Shaded Leaf Research Articles

ASYMMETRIC LEAVES1 promotes leaf hyponasty in Arabidopsis by light-mediated auxin signaling.

In plants, balancing growth and environmental responses is crucial for maximizing fitness. Close proximity among plants and canopy shade, which negatively impacts reproduction, elicits morphological adjustments such as hypocotyl growth and leaf hyponasty, mainly through changes in light quality and auxin levels. However, how auxin, synthesized from a shaded leaf blade, distally induces elongation of hypocotyl and petiole cells remains to be elucidated. We demonstrated that ASYMMETRIC LEAVES1 (AS1) promotes leaf hyponasty through the regulation of auxin biosynthesis, polar auxin transport, and auxin signaling genes in Arabidopsis (Arabidopsis thaliana). AS1 overexpression leads to elongation of the abaxial petiole cells with auxin accumulation in the petiole, resulting in hyponastic growth, which is abolished by the application of an auxin transport inhibitor to the leaf blade. In addition, the as1 mutant exhibits reduced hypocotyl growth under shade conditions. We observed that AS1 protein accumulates in the nucleus in response to shade or far-red light. Chromatin immunoprecipitation analysis identified the association of AS1 with the promoters of YUCCA8 (YUC8) and INDOLE-3-ACETIC ACID INDUCIBLE 19 (IAA19). In addition, AS1 forms complexes with PHYTOCHROME INTERACTING FACTORs in the nucleus and synergistically induces YUC8 and IAA19 expression. Our findings suggest that AS1 plays a crucial role in facilitating phenotypic plasticity to the surroundings by connecting light and phytohormone action.

Excellent Canopy Structure in Soybeans Can Improve Their Photosynthetic Performance and Increase Yield

In the maize-soybean intercropping system, varying degrees of maize leaf shading are an important factor that reduces the uniformity of light penetration within the soybean canopy, altering the soybean canopy structure. Quantitative analysis of the relationship between the soybean canopy structure and canopy photosynthesis helps with breeding shade-tolerant soybean varieties for intercropping systems. This study examined the canopy structure and photosynthesis of intercropped soybeans during the shading stress period (28 days before the corn harvest), the high light adaptation period (15 days after the corn harvest), and the recovery period (35 and 55 days after the corn harvest), using a field high-throughput phenotyping platform and a plant gas exchange testing system (CAPTS). Additionally, indoor shading experiments were conducted for validation. The results indicate that shade-tolerant soybean varieties (STV varieties) have significantly higher yields than shade-sensitive soybean varieties (SSV varieties). This is attributable to the STV varieties having a larger top area, lateral width, and lateral external rectangular area. Compared to the SSV varieties, the four top areas of the STV varieties are, on average, 52.09%, 72.05%, and 61.37% higher during the shading stress, high light adaptation, and recovery periods, respectively. Furthermore, the average maximum growth rates (GRs) for the side mean width (SMW) and side rectangle area (SRA) of the STV varieties are 62.92% and 22.13% in the field, and 83.36% and 55.53% in the indoor environment, respectively. This results in a lower canopy overlap in STV varieties, leading to a more uniform light distribution within the canopy, which is reflected in higher photosynthetic rates (Pn), apparent quantum efficiency, and whole-leaf photosynthetic potential (WLPP) for the STV varieties, thereby enhancing their adaptability to shading stress. Above-ground dry matter accumulation was higher in STV varieties, with more assimilates stored in the source and sink, promoting assimilate accumulation in the grains. These results provide new insights into how the superior canopy structure and photosynthesis of shade-tolerant soybean varieties contribute to increased yield.

