Foxtail millet is an important minor cereal crop rich in nutrients. Due to the small size of its seeds, there is little information on the diversity of its seed structure among germplasms, limiting the identification of genes controlling seed development and germination. This paper utilized X-ray computed tomography (CT) scanning technology and deep learning models to reveal the microstructure of foxtail millet seeds, gaining insights into their internal features, distribution, and composition. A total of 100 foxtail millet varieties were scanned with X-ray computed tomography to obtain 3D reconstruction images and slices. Pre-processing steps were adopted to improve image segmentation accuracy, including noise reduction, rotation, contrast enhancement, and brightness enhancement. The experiment revealed that traditional OpenCV image processing methods failed to achieve precise segmentation, whereas deep learning models exhibited outstanding performance in segmenting seed CT slice images. We compared UNet, PSPNet, and DeepLabV3 models, selected different backbones and optimizers based on the dataset, and continuously adjusted learning rates and maximum training epochs to train the models. Results demonstrated that VGG16-UNet achieved an accuracy of 99.19% on the foxtail millet seed CT slice image dataset, outperforming PSPNet and DeepLabV3 models. Compared to ResNet-UNet, VGG16-UNet shows an improvement of approximately 3.18% in accuracy, demonstrating superior performance in accurately segmenting the inner glume, outer glume, embryo, and endosperm under various adhesion conditions. Accurate segmentation of foxtail millet CT images enables analysis of embryo size, endosperm size, and glume thickness, which impact germination, growth, and nutrition. This study fills a gap in small grain structure research, offering insights to optimize agriculture and molecular breeding for improved yield and quality.

Understanding the environmental impacts on root growth and root health is essential for effective agricultural and environmental management. Hyperspectral imaging (HSI) technology provides a non-destructive method for detailed analysis and monitoring of plant tissues and organ development, but unfortunately examples for its application to root systems and the root-soil interface are very scarce. There is also a notable lack of standardized guidelines for image acquisition and data analysis pipelines.

Quantitative disease resistance (QR) is a complex, dynamic trait that is most reliably quantified in field-grown crops. Traditional disease assessments offer limited potential to disentangle the contributions of different components to overall QR at critical crop developmental stages. Yet, a better functional understanding of QR could greatly support a more targeted, knowledge-based selection for QR and improve predictions of seasonal epidemics. Image-based approaches together with advanced image processing methodologies recently emerged as valuable tools to standardize relevant disease assessments, increase measurement throughput, and describe diseases along multiple dimensions.

BerryPortraits (Phenotyping of Ripening Traits) is open source Python-based image-analysis software that rapidly detects and segments berries and extracts morphometric data on fruit quality traits such as berry color, size, shape, and uniformity. Utilizing the YOLOv8 framework and community-developed, actively-maintained Python libraries such as OpenCV, BerryPortraits software was trained on 512 postharvest images (taken under controlled lighting conditions) of phenotypically diverse cranberry populations (Vaccinium macrocarpon Ait.) from the two largest public cranberry breeding programs in the U.S. The implementation of CIELAB, an intuitive and perceptually uniform color space, enables differentiation between berry color and berry brightness, which are confounded in classic RGB color channel measurements. Furthermore, computer vision enables precise and quantifiable color phenotyping, thus facilitating inclusion of researchers and data analysts with color vision deficiency. BerryPortraits is a phenotyping tool for researchers in plant breeding, plant genetics, horticulture, food science, plant physiology, plant pathology, and related fields. BerryPortraits has strong potential applications for other specialty crops such as blueberry, lingonberry, caneberry, grape, and more. As an open source phenotyping tool based on widely-used python libraries, BerryPortraits allows anyone to use, fork, modify, optimize, and embed this software into other tools or pipelines.

As one of the world's most important vegetable crops, eggplant production is often severely affected by verticillium wilt, leading to significant declines in yield and quality. Traditional multispectral disease-imaging equipment is expensive and complicated to operate. Low-cost multispectral devices cannot capture images and cover less information. The traditional approach to early disease diagnosis involves using multispectral disease-imaging equipment in conjunction with machine learning technology. However, this approach has significant limitations in early disease diagnosis, including challenges such as high costs, complex operation, and low model performance.

