Search Results (17)

Strigolactones (SLs) are key hormones regulating branching and tillering in rice, impacting plant architecture and yield. A rapid, sensitive, and environmentally friendly method using dispersive liquid–liquid microextraction based on the solidification of floating organic droplets (DLLME-SFO), coupled with ultra-high-performance liquid chromatography and tandem mass spectrometry (UHPLC-MS/MS), has been developed for the determination of three SLs (strigol, orobanchol, and 5-deoxystrigol). The DLLME-SFO method integrates one-step low-temperature extraction and enrichment. The DLLME-SFO conditions were optimized through a single-factor experimental design. Under the best-tested conditions, the developed method exhibited excellent linearity, with the coefficient of determination (R²) values greater than 0.9993. The recoveries ranged from 83% to 96%, with precision values ranging from 4.5% to 12.4%. The limits of detection (LODs) varied from 0.6 to 1.2 pg/g fresh weight, indicating the high sensitivity of the method. Additionally, a novel assay protocol for the quantification of SLs in rice in response to nitrogen and phosphorus stress conditions was applied. Full article

The massive loss of nitrogen (N) and phosphorus (P) from farmland ditches contributes to non-point source pollution, posing a significant global environmental challenge. Effectively removing these nutrients remains difficult in intensive agricultural systems. To address this, a novel composite ecological ditch system (CEDS) was developed by modifying traditional drainage ditches to integrate a grit chamber, zeolite, and ecological floating beds. Dynamic monitoring of N and P levels in water, plants, and zeolite was conducted to evaluate the system’s nutrient interception performance and mechanisms. The results showed the following: (1) Water quality improved markedly after passing through the CEDS, with nutrient concentrations decreasing progressively along the flow path. The system intercepted 41.0% of N and 31.9% of P, with inorganic N and particulate P as the primary forms of nutrient loss. (2) Zeolite removes N primarily through ion exchange, and P likely through chemical reactions, with maximum capacities of 3.47 g/kg for N and 1.83 g/kg for P. (3) Ecological floating beds with hydroponic cultivation enhanced nutrient uptake by the roots of Canna indica and Iris pseudacorus, with N uptake surpassing P. (4) Nutrient interception efficiency was positively correlated with temperature, ditch inlet concentrations, and rice runoff concentrations, but negatively with precipitation. This study demonstrates the CEDS’s potential for improving farmland water quality and suggests further enhancements in design and management to increase its economic and aesthetic value. Full article

In seedling cultivation of hybrid rice, fast estimation of seedling density is of great significance for classifying seedling cultivation. This research presents an improved YOLOv8 model for estimating seedling density at the needle leaf stage. Firstly, the auxiliary frame technology was used to address the problem of locating the detection area of seedlings. Secondly, the Standard Convolution (SConv) layers in the neck network were replaced by the Group Shuffle Convolution (GSConv) layer to lightweight the model. A dynamic head module was added to the head network to enhance the capability of the model to identify seedlings. The CIoU loss function was replaced by the EIoU loss function, enhancing the convergence speed of the model. The results showed that the improved model achieved an average precision of 96.4%; the parameters and floating-point computations (FLOPs) were 7.2 M and 2.4 G. In contrast with the original model, the parameters and FLOPs were reduced by 0.9 M and 0.6 G, and the average precision was improved by 1.9%. Compared with state-of-the-art models such as YOLOv7 et al., the improved YOLOv8 achieved preferred comprehensive performance. Finally, a fast estimation system for hybrid rice seedling density was developed using a smartphone and the improved YOLOv8. The average inference time for each image was 8.5 ms, and the average relative error of detection was 4.98%. The fast estimation system realized portable real-time detection of seedling density, providing technical support for classifying seedling cultivation of hybrid rice. Full article