MONTPEL: A multi-component Penman-Monteith energy balance model

Mechanistic modelling is gradually replacing empiricism in crop models, focusing on leaf-level physiological processes. This shift necessitates simulating crop surface temperature at infra-canopy sub-daily scales but many crop models still rely on empirical formulations for canopy temperature estimation, typically on a daily basis. We developed MONTPEL, a multi-component Penman-Monteith model that allows simulating the crop energy balance with flexible canopy representations (“BigLeaf” vs. “Layered”, “Lumped” vs. “Sunlit-Shaded”) and accounts for atmospheric stability conditions. We analyzed the model behavior, sensitivity and accuracy, using measurements from four wheat (Triticum aestivum L.) experiments conducted under varying pedoclimatic and water stress conditions. Measurements included hourly energy balance terms (total net radiation, soil heat flux, sensible and latent energy fluxes), hourly temperature of the canopy surface or of leaves at different depths inside the canopy, and sunlit and shaded leaf temperatures around solar noon at different dates. MONTPEL reproduced the measured energy balance terms with a root mean square error (RMSE) between 21 and 87 Wm-2 and a coefficient of determination (R²) exceeding 0.65. The model's accuracy in simulating canopy temperature, with RMSE ≤ 2.2 °C and R² ≥ 0.92, remained consistent regardless of measurement scale. Adjusting the aerodynamic resistance for atmospheric stability minimized simulated canopy temperature errors, notably in semi-arid conditions. Crop latent energy flux and temperature were most sensitive to the maximal stomatal conductance (gs,max) parameter. However, using a single gs,max value across the simulated experiments yielded satisfactory results, suggesting a weak sensitivity to the temporal and site-to-site variability of gs,max. Distinguishing sunlit from shaded canopy fractions systematically resulted in lower latent energy fluxes compared to “Lumped” canopy representation results. Analysis identified limitations in the multi-component approach, particularly an unrealistic uniform temperature shift across leaf layers when soil surface temperature changes.

Elevated aerosol enhances plant water-use efficiency by increasing carbon uptake while reducing water loss.

Aerosols could significantly influence ecosystem carbon and water fluxes, potentially altering their interconnected dynamics, typically characterized by water-use efficiency (WUE). However, our understanding of the underlying ecophysiological mechanisms remains limited due to insufficient field observations. We conducted 4-yr measurements of leaf photosynthesis and transpiration, as well as 3-yr measurements of stem growth (SG) and sap flow of poplar trees exposed to natural aerosol fluctuation, to elucidate aerosol's impact on plant WUE. We found that aerosol improved sun leaf WUE mainly because a sharp decline in photosynthetically active radiation (PAR) inhibited its transpiration, while photosynthesis was less affected, as the negative effect induced by declined PAR was offset by the positive effect induced by low leaf vapor pressure deficit (VPDleaf). Conversely, diffuse radiation fertilization (DRF) effect stimulated shade leaf photosynthesis with minimal impact on transpiration, leading to an improved WUE. The responses were further verified by a strong DRF on SG and a decrease in sap flow due to the suppresses in total radiation and VPD. Our field observations indicate that, contrary to the commonly assumed coupling response, carbon uptake and water use exhibited dissimilar reactions to aerosol pollution, ultimately enhancing WUE at the leaf and canopy level.

A Lightweight and Dynamic Feature Aggregation Method for Cotton Field Weed Detection Based on Enhanced YOLOv8

Weed detection is closely related to agricultural production, but often faces the problems of leaf shading and limited computational resources. Therefore, this study proposes an improved weed detection algorithm based on YOLOv8. Firstly, the Dilated Feature Integration Block is designed to improve the feature extraction in the backbone network by introducing large kernel convolution and multi-scale dilation convolution, which utilizes information from different scales and levels. Secondly, to solve the problem of a large number of parameters in the feature fusion process of the Path Aggregation Feature Pyramid Network, a new feature fusion architecture multi-scale feature interaction network is designed, which achieves the high-level semantic information to guide the low-level semantic information through the attention mechanism. Finally, we propose a Dynamic Feature Aggregation Head to solve the problem that the YOLOv8 detection head cannot dynamically focus on important features. Comprehensive experiments on two publicly accessible datasets show that the proposed model outperforms the benchmark model, with mAP50 and mAP75 improving by 4.7% and 5.0%, and 5.3% and 3.3%, respectively, whereas the number of model parameters is only 6.62 M. This study illustrates the utility potential of the algorithm for weed detection in cotton fields, marking a significant advancement of artificial intelligence in agriculture.

