Plant roots are crucial for nitrogen uptake. To efficiently acquire N, root system architecture (RSA), which includes the length and quantity of primary roots, lateral roots and root hairs, is dynamically regulated by the surrounding N status. For crops, an ideotype RSA characterized by enhanced plasticity to meet various N demands under fluctuating N conditions is fundamental for high N utilization and subsequent yield. Therefore, exploring the genetic basis of N-dependent RSA, especially in crops, is of great significance. This review summarizes how plants sense both local and systemic N signals and transduce them to downstream pathways. Additionally, it presents the current understanding of genetic basis of N-dependent root plasticity in Arabidopsis and major crops. Also, to fully understand the mechanisms underlying N-dependent root morphogenesis and effectively identify loci associated with an ideotype RSA in crops, more attention should be paid to non-destructive, in situ phenotyping of root traits, cell-type-specific exploration of gene functions, and crosstalk between root architecture, environment and management in the future.

In response to the demand of automatic fruit identification and harvesting, this paper presents a human-robot collaborative picking robot based on somatosensory interactive servo control. The robot system mainly consisted of four parts: picking execution mechanism, hand information acquisition system, human-machine interaction interface, and human-robot collaborative picking strategy. A six-degree-of-freedom robotic arm was designed as the picking execution mechanism. The D-H method is employed for both forward and inverse kinematic modeling of the robotic arm. A four-step inverse kinematic optimal solution selection method, including mechanical interference, correctness, rationality, and smoothness of motion, is proposed. The working principle and use of the Leap Motion controller for hand information acquisition were introduced. Data from three types of hand movements were collected and analyzed. Spatial mapping method between the Leap Motion interaction space and operating range of the robotic arm was proposed to achieve a direct correspondence between the cubic interaction box and the cylindrical space of the fan ring of the robotic arm. The test results demonstrated that the average response time of the double-click picking command was 332 ms. The average time consumption for somatosensory control targeting was 6.5 s. The accuracy rate of the picking gesture judgment was 96.7%.

With the development of smart agriculture, accurately identifying crop diseases through visual recognition techniques instead of by eye has been a significant challenge. This study focused on apple leaf disease, which is closely related to the final yield of apples. A multiscale fusion dense network combined with an efficient multiscale attention (EMA) mechanism called Incept_EMA_DenseNet was developed to better identify eight complex apple leaf disease images. Incept_EMA_DenseNet consists of three crucial parts: the inception module, which substituted the convolution layer with multiscale fusion methods in the shallow feature extraction layer; the EMA mechanism, which is used for obtaining appropriate weights of different dense blocks; and the improved DenseNet based on DenseNet_121. Specifically, to find appropriate multiscale fusion methods, the residual module and inception module were compared to determine the performance of each technique, and Incept_EMA_DenseNet achieved an accuracy of 95.38%. Second, this work used three attention mechanisms, and the efficient multiscale attention mechanism obtained the best performance. Third, the convolution layers and bottlenecks were modified without performance degradation, reducing half of the computational load compared with the original models. Incept_EMA_DenseNet, as proposed in this paper, has an accuracy of 96.76%, being 2.93%, 3.44%, and 4.16% better than Resnet50, DenseNet_121 and GoogLeNet, respectively, proved to be reliable and beneficial, and can effectively and conveniently assist apple growers with leaf disease identification in the field.

Interplant communication is of vital importance for plant performance in natural environments. Mycorrhizal fungi have emerged as key contributors to the below ground communication between plants. These mutualistic fungi form connections between the roots of plants via their hyphae, known as common mycorrhizal networks (CMNs). These hyphal networks are thought to be important ways for the exchange of signals between plants. This paper reviews the evidence for CMN-based transfer of semiochemicals between plants upon exposure to pathogen infection, herbivory or mechanical damage. Potential transport routes are explored, asking whether the fungi can actively contribute to the distribution of such signals within the network and discussing potential drivers for signal exchange. It is concluded that identification of the signals that are exchanged remains an important challenge for the future.

The achievement of global food security faces exceptional challenges due to the rapid population growth, land degradation and climate change. Current farming practices, including mineral fertilizers and synthetic pesticides, alone are becoming insufficient to ensure long-term food security and ecosystem sustainability. The lack of robustness and reliability of conventional approaches warrants efforts to develop novel alternative strategies. Bio-based management strategies offer promising alternatives for improving soil health and food productivity. For example, microbial inoculants can enhance nutrient availability, crop production and stress resistance while also remediating contaminated soils. Nanobiotechnology is a promising strategy that has great potential for mitigating biotic and abiotic stresses on plant toward sustainable agriculture. Biochar (including modified biochar) serves as an effective microbial carrier, improving nutrient availability and plant growth. Also, biochar amendments have been demonstrated to have great potential facilitating soil organic carbon sequestration and mitigating greenhouse gas emissions and therefore contribute to climate change mitigation efforts. This review examines the integration of microbial inoculants, nano-fertilizers and biochar, which demonstrates as a promising strategy to enhance soil health, crop productivity and environmental sustainability. However, overcoming challenges related to their mass production, application and potential risks remains crucial. Future research should focus on optimizing these bio-amendment strategies, evaluating their economic viability and developing robust regulatory frameworks to ensure safe and effective agricultural implementation.

