Abstract Traditional greenhouse cleaning methods are labor-intensive, prone to human error, and inefficient, often compromising light transmittance and productivity. To address these challenges, this study proposes an autonomous robot designed to clean greenhouse roofs efficiently and reliably. The robot features an integrated cleaning system with adjustable brushes, wipers, and water sprinklers, ensuring optimal performance and significantly improving light transmittance. Powered by a 500 W PV system, it utilizes electric wheels for smooth, stable movement and incorporates a replaceable brush-wiper mechanism for enhancing durability and maintenance efficiency. The design process involved SolidWorks modeling for mass properties, CFD simulations with the k-ε turbulence model to evaluate wind load conditions, and ANSYS structural analysis to confirm durability under extreme wind speeds of up to 126 km/h (ten times greater than normal conditions). Structural tested at different robot’s rotational speeds 25 rpm and 50 rpm confirmed optimal performance at 25 rpm, balancing cleaning efficiency and long-term durability. Additionally, the robot incorporates advanced control unit with sensors for autonomous operation, real-time light transmission monitoring, and navigation capabilities, distinguishing it from traditional manual or semi-automated methods. The results demonstrated robust performance in extreme conditions, surpassing existing systems limited to standard weather. The robot’s performance is limited by speed (0.35 m/s), battery life, roof complexity, maintenance, adaptability, and cost, indicating areas for improvement. Future developments will integrate AI for autonomous decision-making, GPS for precise navigation, and a smart cleaning system to optimize performance based on real-time data, further reducing maintenance costs and ensuring optimal greenhouse lighting.

Visible/near-infrared spectroscopy provides a non-destructive approach for evaluating soluble solids content (SSC) of pears (Pyrus pyrifolia Nakai). However, variations among pear cultivars, especially in pear color, markedly affect spectral reflectance, thereby limiting the cross-cultivar SSC prediction. To address this, we developed SpecColorNet, a multimodal deep learning framework that integrates spectral and peel color data to accurately predict the SSC of six different pear cultivars simultaneously, and interpreted its decision-making mechanism with gradient-weighted class activation mapping++ (Grad-CAM++). The prediction accuracy of the multicultivar SpecColorNet was improved by 9.42 % compared to the multi-cultivar model based solely on spectral data with corresponding RMSEP values of 0.63, 0.60, 0.92, 0.70, 0.54 and 0.55 degrees Brix for six different pear cultivars. In addition, the prediction accuracy of SpecColorNet was comparable to that of single-cultivar spectral models. The interpretation analysis indicated that the SpecColorNet successfully directed the model's attention to the 555-640 nm spectral region, where the most marked cross-cultivar differences occur, thereby improving its generalization and accuracy. Overall, this study proposed a multimodal approach for robust SSC prediction across diverse pear cultivars, which overcame the challenge of cultivar diversity and peel color variability to enable multi-cultivar models with enhanced robustness over pure spectroscopy-based methods. (c) 2025 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Precision seeding represents a key advancement in rapeseed mechanization, offering an effective strategy to minimize seed usage, reduce labor requirements, and improve efficiency. Accurate real-time seed-flow monitoring is essential for effectively maintaining sowing quality. Conventional detection techniques struggle with high-frequency seed discharge, leading to missed detections of overlapping seeds, limited resistance to interference, and diminished accuracy. This study introduces a novel rapeseed seed-flow detection method and sensor system based on microwave resonant cavity perturbation with absorptive damping. By constructing a unidirectional microwave field and incorporating absorbent materials, multipath reflections within the cavity were significantly mitigated, thereby improving detection stability and precision. CST simulations confirmed the directional damping and absorption characteristics of the materials, introducing negligible attenuation (mean 0.41 dB) along the main propagation path while providing strong suppression (mean 16.92 dB) of unwanted field components. The signal-processing circuit features two-stage intermediate-frequency amplification, RMS detection, and multithreshold comparators. When integrated with an overlapping-seed recognition algorithm and an STM32 microcontroller, the system achieved real-time counting of both single and overlapping seeds. Bench tests revealed that at a seeding frequency of approximately 31 Hz, the detection error of the device was below 2.3%, compared to over 4.1% for traditional photoelectric and fiber-optic sensors. Additional tests using multiple rapeseed varieties further verified robust single-seed detection accuracy (>97%). The recognition rates for double and triple overlapping seeds reached 76.3% and 42.0%, respectively. In field trials, detection accuracy remained above 95.5% with stable performance under operational speeds of 2.8-4.8 km/h and seeding frequencies of 12.7-30.7 Hz. The results offer an effective technical solution for precise seed-flow monitoring in rapeseed direct seeding, enhancing precision planter intelligence.