Improved Weed Detection in Cotton Fields Using Enhanced YOLOv8s with Modified Feature Extraction Modules

Weed detection plays a crucial role in enhancing cotton agricultural productivity. However, the detection process is subject to challenges such as target scale diversity and loss of leaf symmetry due to leaf shading. Hence, this research presents an enhanced model, EY8-MFEM, for detecting weeds in cotton fields. Firstly, the ALGA module is proposed, which combines the local and global information of feature maps through weighting operations to better focus on the spatial information of feature maps. Following this, the C2F-ALGA module was developed to augment the feature extraction capability of the underlying backbone network. Secondly, the MDPM module is proposed to generate attention matrices by capturing the horizontal and vertical information of feature maps, reducing duplicate information in the feature maps. Finally, we will replace the upsampling module of YOLOv8 with the CARAFE module to provide better upsampling performance. Extensive experiments on two publicly available datasets showed that the F1, mAP50 and mAP75 metrics improved by 1.2%, 5.1%, 2.9% and 3.8%, 1.3%, 2.2%, respectively, compared to the baseline model. This study showcases the algorithm’s potential for practical applications in weed detection within cotton fields, promoting the significant development of artificial intelligence in the field of agriculture.

Quantifying foliar trait variation and covariation in sun and shade leaves using leaf spectroscopy in eastern North America

Characterizing foliar trait variation in sun and shade leaves can provide insights into inter- and intra-species resource use strategies and plant response to environmental change. However, datasets with records of multiple foliar traits from the same individual and including shade leaves are sparse, which limits our ability to investigate trait-trait, trait-environment relationships and trait coordination in both sun and shade leaves. We presented a comprehensive dataset of 15 foliar traits from sun and shade leaves sampled with leaf spectroscopy, including 424 individuals of 110 plant species from 19 sites across eastern North America. We investigated trait variation, covariation, scaling relationships with leaf mass, and the effects of environment, canopy position, and taxonomy on trait expression. Generally, sun leaves had higher leaf mass per area, nonstructural carbohydrates and total phenolics, lower mass-based chlorophyll a + b, carotenoids, phosphorus, and potassium, but exhibited species-specific characteristics. Covariation between sun and shade leaf traits, and trait-environment relationships were overall consistent across species. The main dimensions of foliar trait variation in seed plants were revealed including leaf economics traits, photosynthetic pigments, defense, and structural traits. Taxonomy and canopy position collectively explained most of the foliar trait variation. This study highlights the importance of including intra-individual and intra-specific trait variation to improve our understanding of ecosystem functions. Our findings have implications for efficient field sampling, and trait mapping with remote sensing.

DSW-YOLO: A detection method for ground-planted strawberry fruits under different occlusion levels

The problems of stem and leaf shading, fruit overlapping, and different ripening stages pose a great challenge to picking robots, which are usual for the ground-planted strawberries in field. To achieve accurate detection of ripe strawberries and their occlusion levels, a DSW-YOLO (DCNv3-SA-WIoU-YOLO) network model is proposed based on the YOLOv7 network model. The model is designed with a DCN-ELAN module by introducing DCNv3 (Deformable Convolution v3) in the ELAN module to improve the feature extraction capability of the network for non-regular targets. Meanwhile, the SA (Shuffle Attention) mechanism is introduced at the end of the backbone network to enhance the extraction of valid information and improve detection accuracy. Optimizing the bounding box loss function to WIoU v3 (Wise-IoU v3) accelerates the convergence speed of the network. The proposed network model was tested using the ground-planted strawberry dataset, and the experimental results show that the P, R, and mAP@.5 of the improved network model are 82.8 %, 82.1 %, and 86.7 %, respectively, which are improved by 5.0 %, 1.7 %, and 2.2 %, respectively, compared with the YOLOv7 model. By comparing with other classical target detection algorithms, it is verified that the proposed model has advantages in terms of accuracy and model size. Finally, the DSW-YOLO model was deployed on Jetson Xavier NX of a strawberry picking robot, and visual recognition tests were conducted in field. The detection time of a single image is 92 ms, which satisfies the speed requirement of robot picking, and the detection results verify that the DSW-YOLO network model can quickly and accurately detect ripe strawberry fruits under different occlusion levels in a complex field environment, which provides a theoretical basis for the picking robot to achieve accurate positioning and efficient picking.

