Improved detection of soluble solid and anthocyanin in litchi fruits by normalized VNIR-SWIR transmittance hyperspectral imaging and SPF-SSF-GSF-NLF fusing method.

Detection accuracy of internal component contents in fruits by hyperspectral imaging (HSI) suffered from the geometric structure and the nonlinear relation between the content and spectral features. These issues were respectively addressed by developing approaches based on spectral normalization and spectral features (SPF)-image features (SSF)-geometric structure features (GSF)-nonlinear features (NLF) fusing. For this purpose, VNIR-SWIR transmission HSI combined with partial least squares regression (PLSR) model was employed to detect the soluble solid content (SSC) and anthocyanin content (AC) in litchi fruits.

Reconstructed hyperspectral imaging for nutrient prediction in pine needles.

Introduction: Hyperspectral imaging (HSI) is a powerful, non-destructive technology that enables precise analysis of plant nutrient content, which can enhance forestry productivity and quality. However, its high cost and complexity hinder practical field applications.

Methods: To overcome these limitations, we propose a deep-learning-based method to reconstruct hyperspectral images from RGB inputs for in situ needle nutrient prediction.

Reinforcement learning-based event-driven optimal prevention control strategy for citrus huanglongbing model.

Citrus Huanglongbing (HLB) is an infectious disease transmitted by Asian citrus psyllids (ACP), which leads to serious economic losses in the citrus industry. Therefore, it is crucial to investigate the prevention and control strategy of citrus HLB. In this paper, the dynamics of HLB propagation between citrus trees and ACP is considered.

Rice Canopy Disease and Pest Identification Based on Improved YOLOv5 and UAV Images.

Traditional monitoring methods rely on manual field surveys, which are subjective, inefficient, and unable to meet the demand for large-scale, rapid monitoring. By using unmanned aerial vehicles (UAVs) to capture high-resolution images of rice canopy diseases and pests, combined with deep learning (DL) techniques, accurate and timely identification of diseases and pests can be achieved. We propose a method for identifying rice canopy diseases and pests using an improved YOLOv5 model (YOLOv5_DWMix).

Research on a Method for Identification of Chinese Rose Leaf Pests and Diseases Based on a Lightweight CR-YOLO Model.

Accurate and rapid detection of pests and diseases on Chinese rose leaves is crucial for horticultural management and production quality. Despite advances in detection methods, challenges such as complex backgrounds, variable lighting conditions, and subtle disease manifestations in natural environments often lead to diminished detection accuracy and high computational costs. Traditional detection models typically require substantial computational resources, limiting their practical applicability in real-world horticultural settings.

Spectroscopic detection of cotton Verticillium wilt by spectral feature selection and machine learning methods.

Introduction: Verticillium wilt is a severe soil-borne disease that affects cotton growth and yield. Traditional monitoring methods, which rely on manual investigation, are inefficient and impractical for large-scale applications. This study introduces a novel approach combining machine learning with feature selection to identify sensitive spectral features for accurate and efficient detection of cotton Verticillium wilt.

Apple Yield Estimation Method Based on CBAM-ECA-Deeplabv3+ Image Segmentation and Multi-Source Feature Fusion.

Apple yield estimation is a critical task in precision agriculture, challenged by complex tree canopy structures, growth stage variability, and orchard heterogeneity. In this study, we apply multi-source feature fusion by combining vegetation indices from UAV remote sensing imagery, structural feature ratios from ground-based fruit tree images, and leaf chlorophyll content (SPAD) to improve apple yield estimation accuracy. The DeepLabv3+ network, optimized with Convolutional Block Attention Module (CBAM) and Efficient Channel Attention (ECA), improved fruit tree image segmentation accuracy.

Detection of litchi fruit maturity states based on unmanned aerial vehicle remote sensing and improved YOLOv8 model.

Rapid and accurate detection of the maturity state of litchi fruits is crucial for orchard management and picking period prediction. However, existing studies are largely limited to the binary classification of immature and mature fruits, lacking dynamic evaluation and precise prediction of maturity states. To address these limitations, this study proposed a method for detecting litchi maturity states based on UAV remote sensing and YOLOv8-FPDW.

Analysis of the genetic basis of fiber-related traits and flowering time in upland cotton using machine learning.

Cotton is an important crop for fiber production, but the genetic basis underlying key agronomic traits, such as fiber quality and flowering days, remains complex. While machine learning (ML) has shown great potential in uncovering the genetic architecture of complex traits in other crops, its application in cotton has been limited. Here, we applied five machine learning models-AdaBoost, Gradient Boosting Regressor, LightGBM, Random Forest, and XGBoost-to identify loci associated with fiber quality and flowering days in cotton.

Design and performance evaluation of a spiral bar precision weeding mechanism for corn fields.