Rice is a water-intensive crop, conventionally grown under submerged conditions, with standing water for about 80% of its growth period. There is an urgent need for water-saving technologies to address challenges associated with conventional irrigation techniques for rice. The alternate wetting and drying (AWD) technique is one of these water-saving techniques; however, it requires continuous monitoring of water levels in the field. The implementation of real-time, electronic sensor-based precision irrigation technology may address the problems associated with conventional irrigation systems and AWD leading to high water use efficiency. Therefore, a study was undertaken to develop a suitable sensor-based automated irrigation system to maintain optimal water levels in rice fields. This study conceptualized an electronic sensor-based automated irrigation system for rice cultivated under the AWD technique. In this method, the rice field is initially flooded to a maximum depth of 5 cm. Irrigation is reapplied once the water level reduces to 10 cm below the soil surface. This developed system helps address water scarcity by regulating water levels, preventing excess ponding. It uses magnetic float-based sensors and electronic circuits to detect water levels, converting them into electronic signals transmitted wirelessly via radio frequency (RF) to a controller. The controller has been programmed for different growth stages that need to be set manually during the cropping period. The system is designed primarily for the AWD method but includes an option for continuous ponding (CP), needed during the flowering stage. The maximum water level at full maturity is set at 5 cm above the soil surface, while irrigation with the AWD method begins when the water level falls 10 cm below the soil surface. The developed system was tested during the Kharif season of 2018–19; the irrigation water productivity was 6.15 kg ha⁻¹mm⁻¹ with the automated system, compared to 3.06 kg ha⁻¹mm⁻¹ in the control (continuous ponding). Total water productivity was 4.80 kg ha⁻¹mm⁻¹ for the automated system and 2.63 kg ha⁻¹mm⁻¹ for the control. The automated system achieved 36% more water savings over the control, which used continuous ponding as farmers practice. The developed system supports AWD, a proven water-saving technique in rice cultivation. Full article

To optimize rice yield and enhance quality through targeted field management at each growth stage, rapid and accurate identification of rice growth stages is crucial. This study presents the Mobilenetv3-YOLOv8 rice growth-stage recognition model, designed for high efficiency and accuracy using Unmanned Aerial Vehicle (UAV) imagery. A UAV captured images of rice fields across five distinct growth stages from two altitudes (3 m and 20 m) across two independent field experiments. These images were processed to create training, validation, and test datasets for model development. Mobilenetv3 was introduced to replace the standard YOLOv8 backbone, providing robust small-scale feature extraction through multi-scale feature fusion. Additionally, the Coordinate Attention (CA) mechanism was integrated into YOLOv8’s backbone, outperforming the Convolutional Block Attention Module (CBAM) by enhancing position-sensitive information capture and focusing on crucial pixel areas. Compared to the original YOLOv8, the enhanced Mobilenetv3-YOLOv8 model improved rice growth-stage identification accuracy and reduced the computational load. With an input image size of 400 × 400 pixels and the CA implemented in the second and third backbone layers, the model achieved its best performance, reaching 84.00% mAP and 84.08% recall. The optimized model achieved parameters and Giga Floating Point Operations (GFLOPs) of 6.60M and 0.9, respectively, with precision values for tillering, jointing, booting, heading, and filling stages of 94.88%, 93.36%, 67.85%, 78.31%, and 85.46%, respectively. The experimental results revealed that the optimal Mobilenetv3-YOLOv8 shows excellent performance and has potential for deployment in edge computing devices and practical applications for in-field rice growth-stage recognition in the future. Full article

The Najeoer Pond was created in a rice paddy as a part of a plan to build the National Institute of Ecology. To induce the establishment of various plants, the maximum depth of the pond was 2.0 m, and diverse depths were created with a gentle slope on the pond bed. When introducing vegetation, littoral and emergent vegetation were first introduced to stabilize the space secured for the creation of the pond, whereas the introduction of other vegetation was allowed to develop naturally. In this pond, floating, emergent, wetland, and littoral plants have been established to various degrees, reflecting the water depth and water table. As a result of stand ordination, based on vegetation data obtained from the created Najeoer Pond and a natural lagoon selected as the reference site, the species’ composition resembled that of the reference site. Diversity, based on vegetation type, community, and species, tended to be higher than that of the reference site. The proportion of exotic species increased due to the disturbance that occurred during the pond creation process but continued to decrease as the vegetation introduced during the creation of the pond became established. Considering these results comprehensively, the restorative treatment served to increase both the biological integrity and ecological stability of the pond and, thus, achieved the creation goal from the viewpoint of the pond structure. Full article