The performance evaluation of fertilizer spreaders is essential for improving the efficiency and accuracy of fertilizer application in modern agriculture. Traditional methods, such as manual collection boxes, are timeconsuming, labor-intensive, and unsuitable for intelligent agricultural systems. To address these limitations, this study proposes a deep learning-based intelligent detection system for analyzing granular fertilizer deposition distribution patterns. The system integrates image acquisition, data processing, visualization, and storage functions, enabling real-time detection and operational optimization. A novel segmentation model, SF-TPP, was developed by combining a swin transformer backbone with a path aggregation network (PANet), region proposal network (RPN), ROI alignment, and enhanced by CBAM and feature refinement modules. Comparative experiments demonstrated that SF-TPP outperformed conventional models (F-TPP, SOLO, YOLOv5), achieving 92.8% precision, 91.3% recall, and an F1 score of 0.920. A strong correlation (R2 = 0.9626) was established between pixel area and fertilizer mass, enabling accurate mass estimation. Field experiments showed that the system achieved low mean absolute errors in key metrics, including a CV MAE of 0.99 and a fertilization amount MAE of 0.76 g. These results demonstrate the system's high accuracy and practical potential for supporting precision agriculture.

Fruit detection in orchard environments is often challenged by illumination variation, leaf occlusion, cluttered backgrounds, and dense distributions of small targets, which limit the robustness of visible-light detectors in practical applications. To address these issues, this study proposes DTNet, an end-to-end RGB-IR dual-modal detection framework for close-range fruit instance recognition. Rather than relying on single-modal appearance cues, DTNet improves detection robustness by jointly exploiting cross-modal complementary perception, fine-grained local detail enhancement, and global relational modeling, thereby strengthening the representation of small, partially occluded, and illumination-sensitive fruit targets. In addition, a multimodal balanced detection loss is introduced to improve optimization stability under class imbalance and hard-sample interference. Experiments on a grape RGB-IR object detection dataset show that DTNet achieves a Precision of 0.9132, a Recall of 0.8931, an mAP@0.5 of 0.9552, and an mAP@0.5:0.95 of 0.8001, outperforming competing methods overall. On an additional tomato RGB-IR dataset, DTNet also maintains stable detection accuracy, indicating favorable adaptability to another fruit RGB-IR detection task. The results indicate that DTNet is a promising approach for robust RGB-IR fruit detection in complex orchard environments.

ABSTRACTThe robotics industry has seen significant growth and advancement in the agricultural sector, particularly, in agricultural greenhouses. This study presents the most common cleaning methods for greenhouse roofs, including manual and automatic cleaning techniques. On the other hand, it provides greenhouse types, designs, and factors affecting lighting. It highlights the importance of cleaning and discusses the challenges of various robot designs and their future prospects. It was found that dust accumulation on greenhouse roofs could decrease the light intensity by 30%. Consequently, it could reduce the crop yield and quality by over 30%. Therefore, regular cleaning is crucial to provide the optimal light intensity for crops. The manual or traditional cleaning methods can cause damage, injuries, inefficiency, high costs, and labor shortages. This study investigates greenhouse‐cleaning robots and devices in terms of their operation, specifications, quality, and accuracy. Meanwhile, it summarizes the pros and cons of automatic cleaning. Furthermore, it evaluates robots based on a movement system, cleaning efficiency, and all characteristics in different countries. It was found that robots for Venlo‐type greenhouses are the most common and advanced robots, which achieve a light transmittance of up to 88% after cleaning. China and the Netherlands are at the forefront of commercial research technology applications. In conclusion, this study recommends investing more in robotic greenhouse cleaners to boost productivity and develop an intelligent cleaning system for greenhouse roofs. In addition, further research should be implemented to improve the durability and decrease the prices of robots.