Object localization methodology in occluded agricultural environments through deep learning and active sensing

The predominance of branch and leaf shade in agricultural environments presents a barrier for accurate target recognition. Particularly for picking robots, precise localization of the picking object is essential. For this purpose, this paper proposes detection and localization methods based on deep learning and active sensing for harvesting robots in real-world environments with occlusion and varying lighting conditions. Using a deep learning network, the detection method firstly extracts the peduncle and fruit regions; the fruit region is then used to calculate the occlusion rate and the offset distance of the peduncle relative to the fruit. With such information, the robot arm adjusts the camera's field of view to perform multiple recognitions until the confidence is satisfied. Furthermore, to solve the picking problem caused by the peduncle's random tilting, this paper proposes a method to calculate the peduncle's tilt angle for controlling the end-effector to make the corresponding angle rotation. The robot arm and its end-effector are directed to complete the harvesting with the picking point location and tilt angle. In this study, data collection, detection and picking tests were implemented in the field, the results indicated that the method obtained an average successful picking rate of 90% after 300 trials, the error between the estimated occlusion ratio and the genuine value is 16% in average, and the active sensing method has improved the confidence score in occluded situations by over 50%. The proposed active methods have a 33% increase in precision and a 43% increase in efficiency compared to constant methods.

YOLOv7-Plum: Advancing Plum Fruit Detection in Natural Environments with Deep Learning.

The plum is a kind of delicious and common fruit with high edible value and nutritional value. The accurate and effective detection of plum fruit is the key to fruit number counting and pest and disease early warning. However, the actual plum orchard environment is complex, and the detection of plum fruits has many problems, such as leaf shading and fruit overlapping. The traditional method of manually estimating the number of fruits and the presence of pests and diseases used in the plum growing industry has disadvantages, such as low efficiency, a high cost, and low accuracy. To detect plum fruits quickly and accurately in a complex orchard environment, this paper proposes an efficient plum fruit detection model based on an improved You Only Look Once version 7(YOLOv7). First, different devices were used to capture high-resolution images of plum fruits growing under natural conditions in a plum orchard in Gulin County, Sichuan Province, and a dataset for plum fruit detection was formed after the manual screening, data enhancement, and annotation. Based on the dataset, this paper chose YOLOv7 as the base model, introduced the Convolutional Block Attention Module (CBAM) attention mechanism in YOLOv7, used Cross Stage Partial Spatial Pyramid Pooling-Fast (CSPSPPF) instead of Cross Stage Partial Spatial Pyramid Pooling(CSPSPP) in the network, and used bilinear interpolation to replace the nearest neighbor interpolation in the original network upsampling module to form the improved target detection algorithm YOLOv7-plum. The tested YOLOv7-plum model achieved an average precision (AP) value of 94.91%, which was a 2.03% improvement compared to the YOLOv7 model. In order to verify the effectiveness of the YOLOv7-plum algorithm, this paper evaluated the performance of the algorithm through ablation experiments, statistical analysis, etc. The experimental results showed that the method proposed in this study could better achieve plum fruit detection in complex backgrounds, which helped to promote the development of intelligent cultivation in the plum industry.