Peucedanum praeruptorum Dunn has typical stacked umbels and medicinal value; however, the lack of an effective tissue culture system for P. praeruptorum has limited the large-scale propagation of its seedlings.

With the emergence of the new generation vision architecture Vmamba and the further demand for agricultural yield and efficiency, we propose an efficient and high-accuracy target detection network for automated pear picking tasks based on Vmamba, aiming to address the issue of low efficiency in current Transformer architectures. The proposed network, named SRSMamba, employs a Reward and Punishment Mechanism (RPM) to focus on important information while minimizing redundancy interference. It utilizes 3D Selective Scan (SS3D) to extend scanning dimensions and integrates global information across channel dimensions, thereby enhancing the model's robustness in complex agricultural environments and effectively adapting to the extraction of complex features in pear orchards and farmlands. Additionally, a Stacked Feature Pyramid Network (SFPN) is introduced to enhance semantic information during the feature fusion stage, particularly improving the detection capability for small targets. Experimental results show that SRSMamba has a low parameter count of 21.1 M, GFLOPs of 50.4, mAP of 72.0%, mAP50 reaching 94.8%, mAP75 at 68.1%, and FPS at 26.9. Compared with other state-of-the-art (SOTA) object detection methods, it achieves a good trade-off between model efficiency and detection accuracy.

Foxtail millet [Setaria italica (L.) Beauv] is a C graminoid crop cultivated mainly in the arid and semiarid regions of China for more than 7000 years. Its grain highly nutritious and is rich in starch, protein, essential vitamins such as carotenoids, folate, and minerals. To expand the utilisation of foxtail millet, efficient and precise methods for dynamic phenotyping of its growth stages are needed. Traditional foxtail millet monitoring methods have high labour costs and are inefficient and inaccurate, impeding the precise evaluation of foxtail millet genotypic variation.

In soybean breeding programs, a great deal of time is devoted to the use of methods that perform selection of individual plants during the initial generations. Our hypothesis is that BLUPIS (simulated individual BLUP) can be efficient when applied in the initial stages of soybean breeding programs. This study aimed to explore the potential of BLUPIS in the early generations of a soybean breeding program, as well as to assess the viability of the strategy of dividing the useful area of experimental plots for estimating genotypic effects and plant selection. The experiment involved 84 segregating populations and 15 soybean parents in the F and F generations. Yield data was collected from the 2019/2020 and 2020/2021 cropping seasons. In the F generation, different data exploration methods were applied to determine the most suitable adaptation to be used in the F generation. The individual BLUP (BLUPI) was compared with BLUPIS using information from different replications and/or equal to the information used in BLUPI. The selection conducted by BLUPIS and BLUPI showed high concordance regarding the selected plants. In the F generation, segregating populations were selected based on positive genotypic effects, and individual plants within these populations were further selected according to the number of plants determined by BLUPIS. The division of the plot area was an efficient strategy for selecting segregating populations and individual plants within superior populations in the F generation, resulting in genetic gains of approximately 1.56 g per plant. When combined with the strategy of advancing generations in the off-season, the BLUPIS approach reduces the time required to achieve a high level of homozygosity. Therefore, BLUPIS proved to be a powerful statistical tool for early selection based on grain yield in soybeans.

The early and specific detection of abiotic and biotic stresses, particularly their combinations, is a major challenge for maintaining and increasing plant productivity in sustainable agriculture under changing environmental conditions. Optical imaging techniques enable cost-efficient and non-destructive quantification of plant stress states. Monomodal detection of certain stressors is usually based on non-specific/indirect features and therefore is commonly limited in their cross-specificity to other stressors. The fusion of multi-domain sensor systems can provide more potentially discriminative features for machine learning models and potentially provide synergistic information to increase cross-specificity in plant disease detection when image data are fused at the pixel level.