The effective diagnosis of mild nutrient stress across the complete growth cycle of facility-grown tomatoes is challenging. This study proposes a deep learning framework based on CNN + LSTM, using canopy near-infrared spectroscopy from different growth stages of tomatoes as input, to diagnose mild stress of nitrogen (N), potassium (K), and calcium (Ca) throughout the entire growth cycle of facility-grown tomatoes. The study compares the diagnostic performance of Random Forest (RF), Support Vector Machine (SVM), Partial Least Squares (PLS), Convolutional Neural Networks (CNNs), and CNN + Long Short-Term Memory (LSTM) models for detecting mild nutrient stress in facility-grown tomatoes. Firstly, the preprocessing method of spectral characteristic bands combined with Savitzky-Golay (SG) + Standard Normal Variate (SNV) was determined. Subsequently, all sample data were divided into six groups: N-deficient, K-deficient, Ca-deficient, N-excess, K-excess, and Ca-excess. The aforementioned models were then used for classification prediction. The results show that RF and CNN + LSTM models demonstrated good predictive performance. Specifically, RF achieved accuracy rates of 70.14%, 90.81%, 88.59%, and 85.37% in the classification tasks of Ca-deficient, N-excess, K-excess, and Ca-excess, respectively. The CNN + LSTM model achieved accuracy rates of 93.33%, 63.33%, 99.2%, 83.33%, and 98.52% in the classification tasks of K-deficient, Ca-deficient, N-excess, K-excess, and Ca-excess, respectively. Finally, in the Leave-One-Group-Out Validation (LOGOV) for validating the model’s generalisation performance, RF performed better in the N-deficient, K-deficient, and Ca-deficient tasks, achieving diagnostic accuracy rates of 80.19%, 81.43%, and 77.02%, respectively. The CNN + LSTM model showed a diagnostic accuracy rate of 66.72% in the N-excess classification task. The study concludes that, given complete training data, the CNN + LSTM model can effectively diagnose mild nutrient stress (N, K, and Ca) in facility-grown tomatoes in most scenarios.

Greenhouse technology is crucial for combating climate change and enhancing off-season produce and crop yield. Dust build-up on greenhouse roofs remains a significant problem, significantly reducing light transmission and affecting plant growth. Therefore, this study aims to classify dust build-up on greenhouse roofs and to address it by designing, manufacturing, operating, and evaluating a solar-powered cleaning robot system. Water and cement removal agent (CRA) with different mixing ratios were selected to clean the greenhouse roof. The initial findings demonstrate the greenhouse cover's ability to allow light to pass through can differentiate between different levels of dust accumulation. This study reveals that 88 %-93 % of transmittance levels indicate a clean roof, 82 %-87 % reveal minor dust accumulation, 81 %-75 % denote moderate dust accumulation, and below 75 % signify major dust accumulation. This increased the efficiency of the light intensity for minor dust roofs from 39,285 to 41,731 lux ((CRA) 1:3 water), moderate dust roofs from 36,777 to 39,383 lux ((CRA) 1:1 water), and major dust roofs from 30,585 to 35,525 lux (CRA). The results show a substantial improvement in light transmittance from 88 % to 93 % for clean roofs, compared to much lower levels for dust-accumulated roofs.