YOLOv5-ASFF: A Multistage Strawberry Detection Algorithm Based on Improved YOLOv5

The smart farm is currently a hot topic in the agricultural industry. Due to the complex field environment, the intelligent monitoring model applicable to this environment requires high hardware performance, and there are difficulties in realizing real-time detection of ripe strawberries on a small automatic picking robot, etc. This research proposes a real-time multistage strawberry detection algorithm YOLOv5-ASFF based on improved YOLOv5. Through the introduction of the ASFF (adaptive spatial feature fusion) module into YOLOv5, the network can adaptively learn the fused spatial weights of strawberry feature maps at each scale as a way to fully obtain the image feature information of strawberries. To verify the superiority and availability of YOLOv5-ASFF, a strawberry dataset containing a variety of complex scenarios, including leaf shading, overlapping fruit, and dense fruit, was constructed in this experiment. The method achieved 91.86% and 88.03% for mAP and F1, respectively, and 98.77% for AP of mature-stage strawberries, showing strong robustness and generalization ability, better than SSD, YOLOv3, YOLOv4, and YOLOv5s. The YOLOv5-ASFF algorithm can overcome the influence of complex field environments and improve the detection of strawberries under dense distribution and shading conditions, and the method can provide technical support for monitoring yield estimation and harvest planning in intelligent strawberry field management.

Experimental study of the effect of fallen leaf shading on the polarization characteristics of photovoltaic modules

Coverings have an important impact on the output and safety of photovoltaic (PV) modules. However, due to the complex on-site environment and difficult identification, it is difficult to obtain timely and effective cleaning. In this paper, the optical polarization characteristics of fallen leaf are studied to find the difference for the PV modules with or without fallen leaves covered, which is one of the typical coverings, based on the basic theory of polarization imaging. Firstly, a polarimetric imaging platform with a SALSA camera for PV modules is built. Then, several experiments are conducted and the corresponding images of PV modules shaded by heather or camphor leaves are obtained. Finally, shaded by the impacts on the polarization characteristics of the PV module from fallen leaves, the waveband of the incident light, and the observation angle are analyzed. The results show that the optical polarization characteristics of PV modules at bands of 420 nm and 546 nm are significantly different from others, wherever there existed shading leaves or not; The degree of polarization decreases and then increases with the observation angle from 0° to 70° at 9:00–12:00 a.m. on sunny days; At 0° observation angle, the stokes parameter S_{0} of PV modules with or without fallen leaves has the greatest difference. HighlightsThe influence of fallen leaves on the polarization characteristics of PV modules is studied.The polarization characteristic parameter with the largest difference between the PV modules with and without fallen leaves is obtained.This parameter is more conducive to detecting fallen leaves on the surface of PV modules.

Effects of stereoscopic traction on photosynthetic characteristics, yield, and quality of Codonopsis pilosula under organic cultivation

To solve the serious problem of stem and leaf shading in the middle and late stage of traditional flat planting of Codonopsis pilosula, this study analyzed the effects of different stereoscopic traction heights on the photosynthetic characteristics and growth of C. pilosula and explored the optimal traction height to improve the yield and quality of C. pilosula. The experiment designed three stereo-scopic traction heights [H1(60 cm), H2(90 cm), and H3(120 cm)] with natural growth without traction as the control(CK). The results showed that the increase in stereoscopic traction heights broadened the growth space of stems and leaves of C. pilosula, enhanced the ventilation effect, significantly increased the average daily net photosynthetic rate of C. pilosula, promoted the absorption of intercellular CO_2, decreased the transpiration rate, and reduced the evaporation of water. Moreover, it effectively avoided the problem of weakened photosynthesis, maintained the carbon balance of individual plants, and promoted the growth and development of the C. pilosula roots. In terms of the seed yield of C. pilosula, it was ranked as H2>H1>H3>CK. To be specific, H1 increased by 213.41% compared with CK, H2 increased by 282.43% compared with CK, and H3 increased by 133.95% compared with CK. The yield and quality of C. pilosula were the highest in the H3 treatment group, with the fresh yield of 6 858.33 kg·hm~(-2), 50.59% higher than CK, dry yield of 2 398.33 kg·hm~(-2), 76.54% higher than CK, and lobetyolin content of 0.56 mg·g~(-1), 45.22% higher than CK. Therefore, the stereoscopic traction height has a great influence on the photosynthetic characteristics, yield, and quality of C. pilosula. Particularly, the yield and quality of C. pilosula can be optimized and improved in the traction height treatment of H3(120 cm). This planting method is worth popularizing and applying in the cultivated management of C. pilosula.