As reported by the FAO, in 2022, approximately 735 million people experienced undernourishment, underscoring the critical need for effective strategies to address micronutrient deficiencies. Among these strategies, the mass fortification of staple foods, particularly rice—a dietary staple for half of the global population—has emerged as one of the most effective approaches. Conventional milling processes diminish the nutritional content of rice, necessitating the development of fortification methods to enhance its nutrient profile. This study investigates advanced fortification techniques to improve the nutritional value of rice, focusing on vitamins B1, B2, and B6, with guidelines from the US Institute of Medicine’s Dietary Reference Intakes. The results indicate that implementing ultrasonic treatments and optimal soaking conditions (60 °C for 60 min) significantly enhances the absorption of these vitamins. Effective parameters included a concentration of 1500 ppm for vitamin B1 and higher levels for vitamins B2 and B6, with a rice-to-vitamin solution ratio of 1:4. These conditions yielded an absorbed vitamin B1 content of 1050 mg/kg, bringing the fortified rice closer to meeting recommended intake levels. Given the global average daily consumption of 100 g of rice per person, this research demonstrates the feasibility of fortifying rice to address nutrient deficiencies effectively and contribute to improved dietary health worldwide. Further enhancement of vitamin B2 and B6 levels remains essential for optimal fortification, highlighting the potential of fortified rice as a sustainable solution for improving global nutrition. Full article

The culture of mandarin fish using artificial feed has been gaining increasing attention in China. Ensuring good water quality in the ponds is crucial for successful aquaculture. Recently, the trial of pond-based rice floating beds (PRFBs) in aquaculture ponds has shown promising results. This research assessed the impact of PRFBs on the microbial community structure and overall quality of the aquaculture pond, thereby enhancing our understanding of its functions. The results revealed that the PRFB group exhibited lower levels of NH₄⁺-N, NO₂⁻-N, NO₃⁻-N, TN, TP, and Alk in pond water compared to the control group. The microbial diversity indices in the PRFB group showed a declining trend, while these indices were increasing in the control group. At the phylum level, there was a considerable increase in Proteobacteria abundance in the PRFB group throughout the culture period, suggesting that PRFBs may promote the proliferation of Proteobacteria. In the PRFB group, there was a remarkable decrease in bacterial populations related to carbon, nitrogen, and phosphorus metabolism, including genera Rhodobacter, Rhizorhapis, Dinghuibacter, Candidatus Aquiluna, and Chryseomicrobium as well as the CL500_29_marine_group. Overall, the research findings will provide a basis for the application of aquaculture of mandarin fish fed an artificial diet and rice floating beds. Full article

Colored rice anthocyanins are water-soluble natural pigments that can be used as an active ingredient in healthy food and pharmaceutical products. However, anthocyanin utilization is limited because of its instability. This work produced anthocyanin-rich gel beads from colored rice using a modified ionotropic gelation technique for encapsulation, and their efficacy was studied in vitro in the gastrointestinal tract. In total, 15 colored rice samples of three types (whole grain rice, ground rice, and ground germinated rice) were screened to identify the highest anthocyanin content. The anthocyanin content of the whole grain rice was significantly (p < 0.05) higher than it was in the ground and ground germinated rice. The sample with the highest anthocyanin content (1062.7 µg/g) was the black glutinous rice grain from Phrae, chosen based on its anthocyanin-rich crude extract. A new formula using a modified ionotropic gelation technique was prepared for the inclusion of the extract in gel beads. The results indicated that the incorporation of oil and wax significantly increased the encapsulation efficiency of the gel beads (% EE value of 85.43%) and improved the bioavailability of the active ingredient. Moreover, after simulated digestion, the release of anthocyanin and total phenolic content occurred more than five times. Scanning electron microscopy revealed that the surface of the gel beads was smooth. Furthermore, the presence of polyphenols and polysaccharides in the gel beads was confirmed using FTIR. The oil-wax-incorporated, anthocyanin-rich gel beads could be implemented for antioxidant delivery into the gastrointestinal tract to further improve healthy food and nutraceutical products. Full article

