Using the Pearson's correlation coefficient as the sole metric to measure the accuracy of quantitative trait prediction: is it sufficient?

How to evaluate the accuracy of quantitative trait prediction is crucial to choose the best model among several possible choices in plant breeding. Pearson's correlation coefficient (PCC), serving as a metric for quantifying the strength of the linear association between two variables, is widely used to evaluate the accuracy of the quantitative trait prediction models, and generally performs well in most circumstances. However, PCC may not always offer a comprehensive view of predictive accuracy, especially in cases involving nonlinear relationships or complex dependencies in machine learning-based methods.

Development and Validation of a Mechanistic, Weather-Based Model for Walnut Blight Caused by pv. .

Walnut blight, caused by Xanthomonas arboricola pv. juglandis (Xaj), occurs worldwide in almost all areas where the Persian walnut (Juglans regia) is grown, causing significant reductions in nut yield via defoliation and fruit drop. The disease control relies on the calendar-based, repeated use of chemical bactericides, negatively impacting economic and environmental sustainability and potentially inducing Xaj resistance to chemicals.

Blockchain-based proxy re-encryption access control method for biological risk privacy protection of agricultural products.

In today's globalized agricultural system, information leakage of agricultural biological risk factors can lead to business risks and public panic, jeopardizing corporate reputation. To solve the above problems, this study constructs a blockchain network for agricultural product biological risk traceability based on agricultural product biological risk factor data to achieve traceability of biological risk traceability data of agricultural product supply chain to meet the sustainability challenges. To guarantee the secure and flexible sharing of agricultural product biological risk privacy information and limit the scope of privacy information dissemination, the blockchain-based proxy re-encryption access control method (BBPR-AC) is designed.

Predicting the oil content of individual corn kernels combining NIR-HSI and multi-stage parameter optimization techniques.

Predicting the oil content of individual corn kernels using hyperspectral imaging and ML offers the advantages of being rapid and non-destructive. However, traditional methods rely on expert experience for setting parameters. In response to these limitations, this study has designed an innovative multi-stage grid search technique, tailored to the characteristics of spectral data.

Research on Positioning and Navigation System of Greenhouse Mobile Robot Based on Multi-Sensor Fusion.

The labor shortage and rising costs in the greenhouse industry have driven the development of automation, with the core of autonomous operations being positioning and navigation technology. However, precise positioning in complex greenhouse environments and narrow aisles poses challenges to localization technologies. This study proposes a multi-sensor fusion positioning and navigation robot based on ultra-wideband (UWB), an inertial measurement unit (IMU), odometry (ODOM), and a laser rangefinder (RF).

Phenotype identification and genome-wide association study of ear-internode vascular bundles in maize (Zea mays).

The vascular bundle in the ear-internode of maize is a key conduit for transporting photosynthetic materials between "source" and "sink", making it critically important to examine its micro-phenotypes and genetic architecture to identify advantageous characteristics and cultivate high-yielding and high-quality varieties. Unfortunately, the limited observation methods and scope of study precludes any comprehensive and systematic investigations into the microscopic phenotypes and genetic mechanisms of vascular bundle in maize ear-internode. In this study, 47 phenotypic traits were extracted in 495 maize inbred lines using micro computed tomography (Micro-CT) scanning technology and a deep learning-based phenotype acquisition method for stem vascular bundle, which included stem slice-related, epidermis zone-related, periphery zone-related, inner zone-related and vascular bundles-related traits.

3D Morphological Feature Quantification and Analysis of Corn Leaves.

Marked variations in the 3-dimensional (3D) shape of corn leaves can be discerned as a function of various influences, including genetics, environmental factors, and the management of cultivation processes. However, the causes of these variations remain unclear, primarily due to the absence of quantitative methods to describe the 3D spatial morphology of leaves. To address this issue, this study acquired 3D digitized data of ear-position leaves from 478 corn inbred lines during the grain-filling stage.

A Clean and Health-Care-Focused Way to Reduce Indoor Airborne Bacteria in Calf House with Long-Wave Ultraviolet.

Long-term exposure to a relatively high concentration of airborne bacteria emitted from intensive livestock houses could potentially threaten the health and welfare of animals and workers. There is a dual effect of air sterilization and promotion of vitamin D synthesis for the specific bands of ultraviolet light. This study investigated the potential use of A-band ultraviolet (UVA) tubes as a clean and safe way of reducing airborne bacteria and improving calf health.

