluated a CNN-based estimation method for rice AGB using digital images with 59 diverse cultivars which were mostly in World Rice Core Collection. Across two years at two locations, we took 12,183 images of 59 cultivars with commercial digital cameras and manually obtained their corresponding AGB. The CNN model was established by using 28 cultivars and showed high accuracy (R² = 0.95) to the test dataset. We further evaluated the performance of the CNN model by using 31 cultivars, which were not in the model establishment. The CNN model successfully estimated AGB when the observed AGB was lesser than 924 g m⁻² (R² = 0.87), whereas it underestimated AGB when the observed AGB was greater than 924 g m⁻² (R² = 0.02). This underestimation might be improved by adding training data with a greater AGB in further study. The present study indicates that this CNN-based estimation method is highly versatile and could be a practical tool for monitoring crop AGB in diverse cultivars.

The culmination of conventional yield improving parameters has widened the margin between food demand and crop yield, leaving the potential yield productivity to be bridged by the manipulation of photosynthetic processes in plants. Efficient strategies to assess photosynthetic capacity in crops need to be developed to identify suitable targets that have the potential to improve photosynthetic efficiencies. Here, we assessed the photosynthetic capacity of the Japanese soybean mini core collection (GmJMC) using a newly developed high-throughput photosynthesis measurement system “MIC-100” to analyze physiological mechanisms and genetic architecture underpinning photosynthesis. K-means clustering of light-saturated photosynthesis (A_sat) classified GmJMC accessions into four distinct clusters with Cluster2 comprised of highly photosynthesizing accessions. Genome-wide association analysis based on the variation of A_sat revealed a significant association with a single nucleotide polymorphism (SNP) on chromosome 17. Among the candidate genes related to photosynthesis in the genomic region, variation in expression of a gene encoding G protein alpha subunit 1 (GPA1) showed a strong correlation (r = 0.72, p < 0.01) with that of A_sat. Among GmJMC accessions, GmJMC47 was characterized by the highest A_sat, stomatal conductance (g_s), stomatal density (S_Density), electron transfer rate (ETR), and light use efficiency of photosystem II (Fv’/Fm′) and the lowest non-photochemical quenching [NPQ(t)], indicating that GmJMC47 has greater CO₂ supply and efficient light-harvesting systems. These results provide strong evidence that exploration of plant germplasm is a useful strategy to unlock the potential of resource use efficiencies for photosynthesis.