This study explores the application of Artificial Intelligence (AI), specifically Convolutional Neural Networks (CNNs), for detecting rice plant diseases using ARM Cortex-M microprocessors. Given the significant role of rice as a staple food, particularly in Malaysia where the rice self-sufficiency ratio dropped from 65.2% in 2021 to 62.6% in 2022, there is a pressing need for advanced disease detection methods to enhance agricultural productivity and sustainability. The research utilizes two extensive datasets for model training and validation: the first dataset includes 5932 images across four rice disease classes, and the second comprises 10,407 images across ten classes. These datasets facilitate comprehensive disease detection analysis, leveraging MobileNetV2 and FD-MobileNet models optimized for the ARM Cortex-M4 microprocessor. The performance of these models is rigorously evaluated in terms of accuracy and computational efficiency. MobileNetV2, for instance, demonstrates a high accuracy rate of 97.5%, significantly outperforming FD-MobileNet, especially in detecting complex disease patterns such as tungro with a 93% accuracy rate. Despite FD-MobileNet's lower resource consumption, its accuracy is limited to 90% across varied testing conditions. Resource optimization strategies highlight that even slight adjustments, such as a 0.5% reduction in RAM usage and a 1.14% decrease in flash memory, can result in a notable 9% increase in validation accuracy. This underscores the critical balance between computational resource management and model performance, particularly in resource-constrained settings like those provided by microcontrollers. In summary, the deployment of CNNs on microcontrollers presents a viable solution for real-time, on-site plant disease detection, demonstrating potential improvements in detection accuracy and operational efficiency. This study advances the field of smart agriculture by integrating cutting-edge AI with practical agricultural needs, aiming to address the challenges of food security in vulnerable regions.

Plant breeding played a very important role in transforming strawberries from being a niche crop with a small geographical footprint into an economically important crop grown across the planet. But even modern marker assisted breeding takes a considerable amount of time, over multiple plant generations, to produce a plant with desirable traits. As a quicker alternative, plants with desirable traits can be raised through tissue culture by doing precise genetic manipulations. Overexpression of morphogenic regulators previously known for meristem development, the transcription factors Growth-Regulating Factors (GRFs) and the GRF-Interacting Factors (GIFs), provided an efficient strategy for easier regeneration and transformation in multiple crops.

The cotton whitefly (Bemisia tabaci) is a major global pest, causing significant crop damage through viral infestation and feeding. Traditional B. tabaci recognition relies on human eyes, which requires a large amount of work and high labor costs. The pests overlapping generations, high reproductive capacity, small size, and migratory behavior present challenges for the real-time monitoring and early warning systems. This study aims to develop an efficient, high-throughput automated system for detection of the cotton whiteflies. In this work, a novel tool for cotton whitefly fast identification and quantification was developed based on deep learning-based model. This approach enhances the effectiveness of B. tabaci control by facilitating earlier detection of its establishment in cotton, thereby allowing for a quicker implementation of management strategies.

The use of renewable energy for sustainable and climate-neutral electricity production is increasing worldwide. High-voltage direct-current (HVDC) transmission via underground cables helps connect large production sides with consumer regions. In Germany, almost 5,000 km of new power line projects is planned, with an initial start date of 2038 or earlier. During transmission, heat is emitted to the surrounding soil, but the effects of the emitted heat on root growth and yield of the overlying crop plants remain uncertain and must be investigated.