Changes in climate attributes and harvest area structures jointly determined spatial-temporal variations in water footprint of maize in the Beijing-Tianjin-Hebei region.

Irrigation contributes significantly to boosting crop yield and ensuring food security. However, in the Beijing-Tianjin-Hebei (BTH) region, unsustainable irrigation practices have led to serious outcomes on freshwater resources. Balancing irrigation with crop productivity in this region, currently facing complex challenge, requires a comprehensive understanding of its spatial pattern and thus to seeking for potential optimization of current crop structures.

Wheat Fusarium Head Blight Automatic Non-Destructive Detection Based on Multi-Scale Imaging: A Technical Perspective.

Fusarium head blight (FHB) is a major threat to global wheat production. Recent reviews of wheat FHB focused on pathology or comprehensive prevention and lacked a summary of advanced detection techniques. Unlike traditional detection and management methods, wheat FHB detection based on various imaging technologies has the obvious advantages of a high degree of automation and efficiency.

Detection and Identification of Tassel States at Different Maize Tasseling Stages Using UAV Imagery and Deep Learning.

The tassel state in maize hybridization fields not only reflects the growth stage of the maize but also reflects the performance of the detasseling operation. Existing tassel detection models are primarily used to identify mature tassels with obvious features, making it difficult to accurately identify small tassels or detasseled plants. This study presents a novel approach that utilizes unmanned aerial vehicles (UAVs) and deep learning techniques to accurately identify and assess tassel states, before and after manually detasseling in maize hybridization fields.

Preparation of waterborne anti-counterfeiting ink based on dual luminescent nanohybrids of bacterial cellulose nanocrystals and lanthanide‑nitrogen co-modified GQDs.

To address the growing challenge of counterfeit prevention, this study developed a novel anti-counterfeiting ink system based on bacterial cellulose nanocrystals (BCNC) and lanthanide (Er, Yb)‑nitrogen (N) co-dropped graphene quantum dots (GQDs), which exhibited both photoluminescence (PL) and upconversion photoluminescence (UCPL) fluorescent properties as well as excellent rheological characteristics. The Er/Yb/N-GQDs with positive charges were synthesized by a one-step hydrothermal method and subsequently assembled with negatively charged BCNC through electrostatic self-assembly to fabricate a novel nanohybrid, Er/Yb/N-GQDs-BCNC. Raman spectroscopy results indicated an enhancement in the graphitization of GQDs due to lanthanide modification.

An ammonia-sensitive fluorescence sensor based on polyvinyl alcohol-graphene quantum dots/halloysite nanotubes hybrid film for monitoring fish freshness.

A fluorescent hybrid film composed of nitrogen-doped graphene quantum dots (N-GQDs) loaded on halloysite nanotubes (HNTs) (N-GQDs/HNTs nanocomposite) as a sensitive element and polyvinyl alcohol (PVA) as a film-forming matrix was designed for freshness detection. The PVA-N-GQDs/HNTs hybrid film exhibited significantly enhanced fluorescence attributed to the loading of N-GQDs onto the surface of HNTs through electrostatic interactions and hydrogen bonding, effectively reducing their aggregation. The fluorescence of the hybrid film could be quenched by ammonia via photoinduced electron transfer (PET), with good linearity in the range of 20 ppm to 500 ppm ammonia and a limit of detection (LOD) of 0.

Deep learning models with optimized fluorescence spectroscopy to advance freshness of rainbow trout predicting under nonisothermal storage conditions.

This study established long short-term memory (LSTM), convolution neural network long short-term memory (CNN_LSTM), and radial basis function neural network (RBFNN) based on optimized excitation-emission matrix (EEM) from fish eye fluid to predict freshness changes of rainbow trout under nonisothermal storage conditions. The method of residual analysis, core consistency diagnostics, and split-half analysis of parallel factor analysis was used to optimize EEM data, and two characteristic components were extracted. LSTM, CNN_LSTM, and RBFNN models based on characteristic components of EEM used to predict the freshness indices.

TrG2P: A transfer-learning-based tool integrating multi-trait data for accurate prediction of crop yield.

Yield prediction is the primary goal of genomic selection (GS)-assisted crop breeding. Because yield is a complex quantitative trait, making predictions from genotypic data is challenging. Transfer learning can produce an effective model for a target task by leveraging knowledge from a different, but related, source domain and is considered a great potential method for improving yield prediction by integrating multi-trait data.

