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<jats:sec><jats:title>Introduction</jats:title><jats:p>Yunnan Xiaomila is a pepper variety whose flowers and fruits become mature at the same time and multiple times a year. The distinction between the fruits and the background is low and the background is complex. The targets are small and difficult to identify.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>This paper aims at the problem of target detection of Yunnan Xiaomila under complex background environment, in order to reduce the impact caused by the small color gradient changes between xiaomila and background and the unclear feature information, an improved PAE-YOLO model is proposed, which combines the EMA attention mechanism and DCNv3 deformable convolution is integrated into the YOLOv8 model, which improves the model’s feature extraction capability and inference speed for Xiaomila in complex environments, and achieves a lightweight model. First, the EMA attention mechanism is combined with the C2f module in the YOLOv8 network. The C2f module can well extract local features from the input image, and the EMA attention mechanism can control the global relationship. The two complement each other, thereby enhancing the model’s expression ability; Meanwhile, in the backbone network and head network, the DCNv3 convolution module is introduced, which can adaptively adjust the sampling position according to the input feature map, contributing to stronger feature capture capabilities for targets of different scales and a lightweight network. It also uses a depth camera to estimate the posture of Xiaomila, while analyzing and optimizing different occlusion situations. The effectiveness of the proposed method was verified through ablation experiments, model comparison experiments and attitude estimation experiments.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>The experimental results indicated that the model obtained an average mean accuracy (mAP) of 88.8%, which was 1.3% higher than that of the original model. Its F1 score reached 83.2, and the GFLOPs and model sizes were 7.6G and 5.7MB respectively. The F1 score ranked the best among several networks, with the model weight and gigabit floating-point operations per second (GFLOPs) being the smallest, which are 6.2% and 8.1% lower than the original model. The loss value was the lowest during training, and the convergence speed was the fastest. Meanwhile, the attitude estimation results of 102 targets showed that the orientation was correctly estimated exceed 85% of the cases, and the average error angle was 15.91°. In the occlusion condition, 86.3% of the attitude estimation error angles were less than 40°, and the average error angle was 23.19°.</jats:p></jats:sec><jats:sec><jats:title>Discussion</jats:title><jats:p>The results show that the improved detection model can accurately identify Xiaomila targets fruits, has higher model accuracy, less computational complexity, and can better estimate the target posture.</jats:p></jats:sec>

<jats:sec><jats:title>Introduction</jats:title><jats:p>Fingered citron slices possess significant nutritional value and economic advantages as herbal products that are experiencing increasing demand. The grading of fingered citron slices plays a crucial role in the marketing strategy to maximize profits. However, due to the limited adoption of standardization practices and the decentralized structure of producers and distributors, the grading process of fingered citron slices requires substantial manpower and lead to a reduction in profitability. In order to provide authoritative, rapid and accurate grading standards for the market of fingered citron slices, this paper proposes a grading detection model for fingered citron slices based on improved YOLOv8n.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>Firstly, we obtained the raw materials of fingered citron slices from a dealer of Sichuan fingered citron origin in Shimian County, Ya'an City, Sichuan Province, China. Subsequently, high-resolution fingered citron slices images were taken using an experimental bench, and the dataset for grading detection of fingered citron slices was formed after manual screening and labelling. Based on this dataset, we chose YOLOv8n as the base model, and then replaced the YOLOv8n backbone structure with the Fasternet main module to improve the computational efficiency in the feature extraction process. Then we redesigned the PAN-FPN structure used in the original model with BiFPN structure to make full use of the high-resolution features to extend the sensory field of the model while balancing the computation amount and model volume, and finally we get the improved target detection algorithm YOLOv8-FCS.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>The findings from the experiments indicated that this approach surpassed the conventional RT-DETR, Faster R-CNN, SSD300 and YOLOv8n models in most evaluation indicators. The experimental results show that the grading accuracy of the YOLOv8-FCS model reaches 98.1%, and the model size is only 6.4 M, and the FPS is 130.3.</jats:p></jats:sec><jats:sec><jats:title>Discussion</jats:title><jats:p>The results suggest that our model offers both rapid and precise grading for fingered citron slices, holding significant practical value for promoting the advancement of automated grading systems tailored to fingered citron slices.</jats:p></jats:sec>