In spatio-temporal plant monitoring, optical sensing (including hyperspectral imaging), is being deployed to, non-invasively, detect and diagnose plant responses to abiotic and biotic stressors. Early and accurate detection and diagnosis of stressors are key objectives. Level of radiometric repeatability of optical sensing data and ability to accurately detect and diagnose biotic stress are inversely correlated. Accordingly, it may be argued that one of the most significant frontiers and challenges regarding widespread adoption of optical sensing in plant research and crop production hinges on methods to maximize radiometric repeatability. In this study, we acquired hyperspectral optical sensing data at noon and midnight from soybean (Glycine max) and coleus wizard velvet red (Solenostemon scutellarioides) plants with/without experimentally infestation of two-spotted spider mites (Tetranychus urticae). We addressed three questions related to optimization of radiometric repeatability: (1) are reflectance-based plant responses affected by time of optical sensing? (2) if so, are plant responses to two-spotted spider mite infestations (biotic stressor) more pronounced at midnight versus at noon? (3) Is detection of biotic stress enhanced by spatial binning (smoothing) of hyperspectral imaging data? Results from this study provide insight into calculations of radiometric repeatability. Results strongly support claims that acquisition of optical sensing data to detect and characterize stress responses by plants to detect biotic stressors should be performed at night. Moreover, the combination of midnight imaging and spatial binning increased classification accuracies with 29% and 31% for soybean and coleus, respectively. Practical implications of these findings are discussed. Study results are relevant to virtually all applications of optical sensing to detect and diagnose abiotic and biotic stress responses by plants in both controlled environments and in outdoor crop production systems.

Cucumis melo L., commonly known as melon, is a crucial horticultural crop. The selection and breeding of superior melon germplasm resources play a pivotal role in enhancing its marketability. However, current methods for melon appearance phenotypic analysis rely primarily on expert judgment and intricate manual measurements, which are not only inefficient but also costly. Therefore, to expedite the breeding process of melon, we analyzed the images of 117 melon varieties from two annual years utilizing artificial intelligence (AI) technology. By integrating the semantic segmentation model Dual Attention Network (DANet), the object detection model RTMDet, the keypoint detection model RTMPose, and the Mobile-Friendly Segment Anything Model (MobileSAM), a deep learning algorithm framework was constructed, capable of efficiently and accurately segmenting melon fruit and pedicel. On this basis, a series of feature extraction algorithms were designed, successfully obtaining 11 phenotypic traits of melon. Linear fitting verification results of selected traits demonstrated a high correlation between the algorithm-predicted values and manually measured true values, thereby validating the feasibility and accuracy of the algorithm. Moreover, cluster analysis using all traits revealed a high consistency between the classification results and genotypes. Finally, a user-friendly software was developed to achieve rapid and automatic acquisition of melon phenotypes, providing an efficient and robust tool for melon breeding, as well as facilitating in-depth research into the correlation between melon genotypes and phenotypes.

Nanomaterial-mediated plant genetic engineering holds promise for developing new crop cultivars but can be hindered by nanomaterial toxicity to protoplasts. We present a fast, high-throughput method for assessing protoplast viability using resazurin, a non-toxic dye converted to highly fluorescent resorufin during respiration. Protoplasts isolated from hypocotyl canola (Brassica napus L.) were evaluated at varying temperatures (4, 10, 20, 30 ˚C) and time intervals (1-24 h). Optimal conditions for detecting protoplast viability were identified as 20,000 cells incubated with 40 µM resazurin at room temperature for 3 h. The assay was applied to evaluate the cytotoxicity of silver nanospheres, silica nanospheres, cholesteryl-butyrate nanoemulsion, and lipid nanoparticles. The cholesteryl-butyrate nanoemulsion and lipid nanoparticles exhibited toxicity across all tested concentrations (5-500 ng/ml), except at 5 ng/ml. Silver nanospheres were toxic across all tested concentrations (5-500 ng/ml) and sizes (20-100 nm), except for the larger size (100 nm) at 5 ng/ml. Silica nanospheres showed no toxicity at 5 ng/ml across all tested sizes (12-230 nm). Our results highlight that nanoparticle size and concentration significantly impact protoplast toxicity. Overall, the results showed that the resazurin assay is a precise, rapid, and scalable tool for screening nanomaterial cytotoxicity, enabling more accurate evaluations before using nanomaterials in genetic engineering.