This paper focuses on addressing the limitations of existing mechanical weeding methods for corn plants by introducing a spiral tendon-type precision weeding device specifically designed for corn fields. The study encompasses mechanical design and theoretical analysis to determine the overall structure, component parts, application scenarios, operation modes, and working principles of the device. The force applied to the spiral tendon weeding cutter head, a crucial working component of the device, is analyzed, along with its motion characteristics.

Evaluating spray drift from Uncrewed Aerial Spray Systems: A machine learning and variance-based sensitivity analysis of environmental and spray system parameters.

Uncrewed Aerial Spray Systems (UASS), commonly called drones, have become an important application technique for plant protection products in Asia and worldwide. As such, environmental variables and spray system parameters influencing spray drift deserve detailed investigations. This study presents the data analysis of 114 UASS drift trials conducted between December 2021 and December 2022 in China.

Assessing the efficiency of UAV for pesticide application in disease management of peanut crop.

Background: Effective utilization of plant protection UAVs in peanut cultivation management necessitates a comprehensive grasp of how application volume rates and pesticides influence peanut leaf spot and rust control. This study aimed to compare the effects of application volume rates and pesticides on droplet deposition, disease, leaf retention rate and peanut yield. A T20 plant protection unmanned aerial vehicle (UAV) sprayer was used to apply four various pesticide doses.

Triboelectric micro-flexure-sensitive fiber electronics.

Developing fiber electronics presents a practical approach for establishing multi-node distributed networks within the human body, particularly concerning triboelectric fibers. However, realizing fiber electronics for monitoring micro-physiological activities remains challenging due to the intrinsic variability and subtle amplitude of physiological signals, which differ among individuals and scenarios. Here, we propose a technical approach based on a dynamic stability model of sheath-core fibers, integrating a micro-flexure-sensitive fiber enabled by nanofiber buckling and an ion conduction mechanism.

Evaluating the use of unmanned aerial vehicles for spray applications in mountain Nanguo pear orchards.

Background: Nanguo pear is a distinctive pear variety in northeast China, grown mainly in mountainous areas. Due to terrain limitations, ground-based pesticide application equipment is difficult to use. This limitation could be overcome by using unmanned aerial vehicles (UAVs) for pesticide application in Nanguo pear orchards.

The effect of Aceria litchii (Keifer) infestation on the surface properties of litchi leaf hosts.

Background: The wettability of target crop surfaces affects pesticide wetting and deposition. The structure and properties of the leaf surface of litchi leaves undergo severe changes after infestation by Aceria litchii (Keifer). The objective of this study was to systematically investigate the surface texture and wettability of litchi leaves infested.

Zinc-oxide nanoparticles ameliorated the phytotoxic hazards of cadmium toxicity in maize plants by regulating primary metabolites and antioxidants activity.

Cadmium stress is a major threat to plant growth and survival worldwide. The current study aims to green synthesis, characterization, and application of zinc-oxide nanoparticles to alleviate cadmium stress in maize ( L.) plants.

Visible and NIR microscopic hyperspectrum reconstruction from RGB images with deep convolutional neural networks.

We investigate the microscopic hyperspectral reconstruction from RGB images with a deep convolutional neural network (DCNN) in this paper. Based on the microscopic hyperspectral imaging system, a homemade dataset consisted of microscopic hyperspectral and RGB image pairs is constructed. For considering the importance of spectral correlation between neighbor spectral bands in microscopic hyperspectrum reconstruction, the 2D convolution is replaced by 3D convolution in the DCNN framework, and a metric (weight factor) used to evaluate the performance reconstructed hyperspectrum is also introduced into the loss function used in training.

Tryptophan Seed Treatment Improves Morphological, Biochemical, and Photosynthetic Attributes of the Sunflower under Cadmium Stress.

Tryptophan, as a signal molecule, mediates many biotic and environmental stress-induced physiological responses in plants. Therefore, an experiment was conducted to evaluate the effect of tryptophan seed treatment in response to cadmium stress (0, 0.15, and 0.

Point Cloud Completion of Plant Leaves under Occlusion Conditions Based on Deep Learning.

The utilization of 3-dimensional point cloud technology for non-invasive measurement of plant phenotypic parameters can furnish important data for plant breeding, agricultural production, and diverse research applications. Nevertheless, the utilization of depth sensors and other tools for capturing plant point clouds often results in missing and incomplete data due to the limitations of 2.5D imaging features and leaf occlusion.

Evaluation of the deposition and distribution of spray droplets in citrus orchards by plant protection drones.

Plant protection drone spraying technology is widely used to prevent and control crop diseases and pests due to its advantages of being unaffected by crop growth patterns and terrain restrictions, high operational efficiency, and low labor requirements. The operational parameters of plant protection drones significantly impact the distribution of spray droplets, thereby affecting pesticide utilization. In this study, a field experiment was conducted to determine the working modes of two representative plant protection drones and an electric backpack sprayer as a control to explore the characteristics of droplet deposition with different spray volumes in the citrus canopy.