<jats:p>Global warming has adversely affected <jats:italic>Picea abies</jats:italic> (L.) H. Karst. forests in Europe, prompting the need for innovative forest-breeding strategies. Somatic embryogenesis (SE) offers promise but requires protocol refinement. Understanding the molecular mechanisms governing somatic embryo development is essential, as oxidative stress plays a crucial role in SE regulation. Ascorbic acid (ASA), is a vital antioxidant that can potentially control oxidative stress. In the present study, we normalized ASA concentrations in induction and proliferation media to enhance embryogenic tissue (ET) regeneration and proliferation capacity of mature explants. The media were supplemented with ASA at 0 mg l<jats:sup>−1</jats:sup>, 25 mg l<jats:sup>−1</jats:sup>, 50 mg l<jats:sup>−1</jats:sup>, 100 mg l<jats:sup>−1</jats:sup>, and 200 mg l<jats:sup>−1</jats:sup>. The accumulation of hydrogen peroxide (H<jats:sub>2</jats:sub>O<jats:sub>2</jats:sub>) and endogenous phytohormones, including auxins, cytokinins, brassinosteroids, abscisic acid, and gibberellin, was measured in non-embryonic calli and ET. Subsequently, their impact on ET induction and multiplication was analyzed. Our results demonstrate that application of ASA at concentrations of 25 mg l<jats:sup>−1</jats:sup> and 200 mg l<jats:sup>−1</jats:sup> led to increased H<jats:sub>2</jats:sub>O<jats:sub>2</jats:sub> levels, potentially inducing oxidative stress while simultaneously reducing the levels of all endohormone groups. Notably, the highest ET induction frequency (approximately 70%) was observed for ASA at 50 mg l<jats:sup>−1</jats:sup>. These findings will enhance SE induction procedures, particularly in more resistant explants, underscoring the significance of ASA application to culture media.</jats:p>

<jats:p>Enlarging the lateral space of drip tubes saves irrigation equipment costs (drip tubes and bypass), but it will lead to an increased risk of grain yield heterogeneity between wheat rows. Adjusting wheat row spacing is an effective cultivation measure to regulate a row’s yield heterogeneity. During a 2-year field experiment, we investigated the variations in yield traits and photosynthetic physiology by utilizing two different water- and fertilizer-demanding spring wheat cultivars (NS22 and NS44) under four kinds of drip irrigation patterns with different drip tube lateral spacing and wheat row spacing [① TR4, drip tube spacing (DTS) was 60 cm, wheat row horizontal spacing (WRHS) was 15 cm; ② TR6, DTS was 90 cm, WRHS was 15 cm; ③ TR6L, DTS was 90 cm, WRHS was 10 cm, inter-block spacing (IBS) was 35 cm; and ④ TR6S, DTS was 80 cm, WRHS was 10 cm, IBS was 25 cm]. The results showed that under 15-cm equal row spacing condition, after the number of wheat rows served by a single tube increased from four (TR4, control) to six (TR6), NS22 and NS44 exhibited a marked decline in yield. The decline of NS22 (9.93%) was higher than that of NS44 (9.04%), and both cultivars also showed a greater decrease in grain weight and average grain-filling rate (AGFR) of inferior grains (NS22: 23.19%, 13.97%; NS44: 7.78%, 5.86%) than the superior grains (NS22: 10.60%, 8.33%; NS44: 4.89%, 4.62%). After the TR6 was processed to narrow WRHS (from 15 to 10 cm) and add IBS (TR6L: 35 cm; TR6S: 25 cm), the grain weight per panicle (GWP) and AGFR of superior and inferior grains in the third wheat row (RW3) of NS22 and NS44 under TR6L increased significantly by 26.05%, 8.22%, 14.05%, 10.50%, 5.09%, and 5.01%, respectively, and under TR6S, they significantly increased by 20.78%, 9.91%, 16.19%, 9.28%, 5.01%, and 4.14%, respectively. The increase in GWP and AGFR was related to the increase in flag leaf area, net photosynthetic rate, chlorophyll content, relative water content, actual photochemical efficiency of PSII, and photochemical quenching coefficient. Among TR4, TR6, TR6L, and TR6S, for both NS22 and NS44, the yield of TR6S was significantly higher than that of TR6 and TR6L. Furthermore, TR6S showed the highest economic benefit.</jats:p>