The realization that mobile phones can detect rice diseases and insect pests not only solves the problems of low efficiency and poor accuracy from manually detection and reporting, but it also helps farmers detect and control them in the field in a timely fashion, thereby ensuring the quality of rice grains. This study examined two Improved detection models for the detection of six high-frequency diseases and insect pests. These models were the Improved You Only Look Once (YOLO)v5s and YOLOv7-tiny based on their lightweight object detection networks. The Improved YOLOv5s was introduced with the Ghost module to reduce computation and optimize the model structure, and the Improved YOLOv7-tiny was introduced with the Convolutional Block Attention Module (CBAM) and SIoU to improve model learning ability and accuracy. First, we evaluated and analyzed the detection accuracy and operational efficiency of the models. Then we deployed two proposed methods to a mobile phone. We also designed an application to further verify their practicality for detecting rice diseases and insect pests. The results showed that Improved YOLOv5s achieved the highest F1-Score of 0.931, 0.961 in mean average precision (mAP) (0.5), and 0.648 in mAP (0.5:0.9). It also reduced network parameters, model size, and the floating point operations per second (FLOPs) by 47.5, 45.7, and 48.7%, respectively. Furthermore, it increased the model inference speed by 38.6% compared with the original YOLOv5s model. Improved YOLOv7-tiny outperformed the original YOLOv7-tiny in detection accuracy, which was second only to Improved YOLOv5s. The probability heat maps of the detection results showed that Improved YOLOv5s performed better in detecting large target areas of rice diseases and insect pests, while Improved YOLOv7-tiny was more accurate in small target areas. On the mobile phone platform, the precision and recall of Improved YOLOv5s under FP16 accuracy were 0.925 and 0.939, and the inference speed was 374 ms/frame, which was superior to Improved YOLOv7-tiny. Both of the proposed improved models realized accurate identification of rice diseases and insect pests. Moreover, the constructed mobile phone application based on the improved detection models provided a reference for realizing fast and efficient field diagnoses. Full article

Crop diseases are one of the important factors affecting crop yield and quality and are also an important research target in the field of agriculture. In order to quickly and accurately identify crop diseases, help farmers to control crop diseases in time, and reduce crop losses. Inspired by the application of convolutional neural networks in image identification, we propose a lightweight crop disease image identification model based on attentional feature fusion named DSGIResNet_AFF, which introduces self-built lightweight residual blocks, inverted residuals blocks, and attentional feature fusion modules on the basis of ResNet18. We apply the model to the identification of rice and corn diseases, and the results show the effectiveness of the model on the real dataset. Additionally, the model is compared with other convolutional neural networks (AlexNet, VGG16, ShuffleNetV2, MobileNetV2, MobileNetV3-Small and MobileNetV3-Large), and the experimental results show that the accuracy, sensitivity, F1-score, AUC of the proposed model DSGIResNet_AFF are 98.30%, 98.23%, 98.24%, 99.97%, respectively, which are better than other network models, while the complexity of the model is significantly reduced (compared with the basic model ResNet18, the number of parameters is reduced by 94.10%, and the floating point of operations(FLOPs) is reduced by 86.13%). The network model DSGIResNet_AFF can be applied to mobile devices and become a useful tool for identifying crop diseases. Full article

The supply of animal feed is one of the main concerns of producers in the aquaculture industry, including aspects such as the cost of fish flour and its nutritional balance. The aim of this study was the preparation of a pellet-type fish food using powdered Spirulina sp. cultivated as a protein source supplemented with agro-industrial waste, and its evaluation to comply with the necessary parameters for the elaboration of extruded pellets. Spirulina sp. was cultivated in a photobioreactor at a volume of 50 L, separated by decantation and dried. The proximal characterization was 6.79% ± 0.05 moisture, 6.93% ± 0.01 ash, 66.88% ± 0.33 protein, and 5.50% ± 0.26 fat. Subsequently, flours were prepared using cassava leaves, gliricidia leaves, and rice husks. The results for the cohesion showed that the flours obtained to comply with the necessary parameters for the elaboration of extruded food. The fish feed was prepared in pellet form using the formulation for fattening Tilapia: Spirulina sp. (20%), cassava leaf flour (50%), gliricidia leaf flour (20%) and flour of rice husk (10%). Floatation analysis showed that 60% of the pellets floated for more than 40 min, and 80% retained their shape for 4 h. The results show that the obtained product can be used as fish feed, due to the lowest disintegration, together with its great capacity for water absorption and especially, its greater flotage due to the expansion effect, are physic characteristic determinants so that the fish has more time to consume extruded diets and avoid losses. Full article