Adequate dietary zinc intake remains a public health challenge in China. Also, there is a lack of information on the relationship between Zn intake and food consumption patterns across provinces and over time. In this study, data from the China Health and Nutrition Survey 2004–2011 (21,266 individuals) was used to explore associations between dietary Zn intake and sociodemographic factors. Zn intake per person declined from 11.1 mg·d⁻¹ in 2004 to 9.89 mg·d⁻¹ in 2011, with reduction in cereal consumption the greatest contributor to this. However, the reduction resulting from the lower cereal consumption was only partly compensated by an increase in consumption of Zn-rich foods. The percentage of the study population with inadequate Zn intake increased from 23% in 2004 to 37% in 2011. While Zn intake was positively associated with income levels in each survey year, the time trend for all income levels was a gradually reducing Zn intake. In all years, males had an average higher dietary Zn intake, whereas no significant difference was found between living areas. In conclusion, this study shows that dietary Zn inadequacy was high and has increased over the studied period. Remediation could be sought by shifting dietary patterns toward more Zn-dense food or by enhancing Zn concentration through biofortification.

● Water erosion models mainly applied in central China at large scales.

Soil erosion models are effective tools for assessing soil erosion indicators and simulating erosion processes. China has some of the most severe soil erosion in the world. To better apply soil erosion models to address soil erosion issues, it is necessary to understand the development process and current status of soil erosion modeling research in China. In this study, a combination of bibliometric analysis and statistical methods was used to review and organize Chinese soil erosion models (1982–2022) from various perspectives, including keywords, model operations, model classification, model spatiotemporal scales, and model geographical applications. This findings of this analysis indicate that the study of soil erosion models in China mainly focuses on large scales (regional and large river basins) using empirical models including USLE, RUSLE, and CSLE. The research areas are primarily concentrated in southeastern and central China. The research content has gradually shifted from studying soil erosion characteristics to analyzing influencing factors, spatiotemporal evolution of erosion, and erosion process and morphology stages. However, there are several issues in current Chinese soil erosion modeling research. These include a lack of validation of model application results with field measurements, limited application areas for the models, and relatively weak research on erosion process mechanisms. On this basis, it is recommended that future research should increase the observation of soil erosion processes and establish methods for data or mathematical formula conversion based on different geographical environments. Also, there is a need to strengthen research on erosion process mechanisms. The findings of this study should provide a valuable resource for researchers to future understand the development process and current issues of Chinese soil erosion models, providing insights for future research directions.

● Drying is a crucial postharvest process for paddy grain and impacts the quality of both paddy and rice.

In the context of food security, drying is a crucial postharvest process for paddy grain because it significantly impacts the quality of both paddy and rice. To conserve energy during the drying process, deep bed dryers are used as convective dryers that use a combination of ambient airflow and heating, thus relying on airflow, temperature, and relative humidity (RH) as the primary drying parameters. Consequently, an aeration system is necessary so that the drying air can penetrate the thick pile of paddy grain and distribute evenly throughout the drying chamber. This analysis aimed to determine the most optimal aeration system by using computational fluid dynamics (CFD) and the AHP-TOPSIS method. The quantitative and visual analysis of the airflow velocity, pressure, temperature, and RH was conducted using CFD on four different dryer aeration systems models, which were then ranked by preference value using the AHP-TOPSIS method. Model 4, with a sloping floor and circular pipe formation, was found to have the most optimal aeration system (preference value of 0.788) for a paddy grain deep bed dryer prototype.

● A new vegetation index, rice blast index (RBI), was constructed to detect rice leaf blast.

Leaf blast is a significant global problem, severely affecting rice quality and yield, making swift, non-invasive detection crucial for effective field management. This study used hyperspectral remote sensing technology via an unmanned aerial vehicle to gather spectral data from rice crops. ANOVA and the Relief-F algorithm were used to identify spectral bands sensitive to the disease and developed a new vegetation index, the rice blast index (RBI). This RBI was compared with 30 established vegetation indexes, using correlation analysis and visual comparison to further shortlist six superior indexes, including RBI. These were evaluated using the K-nearest neighbor (KNN) and random forests (RF) classification models. RBI demonstrated superior detection accuracy for leaf blast in both the KNN model (95.0% overall accuracy and 93.8% kappa coefficient) and the RF model (95.1% overall accuracy and 92.5% kappa coefficient). This study highlights the significant potential of RBI as an effective tool for precise leaf blast detection, offering a powerful new mechanism and theoretical basis for enhanced disease management in rice cultivation.

● Plants can respond to heterogeneously distributed nutrient resources by enhancing root foraging capacity.

Localized fertilization strategies (banding fertilizers) developed to minimize nutrient fixation by soil are used widely in intensive agricultural production. Localized fertilization encourages root foraging for heterogeneously distributed soil nutrients. This review focuses on the advances in root growth and nutrient acquisition of heterogeneously distributed soil resources. It is proposed that the incremental amplification of root foraging for nutrients induced by localized fertilization: (1) increased absorption area due to altered root morphology, (2) enhanced mobilization capacity underpinned by enhanced root physiological processes, and (3) intensified belowground interactions due to selective stimulation of soil microorganisms. The increase in root proliferation and the nutrient mobilization capacity as well as microbiome changes caused by localized fertilization can be amplified stepwise to synergistically enhance root foraging capacity, nutrient use efficiency and improve crop productivity. Engineering the roots/rhizosphere through localized, tailored nutrient application to stimulate nature-based root foraging for heterogeneously distributed soil nutrients, and scaling up of the root foraging capacity and nutrient acquisition efficiency from the rhizosphere to the field offers a potential pathway for green and sustainable intensification of agriculture.