<jats:p>Canopy height serves as an important dynamic indicator of crop growth in the decision-making process of field management. Compared with other commonly used canopy height measurement techniques, ultrasonic sensors are inexpensive and can be exposed in fields for long periods of time to obtain easy-to-process data. However, the acoustic wave characteristics and crop canopy structure affect the measurement accuracy. To improve the ultrasonic sensor measurement accuracy, a four-year (2018−2021) field experiment was conducted on maize and wheat, and a measurement platform was developed. A series of single-factor experiments were conducted to investigate the significant factors affecting measurements, including the observation angle (0−60°), observation height (0.5−2.5 m), observation period (8:00−18:00), platform moving speed with respect to the crop (0−2.0 m min<jats:sup>−1</jats:sup>), planting density (0.2−1 time of standard planting density), and growth stage (maize from three−leaf to harvest period and wheat from regreening to maturity period). The results indicated that both the observation angle and planting density significantly affected the results of ultrasonic measurements (p-value&amp;lt; 0.05), whereas the effects of other factors on measurement accuracy were negligible (p-value &amp;gt; 0.05). Moreover, a double-input factor calibration model was constructed to assess canopy height under different years by utilizing the normalized difference vegetation index and ultrasonic measurements. The model was developed by employing the least-squares method, and ultrasonic measurement accuracy was significantly improved when integrating the measured value of canopy heights and the normalized difference vegetation index (NDVI). The maize measurement accuracy had a root mean squared error (RMSE) ranging from 81.4 mm to 93.6 mm, while the wheat measurement accuracy had an RMSE from 37.1 mm to 47.2 mm. The research results effectively combine stable and low-cost commercial sensors with ground-based agricultural machinery platforms, enabling efficient and non-destructive acquisition of crop height information.</jats:p>

<jats:sec><jats:title>Introduction</jats:title><jats:p>Manganese (Mn) plays a pivotal role in plant growth and development. Aside aiding in plant growth and development, Mn as heavy metal (HM) can be toxic in soil when applied in excess. <jats:italic>Morus alba</jats:italic> is an economically significant plant, capable of adapting to a range of environmental conditions and possessing the potential for phytoremediation of contaminated soil by HMs. The mechanism by which <jats:italic>M. alba</jats:italic> tolerates Mn stresses remains obscure.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>In this study, Mn concentrations comprising sufficiency (0.15 mM), higher regimes (1.5 mM and 3 mM), and deficiency (0 mM and 0.03 mM), were applied to <jats:italic>M. alba</jats:italic> in pot treatment for 21 days to understand <jats:italic>M. alba</jats:italic> Mn tolerance. Mn stress effects on the net photosynthetic rate (Pn), stomatal conductance (Gs), transpiration rate (Tr), intercellular CO<jats:sub>2</jats:sub> concentration (Ci), chlorophyll content, plant morphological traits, enzymatic and non-enzymatic parameters were analyzed as well as metabolome signatures via non-targeted LC-MS technique.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Mn deficiency and toxicity decrease plant biomass, Pn, Ci, Gs, Tr, and chlorophyll content. Mn stresses induced a decline in the activities of catalase (CAT) and superoxide dismutase (SOD), while peroxidase (POD) activity, and leaf Mn content, increased. Soluble sugars, soluble proteins, malondialdehyde (MDA) and proline exhibited an elevation in Mn deficiency and toxicity concentrations. Metabolomic analysis indicates that Mn concentrations induced 1031 differentially expressed metabolites (DEMs), particularly amino acids, lipids, carbohydrates, benzene and derivatives and secondary metabolites. The DEMs are significantly enriched in alpha-linolenic acid metabolism, biosynthesis of unsaturated fatty acids, galactose metabolism, pantothenate and CoA biosynthesis, pentose phosphate pathway, carbon metabolism, etc.</jats:p></jats:sec><jats:sec><jats:title>Discussion and conclusion</jats:title><jats:p>The upregulation of Galactinol, Myo-inositol, Jasmonic acid, L-aspartic acid, Coproporphyrin I, Trigonelline, Pantothenol, and Pantothenate and their significance in the metabolic pathways makes them Mn stress tolerance metabolites in <jats:italic>M. alba</jats:italic>. Our findings reveal the fundamental understanding of DEMs in <jats:italic>M. alba</jats:italic>’s response to Mn nutrition and the metabolic mechanisms involved, which may hold potential significance for the advancement of <jats:italic>M. alba</jats:italic> genetic improvement initiatives and phytoremediation programs.</jats:p></jats:sec>