An edge-guided method to fruit segmentation in complex environments

Accurate detection and segmentation of fruit is a key factor in the development of smart farming. Problems such as light variation, fruit overlap and leaf shading create a complex environment in orchards and have a significant impact on the development of smart farming. Many current deep learning-based segmentation methods do not make full use of edge information, resulting in inadequate sharpening of the fruit edges obtained from segmentation. To address this problem, an edge-guided based fruit segmentation method (EdgeSegNet) in complex environments is proposed by us. The method first performs feature extraction through the ResNet model as the backbone network, then integrates and refines the high-level semantic and spatial information through the Global Localization Module (GLM) and localizes potential targets in the target region with the help of the proposed Multi-Scale Localization Block (MSLB). Then Boundary Aware Module (BAM) sharpen the edges of potential targets by integrating the feature information of high and low layers, and finally get the accurate segmented image. The principle of the model is blurred positioning, precise sharpening, edge guiding. The experimental results showed that the method achieved an average MIoU of 0.909 and 0.942 on the apple and peach datasets of three different sizes, large, medium and small, respectively, outperforming several other state-of-the-art models in terms of accuracy and complexity as well as inference time.

YOLO-Tea: A Tea Disease Detection Model Improved by YOLOv5

Diseases and insect pests of tea leaves cause huge economic losses to the tea industry every year, so the accurate identification of them is significant. Convolutional neural networks (CNNs) can automatically extract features from images of tea leaves suffering from insect and disease infestation. However, photographs of tea tree leaves taken in a natural environment have problems such as leaf shading, illumination, and small-sized objects. Affected by these problems, traditional CNNs cannot have a satisfactory recognition performance. To address this challenge, we propose YOLO-Tea, an improved model based on You Only Look Once version 5 (YOLOv5). Firstly, we integrated self-attention and convolution (ACmix), and convolutional block attention module (CBAM) to YOLOv5 to allow our proposed model to better focus on tea tree leaf diseases and insect pests. Secondly, to enhance the feature extraction capability of our model, we replaced the spatial pyramid pooling fast (SPPF) module in the original YOLOv5 with the receptive field block (RFB) module. Finally, we reduced the resource consumption of our model by incorporating a global context network (GCNet). This is essential especially when the model operates on resource-constrained edge devices. When compared to YOLOv5s, our proposed YOLO-Tea improved by 0.3%–15.0% over all test data. YOLO-Tea’s AP0.5, APTLB, and APGMB outperformed Faster R-CNN and SSD by 5.5%, 1.8%, 7.0% and 7.7%, 7.8%, 5.2%. YOLO-Tea has shown its promising potential to be applied in real-world tree disease detection systems.