The fungal pathogen Didymella segeticola causes leaf spot and leaf blight on tea plant (Camellia sinensis), leading to production losses and affecting tea quality and flavor. Accurate detection and quantification of D. segeticola growth in tea plant leaves are crucial for diagnosing disease severity or evaluating host resistance. In this study, we monitored disease progression and D. segeticola development in tea plant leaves inoculated with a GFP-expressing strain. By contrast, a DNA-based qRT-PCR analysis was employed for a more convenient and maneuverable detection of D. segeticola growth in tea leaves. This method was based on the comparison of D. segeticola-specific DNA encoding a Cys2His2-zinc-finger protein (NCBI accession number: OR987684) in relation to tea plant Cs18S rDNA1. Unlike ITS and TUB2 sequences, this specific DNA was only amplified in D. segeticola isolates, not in other tea plant pathogens. This assay is also applicable for detecting D. segeticola during interactions with various tea cultivars. Among the five cultivars tested, 'Zhongcha102' (ZC102) and 'Fuding-dabaicha' (FDDB) were more susceptible to D. segeticola compared with 'Longjing43' (LJ43), 'Zhongcha108' (ZC108), and 'Zhongcha302' (ZC302). Different D. segeticola isolates also exhibited varying levels of aggressiveness towards LJ43. In conclusion, the DNA-based qRT-PCR analysis is highly sensitive, convenient, and effective method for quantifying D. segeticola growth in tea plant. This technique can be used to diagnose the severity of tea leaf spot and blight or to evaluate tea plant resistance to this pathogen.

The automation of pest monitoring is highly important for enhancing integrated pest management in practice. In this context, advanced technologies are becoming increasingly explored. Hyperspectral imaging (HSI) is a technique that has been used frequently in recent years in the context of natural science, and the successful detection of several fungal diseases and some pests has been reported. Various automated measures and image analysis methods offer great potential for enhancing monitoring in practice.

Building models that allow phenotypic evaluation of complex agronomic traits in crops of global economic interest, such as grain yield (GY) in soybean and maize, is essential for improving the efficiency of breeding programs. In this sense, understanding the relationships between agronomic variables and those obtained by high-throughput phenotyping (HTP) is crucial to this goal. Our hypothesis is that vegetation indices (VIs) obtained from HTP can be used to indirectly measure agronomic variables in annual crops. The objectives were to study the association between agronomic variables in maize and soybean genotypes with VIs obtained from remote sensing and to identify computational intelligence for predicting GY of these crops from VIs as input in the models. Comparative trials were carried out with 30 maize genotypes in the 2020/2021, 2021/2022 and 2022/2023 crop seasons, and with 32 soybean genotypes in the 2021/2022 and 2022/2023 seasons. In all trials, an overflight was performed at R1 stage using the UAV Sensefly eBee equipped with a multispectral sensor for acquiring canopy reflectance in the green (550 nm), red (660 nm), near-infrared (735 nm) and infrared (790 nm) wavelengths, which were used to calculate the VIs assessed. Agronomic traits evaluated in maize crop were: leaf nitrogen content, plant height, first ear insertion height, and GY, while agronomic traits evaluated in soybean were: days to maturity, plant height, first pod insertion height, and GY. The association between the variables were expressed by a correlation network, and to identify which indices are best associated with each of the traits evaluated, a path analysis was performed. Lastly, VIs with a cause-and-effect association on each variable in maize and soybean trials were adopted as independent explanatory variables in multiple regression model (MLR) and artificial neural network (ANN), in which the 10 best topologies able to simultaneously predict all the agronomic variables evaluated in each crop were selected. Our findings reveal that VIs can be used to predict agronomic variables in maize and soybean. Soil-adjusted Vegetation Index (SAVI) and Green Normalized Dif-ference Vegetation Index (GNDVI) have a positive and high direct effect on all agronomic variables evaluated in maize, while Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE) have a positive cause-and-effect association with all soybean variables. ANN outperformed MLR, providing higher accuracy when predicting agronomic variables using the VIs select by path analysis as input. Future studies should evaluate other plant traits, such as physiological or nutritional ones, as well as different spectral variables from those evaluated here, with a view to contributing to an in-depth understanding about cause-and-effect relationships between plant traits and spectral variables. Such studies could contribute to more specific HTP at the level of traits of interest in each crop, helping to develop genetic materials that meet the future demands of population growth and climate change.