Monitoring fruit tree flowering information in the open world is more crucial than in the research-oriented environment for managing agricultural production to increase yield and quality. This work presents a transformer-based flowering period monitoring approach in an open world in order to better monitor the whole blooming time of modern standardized orchards utilizing IoT technologies. This study takes images of flowering apple trees captured at a distance in the open world as the research object, extends the dataset by introducing the Slicing Aided Hyper Inference (SAHI) algorithm, and establishes an S-YOLO apple flower detection model by substituting the YOLOX backbone network with Swin Transformer-tiny. The experimental results show that S-YOLO outperformed YOLOX-s in the detection accuracy of the four blooming states by 7.94%, 8.05%, 3.49%, and 6.96%. It also outperformed YOLOX-s by 10.00%, 9.10%, 13.10%, and 7.20% for mAP(ALL), mAP(S), mAP(M), and mAP(L), respectively. By increasing the width and depth of the network model, the accuracy of the larger S-YOLO was 88.18%, 88.95%, 89.50%, and 91.95% for each flowering state and 39.00%, 32.10%, 50.60%, and 64.30% for each type of mAP, respectively. The results show that the transformer-based method of monitoring the apple flower growth stage utilized S-YOLO to achieve the apple flower count, percentage analysis, peak flowering time determination, and flowering intensity quantification. The method can be applied to remotely monitor flowering information and estimate flowering intensity in modern standard orchards based on IoT technology, which is important for developing fruit digital production management technology and equipment and guiding orchard production management.

Automatic plant phenotype measurement technology based on the rapid and accurate reconstruction of maize structures at the seedling stage is essential for the early variety selection, cultivation, and scientific management of maize. Manual measurement is time-consuming, laborious, and error-prone. The lack of mobility of large equipment in the field make the high-throughput detection of maize plant phenotypes challenging. Therefore, a global 3D reconstruction algorithm was proposed for the high-throughput detection of maize phenotypic traits. First, a self-propelled mobile platform was used to automatically collect three-dimensional point clouds of maize seedling populations from multiple measurement points and perspectives. Second, the Harris corner detection algorithm and singular value decomposition (SVD) were used for the pre-calibration single measurement point multi-view alignment matrix. Finally, the multi-view registration algorithm and iterative nearest point algorithm (ICP) were used for the global 3D reconstruction of the maize seedling population. The results showed that the R2 of the plant height and maximum width measured by the global 3D reconstruction of the seedling maize population were 0.98 and 0.99 with RMSE of 1.39 cm and 1.45 cm and mean absolute percentage errors (MAPEs) of 1.92% and 2.29%, respectively. For the standard sphere, the percentage of the Hausdorff distance set of reconstruction point clouds less than 0.5 cm was 55.26%, and the percentage was 76.88% for those less than 0.8 cm. The method proposed in this study provides a reference for the global reconstruction and phenotypic measurement of crop populations at the seedling stage, which aids in the early management of maize with precision and intelligence.

[目的]为测定温室中番茄不同成熟阶段的果实数量,提出一种基于彩色点云图像的测定方法.[方法]在移动平台上搭载KinectV2.0采集温室中行栽番茄的图像信息合成番茄植株点云,再将二视角的番茄植株点云合成1个点云,并通过深度信息截取得到近处番茄植株点云,将标注的点云数据输入到PointRCNN目标检测网络训练预测模型,并识别番茄植株点云中的番茄果实,最后利用基于特征矩阵训练的支持向量机(Support vector machine,SVM)分类器对已经识别出来的果实进行成熟阶段分类,获得不同成熟阶段番茄果实的数量.[结果]基于PointRCNN目标检测网络的方法识别番茄果实数量的精确率为86.19％,召回率为83.39％;基于特征矩阵训练的SVM分类器,针对番茄果实成熟阶段的预测结果在训练集上准确率为94.27％,测试集上准确率为96.09％.[结论]基于彩色点云图像的测定方法能够较为准确地识别不同成熟阶段的番茄果实,可以为评估温室番茄产量提供数据支撑.