Verification of improved YOLOX model in detection of greenhouse crop organs: Considering tomato as example

Efficient and accurate monitoring crop growth is an important prerequisite for modern greenhouse crop growth control and management. However, it is still challenging to achieve efficient in coordinated detection of tomato organs. Therefore, in this study, a multiobjective detection model was established to detect the blossom, fruit, and fruit color of crop population under dense planting, leaf shading, fruit overlap, and interference from light change in greenhouses. Specifically, sample equalization, random transformation, and mosaic enhancement were applied to identify multiple targets and small-sample images. A new detection network, YOLOX, was used, which introduces a decoupled head structure and redesigns a detection head according to the anchor-free concept to effectively improve the recognition of small-target occlusion. An efficient tomato flower and fruit recognition model was constructed based on a two-step fine-tuned multi-round optimization strategy training. The results showed that the YOLOX target detection model improved the average recognition accuracy (mAP) by 52.49–10.75 %, was lighter, was only 14 % original YOLOv4, and improved the detection speed by 25.53 FPS compared to six main target detection algorithms. It can be used as a method for the collaborative recognition of tomato flowers and fruits. Accordingly, two sets of lightweight detection networks with different performances were designed according to different agricultural scenarios. YOLOXMOB modeled with a small size (only 34.2 MB), high mAP of 81.39 %, and detection speed of 61.85 FPS. It is recommended for use as a deployment model for mobile devices. YOLOXPC, with a deep network layer and strong performance, achieved 86.16 % mAP, and is recommended for monitoring and dispatching computers. This study puts forward a set of multiobjective detection model scheme adapted to the real environment of greenhouse, which provides ideas for the construction of a highly intelligent monitoring system platform for the agricultural Internet of Things.

MIX-NET: Deep Learning-Based Point Cloud Processing Method for Segmentation and Occlusion Leaf Restoration of Seedlings

In this paper, a novel point cloud segmentation and completion framework is proposed to achieve high-quality leaf area measurement of melon seedlings. In particular, the input of our algorithm is the point cloud data collected by an Azure Kinect camera from the top view of the seedlings, and our method can enhance measurement accuracy from two aspects based on the acquired data. On the one hand, we propose a neighborhood space-constrained method to effectively filter out the hover points and outlier noise of the point cloud, which can enhance the quality of the point cloud data significantly. On the other hand, by leveraging the purely linear mixer mechanism, a new network named MIX-Net is developed to achieve segmentation and completion of the point cloud simultaneously. Different from previous methods that separate these two tasks, the proposed network can better balance these two tasks in a more definite and effective way, leading to satisfactory performance on these two tasks. The experimental results prove that our methods can outperform other competitors and provide more accurate measurement results. Specifically, for the seedling segmentation task, our method can obtain a 3.1% and 1.7% performance gain compared with PointNet++ and DGCNN, respectively. Meanwhile, the R2 of leaf area measurement improved from 0.87 to 0.93 and MSE decreased from 2.64 to 2.26 after leaf shading completion.

A Real-Time Detection Algorithm for Sweet Cherry Fruit Maturity Based on YOLOX in the Natural Environment

Fast, accurate, and non-destructive large-scale detection of sweet cherry ripeness is the key to determining the optimal harvesting period and accurate grading by ripeness. Due to the complexity and variability of the orchard environment and the multi-scale, obscured, and even overlapping fruit, there are still problems of low detection accuracy even using the mainstream algorithm YOLOX in the absence of a large amount of tagging data. In this paper, we proposed an improved YOLOX target detection algorithm to quickly and accurately detect sweet cherry ripeness categories in complex environments. Firstly, we took a total of 2400 high-resolution images of immature, semi-ripe, and ripe sweet cherries in an orchard in Hanyuan County, Sichuan Province, including complex environments such as sunny days, cloudy days, branch and leaf shading, fruit overlapping, distant views, and similar colors of green fruits and leaves, and formed a dataset dedicated to sweet cherry ripeness detection by manually labeling 36068 samples, named SweetCherry. On this basis, an improved YOLOX target detection algorithm YOLOX-EIoU-CBAM was proposed, which embedded the Convolutional Block Attention Module (CBAM) between the backbone and neck of the YOLOX model to improve the model’s attention to different channels, spaces capability, and replaced the original bounding box loss function of the YOLOX model with Efficient IoU (EIoU) loss to make the regression of the prediction box more accurate. Finally, we validated the feasibility and reliability of the YOLOX-EIoU-CBAM network on the SweetCherry dataset. The experimental results showed that the method in this paper significantly outperforms the traditional Faster R-CNN and SSD300 algorithms in terms of mean Average Precision (mAP), recall, model size, and single-image inference time. Compared with the YOLOX model, the mAP of this method is improved by 4.12%, recall is improved by 4.6%, F-score is improved by 2.34%, while model size and single-image inference time remain basically comparable. The method in this paper can cope well with complex backgrounds such as fruit overlap, branch and leaf occlusion, and can provide a data base and technical reference for other similar target detection problems.