Evapotranspirative irrigation is a simple idea in a watering field based on the actual evapotranspiration rate, by operating an automatic floating valve in the inlet without electric power to manage water levels. The current study introduces a model of evapotranspirative irrigation and its application under different water levels. The objectives were (1) to evaluate the performances of evapotranspirative irrigation under various irrigation regimes, and to (2) to observe crop and water productivities of the system of rice intensification (SRI) as affected by different types of irrigation. The experiment was performed during one rice planting season, starting from July to November 2020, with three irrigation regimes, i.e., continuous flooded (CFI), moderate flooded (MFI) and water-saving irrigation (WSI). Good performance of the system was achieved; low root mean square error (RMSE) was indicated between observed water level and the set point in all irrigation regimes. Developing a better drainage system can improve the system. Among the regimes, the WSI regime was most effective in water use. It was able to increase water productivity by up to 14.5% while maintaining the crop yield. In addition, it has the highest water-use efficiency index. The index was 34% and 52% higher than those of the MFI and CFI regimes, respectively. Accordingly, the evapotranspirative irrigation was effective in controlling various water levels, and we recommend the system implemented at the field levels. Full article

Real-time analysis of UAV low-altitude remote sensing images at airborne terminals facilitates the timely monitoring of weeds in the farmland. Aiming at the real-time identification of rice weeds by UAV low-altitude remote sensing, two improved identification models, MobileNetV2-UNet and FFB-BiSeNetV2, were proposed based on the semantic segmentation models U-Net and BiSeNetV2, respectively. The MobileNetV2-UNet model focuses on reducing the amount of calculation of the original model parameters, and the FFB-BiSeNetV2 model focuses on improving the segmentation accuracy of the original model. In this study, we first tested and compared the segmentation accuracy and operating efficiency of the models before and after the improvement on the computer platform, and then transplanted the improved models to the embedded hardware platform Jetson AGX Xavier, and used TensorRT to optimize the model structure to improve the inference speed. Finally, the real-time segmentation effect of the two improved models on rice weeds was further verified through the collected low-altitude remote sensing video data. The results show that on the computer platform, the MobileNetV2-UNet model reduced the amount of network parameters, model size, and floating point calculations by 89.12%, 86.16%, and 92.6%, and the inference speed also increased by 2.77 times, when compared with the U-Net model. The FFB-BiSeNetV2 model improved the segmentation accuracy compared with the BiSeNetV2 model and achieved the highest pixel accuracy and mean Intersection over Union ratio of 93.09% and 80.28%. On the embedded hardware platform, the optimized MobileNetV2-UNet model and FFB-BiSeNetV2 model inferred 45.05 FPS and 40.16 FPS for a single image under the weight accuracy of FP16, respectively, both meeting the performance requirements of real-time identification. The two methods proposed in this study realize the real-time identification of rice weeds under low-altitude remote sensing by UAV, which provide a reference for the subsequent integrated operation of plant protection drones in real-time rice weed identification and precision spraying. Full article

Vietnam plays an important role in bearing global food security. However, Vietnamese rice farmers face several challenges, including pressures to develop sustainable livelihoods while reducing the environmental impacts of their production activities. Various Vietnamese agricultural restructuring policies were promulgated to promote the adoption of environmentally friendly practices to generate high value added for rice farmers, but the farmers are reluctant to adopt them because of perceived lack of demand. Decreasing consumption of rice in Asia and increasing demands in Europe shaped Vietnamese rice exporting policies. New trade agreements, such as the UK–Vietnam Free Trade Agreement, offer new target markets for Vietnamese rice farmers. This research provides empirical evidence related to the preferences of UK consumers for ethical attributes for floating rice imported from Vietnam. Floating rice represents a traditional method of rice cultivation that relies on the natural flooding cycle. Its cultivation uses very few agrochemical inputs and provides several other environmental, economic, and social benefits. In an online survey, the study used a choice experiment that asked 306 UK consumers to report their preferences for one kilo of floating rice with three non-market attributes: reduction in carbon dioxide emissions, allocation of profits to the farmers, and restitution of biodiversity. Overall, study participants favored the attributes of floating rice, but reported utility for only the “fair trade” attribute and for a marginal willingness to pay premiums for profit allocations to farmers. Consumers did not find value in either CO₂ emission reduction or biodiversity improvement. Results from the study provide recommendations to develop agricultural programs, distribution strategies, and informational methods to encourage floating rice consumption in the UK. Full article