[目的]本文旨在探索实时监测温室虫情和精准防控虫害的方法.[方法]设计了一种基于诱虫板图像背景均匀化的自适应分割方法,结合基于随机森林(random forest,RF)的图像识别算法识别4类温室番茄害虫(烟粉虱、潜叶蝇、果蝇和蚜虫)并计数.该方法首先提取诱虫板图像RGB(red-green-blue)颜色模型B分量和HSV(hue-saturation-value)颜色模型V分量,然后分别对2张图像分段调整背景灰度值得到均匀背景诱虫板灰度图像,再利用最大类间方差法确定阈值分割图像,经形态学处理后融合2张诱虫板二值图像,最后提取害虫区域的6个颜色特征、8个形状特征和6个纹理特征,训练随机森林以识别害虫并计数.[结果]对比分析Sauvola局部阈值法、Prewitt边缘分割法、k-means聚类法以及本文设计的自适应分割方法,结果表明基于背景均匀化的自适应分割方法效果最好,平均分割准确率为95.34％.对比分析7种特征向量组合下随机森林、C-SVC(C-support vector classification)和BP(back propagation)神经网络3种分类方法,结果表明综合颜色特征向量、形状特征向量和纹理特征向量作为输入的随机森林算法识别效果更好,对烟粉虱、潜叶蝇、果蝇和蚜虫的识别准确率分别为93.89％、90.71％、91.54％和90.40％.[结论]本文设计的方法能够实现诱虫板上4类害虫的识别和计数,可以为温室虫情监测与预警提供参考.

为了探究局部按压对白熟期、转色期、粉红期番茄机械损伤的影响规律,该研究采用水果硬度计对番茄进行按压,并用扫描电镜观察按压处番茄组织微观结构变化,以腐烂、褶皱、有压痕、无压痕4个损伤等级和损伤显象天数作为评价标准,通过按压试验,分析局部按压压强对各级损伤的影响规律,得到不同成熟度番茄各损伤等级的压强分布区间,建立番茄局部机械损伤评估分类模型.最后,通过三指电爪进行了抓取试验.结果表明:1)各成熟度番茄的损伤程度随按压压强和成熟度升高而增大;2)各成熟度番茄的显象天数随成熟度升高而减小,腐烂天数与按压压强相关性很小,褶皱天数随按压压强升高而减小;3)以中位数作为代表压强,一级损伤代表压强按成熟度由低到高依次为:366、355、337 kPa,比二级损伤代表压强265,245,225 kPa均提高了30％左右;出现三级损伤的代表压强按成熟度由低到高依次为:165,115,90 kPa;腐烂天数范围为3～7 d,相较于褶皱天数范围7～17 d提前了50％左右,抓取试验结果与评估分类模型吻合度均大于等于95％,验证了损伤评估分类模型的正确性.研究结果可为番茄多指采摘机械手的设计与开发提供参考.

A method based on image processing, principal component analysis (PCA) and machine learning was proposed to identify pumpkin powdery mildew and improve the identification accuracy of pumpkin powdery mildew. The leaf-disease image was processed by the color feature compositing and detection method,in order to segment the lesion more accurately. Then 20 feature values are extracted, including the texture features, color features and morphological features of the segmented lesions, and the 20-dimensional original feature parameters were simplified to 3-dimensional feature parameters via the PCA method. Finally, three different SVM kernel functions are used to construct the classification model to compare the original feature parameters and principal component feature parameters separately. Experiment results show that the effect of illumination angle and intensity can be effectively eliminated after the super red feature calculation, and that the SVM model based on PCA and polynomial kernel function has a better recognition effect on pumpkin powdery mildew. The recognition rate is 97.3%, which is 3.15% higher than that of the traditional original characteristic parameters. Based on this finding, it can be concluded that this machine learning model can achieve accurate lesions identification.