Narrow but robust advantages in two-big-leaf light use efficiency models over big-leaf light use efficiency models at ecosystem level

This study aims to (1) investigate whether two-big-leaf light use efficiency (LUE) models (TL) outperform big-leaf LUE models (BL) by incorporating different gross primary productivity (GPP) responses in sunlit and shaded leaves; (2) explore the robustness of using the leaf area index (LAI), clumping index (Ω) and spherical leaf angle distribution to partition canopies into sunlit and shaded leaves across canopy architectures; (3) identify optimal light response forms in LUE models. To exclude influences of drivers of GPP other than radiation, we collected various formulations of GPP response functions to temperature, vapor pressure deficit, CO2, soil water supply, light intensity and cloudiness index to construct 5600 BLs and 1120 TLs. These models were evaluated at 196 globally-distributed eddy covariance sites from the FLUXNET observational network using the Nash-Sutcliffe model efficiency (NSE), root mean squared error and Bayesian information criterion. Across all sites, the best big-leaf model (BL*; NSE=0.82) was statistically equal to the best TL (TL*; NSE=0.84). However, daily dynamics in GPP under hot and dry conditions were best described using TL* in 17% of sites, highlighting the local importance in separating sunlit and shaded leaves. Across approaches to represent effective LAI, the best approach relies on using normalized difference vegetation index with a spherical or flexible leaf angle distribution across sites rather than satellite LAI and Ω. We also observed similar performance between non-rectangular hyperbola and reciprocal light response functions across TLs. Models degrade when the maximum LUE is not differentiated between sunlit and shaded leaves, but not when light saturation levels are the same. Despite functional differences, the best five TLs agree in a larger contribution of shaded leaf area to total GPP, resulting from higher LAI and LUE. Overall, these results suggest marginal but robust selection of TL compared to BL.

Decadal nitrogen addition increases divergence in intrinsic water-use efficiency between sapling and mature larch plantations

Intrinsic water-use efficiency (iWUE) is the ratio of the photosynthetic assimilation rate to stomatal conductance and is tightly linked with tree growth and mortality. Increasing atmospheric nitrogen (N) deposition has been recognized to have a profound influence on ecosystem functioning, especially in typically N- and water-limited temperate forests. However, the effects of N deposition on leaf-derived iWUE estimates and the underlying physiological mechanisms remain poorly understood, as well as effects related to the developmental stage of temperate plantation forests. Using leaf stable carbon and oxygen isotope (δ13C and δ18O) approaches, we investigated iWUE of sapling and mature larch plantations subjected to decadal N addition in northern China. Results showed that saplings had significantly higher leaf iWUE compared to mature individuals. Long-term N addition significantly increased iWUE by 6.85% in the sapling stand, with no remarkable changes occurred in the mature stand. In saplings, the N-addition-induced increase in iWUE could be attributed to enhanced photosynthesis (illustrated by higher leaf N concentration), while leaf shading may cause lower leaf photosynthesis regardless of N level in mature trees. The higher and temporal stable leaf iWUE in saplings imply conservative water-use strategy, whereas the opposites in mature trees imply profligate water-use strategy. Thus, the divergent responses of iWUE to decadal N addition should be considered for sustainable forest development under increasing atmospheric N deposition conditions.