Instant estimation of rice yield using ground-based RGB images and its potential applicability to UAV

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 RGB images. During ripening stage and at harvest, over 22,000 digital images were captured vertically downwards over the rice canopy from a distance of 0.8 to 0.9m at 4,820 harvesting plots having the yield of 0.1 to 16.1 t ha-1 across six countries in Africa and Japan. A convolutional neural network (CNN) applied to these data at harvest predicted 68% variation in yield with a relative root mean square error (rRMSE) of 0.22. Even when the resolution of images was reduced (from 0.2 to 3.2cm pixel-1 of ground sampling distance), the model could predict 57% variation in yield, implying that this approach can be scaled by 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

Deep learning enables instant and versatile estimation of rice yield using ground-based RGB images

"AIの目"によるイネ収穫量の簡単・迅速推定. 京都大学プレスリリース. 2023-07-21.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

Preparation and Catalytic Activity of Pd and Bimetallic Pd–Ni Nanowires

This article describes the preparation and catalytic property of Pd and Pd–Ni nanowires with network structure. A soft template with network structure formed by long-chain amidoamine derivative (C18AA) was essential to preparing Pd and Pd–Ni nanowires because of the preparation of only spherical nanoparticles using octadecylamine, which does not form a network structure as a soft template, instead of C18AA. Furthermore, this soft-template method demands a slow reduction rate for the metal ion, the same as the general preparation method for novel metal nanowires. The distinguishing features of the present method is that the nanowires are a few nanometers in diameter and there are no byproducts such as nanoparticles. In addition, the bimetallic Pd–Ni nanowires show very high catalytic activity for the hydrogenation of <i>p</i>-nitrophenol as compared to Pd nanowires, Pd nanoparticles, and Pd–Ni nanoparticles

Changes in the Gut Microbiota are Associated with Hypertension, Hyperlipidemia, and Type 2 Diabetes Mellitus in Japanese Subjects

The human gut microbiota is involved in host health and disease development. Therefore, lifestyle-related diseases such as hypertension (HT), hyperlipidemia (HL), and type 2 diabetes mellitus (T2D) may alter the composition of gut microbiota. Here, we investigated gut microbiota changes related to these diseases and their coexistence. This study involved 239 Japanese subjects, including healthy controls (HC). The fecal microbiota was analyzed through the isolation of bacterial genomic DNA obtained from fecal samples. Although there were no significant differences in the microbial structure between groups, there was a significant difference in the &alpha;-diversity between HC and the patients in whom two diseases coexisted. Moreover, Actinobacteria levels were significantly increased, whereas Bacteroidetes levels were significantly decreased in all disease groups. At the genus level, Bifidobacterium levels were significantly increased in the HL and T2D groups, as were those of Collinsella in all disease groups. In contrast, Alistipes levels were significantly lower in the HL group. Furthermore, metabolic enzyme families were significantly increased in all disease groups. Interestingly, the structure and function of the gut microbiota showed similar profiles in all the studied diseases. In conclusion, several changes in the structure of the gut microbiota are associated with T2D, HT, and HL in Japanese subjects

Deep learning-based estimation of rice yield using RGB image

Crop productivity is poorly assessed globally. Here, we provide a deep learning-based approach for estimating rice yield using RGB images. During ripening stage and at harvest, over 22,000 digital images were captured vertically downwards over the rice canopy from a distance of 0.8 to 0.9 m, and rice yields were obtained in the corresponding area ranging from 0.1 and 16.1 t ha −1 . A convolutional neural network (CNN) applied to these data at harvest predicted 70% variation in rice yield with a relative root mean square error (rRMSE) of 0.22. Images obtained during the ripening stage can also be used to forecast the final rice yield. Our work suggests that this low-cost, hands-on, and rapid approach can provide a breakthrough solution to assess the impact of productivity-enhancing interventions and identify fields where these are needed to sustainably increase crop productio