针对无人机精确植保过程中,果树冠层区域颜色特征和杂草相似度较高、难以分割等问题,采用基于超像素特征向量的果树冠层分割方法,以消除不同杂草特征对树冠分离的干扰,减小农药喷雾区域,节省农药使用量.通过分析无人机采集合成的样本图像在HSV彩色空间上色调与饱和度的分布情况,选取合适的阈值范围,提取样本图像中包含果树冠层与杂草的绿色区域,将提取的绿色区域RGB图像转换生成Lab和HSV彩色空间模型下的图像,然后运用简单的线性迭代聚类(Simple linear iterative clustering,SLIC)超像素分割算法将RGB图像预设分割成250个超像素单元,结合超像素的分割信息与RGB图像、Lab图像、HSV图像以及灰度图,提取超像素单元的特征向量,随机选取25％的超像素样本的特征向量作为SVM分类器的训练集,利用SVM分类器对所有样本进行预测分类,实现果树冠层与杂草分割.将基于超像素特征向量的方法和基于光谱阈值、K-means聚类的2种方法进行对比分析,结果显示,基于超像素特征向量的方法在识别果树冠层位置方面生产者精度为90.83％,在提取果树冠层轮廓上F测度值为87.62％,总体分割性能优于后两种方法.说明,基于超像素特征向量的方法能够较为准确地分割果树冠层与杂草,为实现无人机在果园中精确植保提供重要支撑.

A coefficient CW, which was defined as the ratio of NIR (near infrared) to the red reflected spectral response of the spectrometer, with a standard whiteboard as the measuring object, was introduced to establish a method for calculating height-independent vegetation indices (VIs). Two criteria for designing the spectrometer based on an active light source were proposed to keep CW constant. A designed spectrometer, which was equipped with an active light source, adopting 730 and 810 nm as the central wavelength of detection wavebands, was used to test the Normalized Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI) in wheat fields with two nitrogen application rate levels (NARLs). Twenty test points were selected in each kind of field. Five measuring heights (65, 75, 85, 95, and 105 cm) were set for each test point. The mean and standard deviation of the coefficient of variation (CV) for NDVI in each test point were 3.85% and 1.39% respectively, the corresponding results for RVI were 2.93% and 1.09%. ANOVA showed the measured VIs possessed a significant ability to discriminate the NARLs and had no obvious correlation with the measurement heights. The experimental results verified the feasibility and validity of the method for measuring height-independent VIs.

This study aimed to explore the release rate (RR) of wheat straw nutrients during straw return to a paddy field and examined the possible relationship between wheat stalk shear strength and the content of the remaining components in wheat straw. We used the nylon mesh bag technique to study the decomposition of straw nutrients such as total organic carbon (TOC), total nitrogen (TN), total phosphorus (TP), total potassium (TK), lignin, and cellulose over time. During the time span of 0–90 days, results showed a rapid decomposition rate with a diverse trend under different tillage operations. Furthermore, the decomposition rate was higher under the plough (PRP) conditions than under dry conditions (RP) or water rotation (PR). Moreover, under PRP conditions, the RR of TOC, TK, lignin, and cellulose increased, while the RR of TK was higher than 95% initially and then increased slightly. However, the carbon to nitrogen ratio was first increased and then decreased; similarly the RR of TP first increased and then decreased; a fluctuating pattern was observed for TN. Additionally, we found a strong correlation between wheat stalk shear strength and the remaining contents of lignin, hemicellulose, and cellulose, with R2 ≥ 0.91, which was higher than 0.82 after computing adjustments. Furthermore, the changing trend of nutrients and components and the relationship between shear strength and the content of the remaining components in wheat straw were used to evaluate the release characteristics of nutrients under straw return. The potential effects of the straw shear strength on soil mechanical properties were determined, providing a remarkable opportunity for acquiring nutrients for sustainable application of soil.