<jats:sec><jats:title>Introduction</jats:title><jats:p>Indoor agriculture, especially plant factories, becomes essential because of the advantages of cultivating crops yearly to address global food shortages. Plant factories have been growing in scale as commercialized. Developing an on-site system that estimates the fresh weight of crops non-destructively for decision-making on harvest time is necessary to maximize yield and profits. However, a multi-layer growing environment with on-site workers is too confined and crowded to develop a high-performance system.</jats:p><jats:p>This research developed a machine vision-based fresh weight estimation system to monitor crops from the transplant stage to harvest with less physical labor in an on-site industrial plant factory.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>A linear motion guide with a camera rail moving in both the x-axis and y-axis directions was produced and mounted on a cultivating rack with a height under 35 cm to get consistent images of crops from the top view. Raspberry Pi4 controlled its operation to capture images automatically every hour. The fresh weight was manually measured eleven times for four months to use as the ground-truth weight of the models. The attained images were preprocessed and used to develop weight prediction models based on manual and automatic feature extraction.</jats:p></jats:sec><jats:sec><jats:title>Results and discussion</jats:title><jats:p>The performance of models was compared, and the best performance among them was the automatic feature extraction-based model using convolutional neural networks (CNN; ResNet18). The CNN-based model on automatic feature extraction from images performed much better than any other manual feature extraction-based models with 0.95 of the coefficients of determination (R<jats:sup>2</jats:sup>) and 8.06 g of root mean square error (RMSE). However, another multiplayer perceptron model (MLP_2) was more appropriate to be adopted on-site since it showed around nine times faster inference time than CNN with a little less R<jats:sup>2</jats:sup> (0.93). Through this study, field workers in a confined indoor farming environment can measure the fresh weight of crops non-destructively and easily. In addition, it would help to decide when to harvest on the spot.</jats:p></jats:sec>

<jats:p>Citrus fruits are extensively cultivated fruits with high nutritional value. The identification of distinct ripeness stages in citrus fruits plays a crucial role in guiding the planning of harvesting paths for citrus-picking robots and facilitating yield estimations in orchards. However, challenges arise in the identification of citrus fruit ripeness due to the similarity in color between green unripe citrus fruits and tree leaves, leading to an omission in identification. Additionally, the resemblance between partially ripe, orange-green interspersed fruits and fully ripe fruits poses a risk of misidentification, further complicating the identification of citrus fruit ripeness. This study proposed the YOLO-CIT (You Only Look Once-Citrus) model and integrated an innovative R-LBP (Roughness-Local Binary Pattern) method to accurately identify citrus fruits at distinct ripeness stages. The R-LBP algorithm, an extension of the LBP algorithm, enhances the texture features of citrus fruits at distinct ripeness stages by calculating the coefficient of variation in grayscale values of pixels within a certain range in different directions around the target pixel. The C3 model embedded by the CBAM (Convolutional Block Attention Module) replaced the original backbone network of the YOLOv5s model to form the backbone of the YOLO-CIT model. Instead of traditional convolution, Ghostconv is utilized by the neck network of the YOLO-CIT model. The fruit segment of citrus in the original citrus images processed by the R-LBP algorithm is combined with the background segment of the citrus images after grayscale processing to construct synthetic images, which are subsequently added to the training dataset. The experiment showed that the R-LBP algorithm is capable of amplifying the texture features among citrus fruits at distinct ripeness stages. The YOLO-CIT model combined with the R-LBP algorithm has a Precision of 88.13%, a Recall of 93.16%, an F1 score of 90.89, a mAP@0.5 of 85.88%, and 6.1ms of average detection speed for citrus fruit ripeness identification in complex environments. The model demonstrates the capability to accurately and swiftly identify citrus fruits at distinct ripeness stages in real-world environments, effectively guiding the determination of picking targets and path planning for harvesting robots.</jats:p>

<jats:p>Crop breeding entails developing and selecting plant varieties with improved agronomic traits. Modern molecular techniques, such as genome editing, enable more efficient manipulation of plant phenotype by altering the expression of particular regulatory or functional genes. Hence, it is essential to thoroughly comprehend the transcriptional regulatory mechanisms that underpin these traits. In the multi-omics era, a large amount of omics data has been generated for diverse crop species, including genomics, epigenomics, transcriptomics, proteomics, and single-cell omics. The abundant data resources and the emergence of advanced computational tools offer unprecedented opportunities for obtaining a holistic view and profound understanding of the regulatory processes linked to desirable traits. This review focuses on integrated network approaches that utilize multi-omics data to investigate gene expression regulation. Various types of regulatory networks and their inference methods are discussed, focusing on recent advancements in crop plants. The integration of multi-omics data has been proven to be crucial for the construction of high-confidence regulatory networks. With the refinement of these methodologies, they will significantly enhance crop breeding efforts and contribute to global food security.</jats:p>