Using high-throughput phenotype platform MVS-Pheno to reconstruct the 3D morphological structure of wheat.

It is of great significance to study the plant morphological structure for improving crop yield and achieving efficient use of resources. Three dimensional (3D) information can more accurately describe the morphological and structural characteristics of crop plants. Automatic acquisition of 3D information is one of the key steps in plant morphological structure research.

Three-Dimensional Modeling of Maize Canopies Based on Computational Intelligence.

The 3-dimensional (3D) modeling of crop canopies is fundamental for studying functional-structural plant models. Existing studies often fail to capture the structural characteristics of crop canopies, such as organ overlapping and resource competition. To address this issue, we propose a 3D maize modeling method based on computational intelligence.

Research on the influence factors of sustainable development of plateau characteristic agriculture based on DEMATEL and AISM combined model.

Under the background of the continuous progress of China's agricultural reform, the development of characteristic agriculture is an important field of agricultural development in the country and even the world. Yunnan has unique advantages in geography, location, climate, and human resources, and has unique conditions for the development of agriculture with plateau characteristic. However, the sustainable development of agriculture with plateau characteristic is affected and restricted by many factors.

Design and experiments with a SLAM system for low-density canopy environments in greenhouses based on an improved Cartographer framework.

To address the problem that the low-density canopy of greenhouse crops affects the robustness and accuracy of simultaneous localization and mapping (SLAM) algorithms, a greenhouse map construction method for agricultural robots based on multiline LiDAR was investigated. Based on the Cartographer framework, this paper proposes a map construction and localization method based on spatial downsampling. Taking suspended tomato plants planted in greenhouses as the research object, an adaptive filtering point cloud projection (AF-PCP) SLAM algorithm was designed.

Determination of lead content in oilseed rape leaves in silicon-free and silicon environments based on deep transfer learning and fluorescence hyperspectral imaging.

The ability of fluorescence hyperspectral imaging to predict heavy metal lead (Pb) concentration in oilseed rape leaves was studied in silicon-free and silicon environments. Further, the transfer stacked convolution auto-encoder (T-SCAE) algorithm was proposed based on the stacked convolution auto-encoder (SCAE) algorithm. Fluorescence hyperspectral images of oilseed rape leaves under different Pb stress contents were obtained in the silicon-free and silicon environments.

Agricultural machinery automatic navigation technology.

In this paper, we review, compare, and analyze previous studies on agricultural machinery automatic navigation and path planning technologies. First, the paper introduces the fundamental components of agricultural machinery autonomous driving, including automatic navigation, path planning, control systems, and communication modules. Generally, the methods for automatic navigation technology can be divided into three categories: Global Navigation Satellite System (GNSS), Machine Vision, and Laser Radar.

Study on a Pig Vocalization Classification Method Based on Multi-Feature Fusion.

To improve the classification of pig vocalization using vocal signals and improve recognition accuracy, a pig vocalization classification method based on multi-feature fusion is proposed in this study. With the typical vocalization of pigs in large-scale breeding houses as the research object, short-time energy, frequency centroid, formant frequency and first-order difference, and Mel frequency cepstral coefficient and first-order difference were extracted as the fusion features. These fusion features were improved using principal component analysis.

An FPGA implementation of Bayesian inference with spiking neural networks.

Spiking neural networks (SNNs), as brain-inspired neural network models based on spikes, have the advantage of processing information with low complexity and efficient energy consumption. Currently, there is a growing trend to design hardware accelerators for dedicated SNNs to overcome the limitation of running under the traditional von Neumann architecture. Probabilistic sampling is an effective modeling approach for implementing SNNs to simulate the brain to achieve Bayesian inference.

Porphyrin fluorescence imaging for real-time monitoring and visualization of the freshness of beef stored at different temperatures.

This study presents a novel fluorescence imaging method for the real-time monitoring of beef quality deterioration and freshness. The fluorescence property of porphyrin in the form of heme can be used to characterize quality changes in beef during storage. Therefore, a fluorescence imaging system with an excitation light source of 440 nm and a CCD camera with a specific wavelength filter of 595 nm was constructed, and the porphyrin fluorescence images of beef samples stored at different temperatures were then collected.