To make canopy information measurements in modern standardized apple orchards, a method for canopy information measurements based on unmanned aerial vehicle (UAV) multimodal information is proposed. Using a modern standardized apple orchard as the study object, a visual imaging system on a quadrotor UAV was used to collect canopy images in the apple orchard, and three-dimensional (3D) point-cloud models and vegetation index images of the orchard were generated with Pix4Dmapper software. A row and column detection method based on grayscale projection in orchard index images (RCGP) is proposed. Morphological information measurements of fruit tree canopies based on 3D point-cloud models are established, and a yield prediction model for fruit trees based on the UAV multimodal information is derived. The results are as follows: (1) When the ground sampling distance (GSD) was 2.13–6.69 cm/px, the accuracy of row detection in the orchard using the RCGP method was 100.00%. (2) With RCGP, the average accuracy of column detection based on grayscale images of the normalized green (NG) index was 98.71–100.00%. The hand-measured values of H, SXOY, and V of the fruit tree canopy were compared with those obtained with the UAV. The results showed that the coefficient of determination R2 was the most significant, which was 0.94, 0.94, and 0.91, respectively, and the relative average deviation (RADavg) was minimal, which was 1.72%, 4.33%, and 7.90%, respectively, when the GSD was 2.13 cm/px. Yield prediction was modeled by the back-propagation artificial neural network prediction model using the color and textural characteristic values of fruit tree vegetation indices and the morphological characteristic values of point-cloud models. The R2 value between the predicted yield values and the measured values was 0.83–0.88, and the RAD value was 8.05–9.76%. These results show that the UAV-based canopy information measurement method in apple orchards proposed in this study can be applied to the remote evaluation of canopy 3D morphological information and can yield information about modern standardized orchards, thereby improving the level of orchard informatization. This method is thus valuable for the production management of modern standardized orchards.

A field experiment was conducted to study the effects of different tillage methods, and their interaction on the dynamic changes of straw decomposition rate, mechanical properties, and micro-structure of the stalk. A nylon mesh bag technique was used. An obvious change was observed in the decomposition rate of straw, and its mechanical, and micro-structural properties. The decomposition rate of straw was increased in all tillage treatments. Specifically, it increased consistently in conventional and dry rotary tillage, and sharply in wet rotary tillage. Furthermore, for all tillage, the mechanical properties like shear and bending strengths decreased sharply while compressive strength first decreased linearly and then increased, whereas the micro-structure of wheat straw showed a fluctuating trend, i.e., it changed neither regularly nor consistently over time. Moreover, the micro-structure of the stalk explained the morphological changes to the straw that returned to the field, which may impact the mechanical properties. However, these changes could not explain the degradation trend of straw directly. The findings of the study could be used as a theoretical reference for the design of tillage and harvesting machinery keeping in view soil solidification and compaction dynamics.

[目的]针对黄瓜植株极易染病且部分病害症状相似的问题,利用叶绿素荧光成像系统研究黄瓜植株不同病害区分及早期病害监测的可行性.[方法]采用叶绿素荧光成像系统采集全植株冠层图像,以褐斑病和炭疽病胁迫下的黄瓜植株为试验材料,分析植株生理状态,建立基于叶绿素荧光参数的病害分类和病情诊断模型.首先通过图像的分割得到病斑区域;然后采集植株氮含量、叶绿素含量与荧光参数,并分析其变化趋势;最后基于叶绿素荧光强度和动力学参数对黄瓜褐斑病和炭疽病进行分类和早期监测,分别采用支持向量机(SVM)算法和极端梯度提升(XGBoost)算法对不同程度病害植株进行分类.[结果]与对照植株相比,染病植株叶绿素含量及氮含量呈逐渐下降趋势,最大光化学量子产量(Fv/Fm)、实际光化学效率(ΦPSⅡ)降低,非光化学淬灭(NPQ)、非光化学淬灭系数(qN)和光化学淬灭系数(qP)上升.对于植株的病害与病情分类,利用XGBoost算法进行分类的结果整体较好.对2种病害单独分类的准确率达到90％以上,对2种病害同时分类准确率达到85％以上,对病情和病害种类同时监测的准确率接近80％.[结论]基于叶绿素荧光成像系统监测黄瓜病情和区分其病害种类是可行的,具有良好的应用前景.