PDDD-PreTrain:A Series of Commonly Used Pre-Trained Models Support Image-Based Plant Disease Diagnosis

Plant diseases threaten global food security by reducing crop yield;thus,diagnosing plant diseases is critical to agricultural production.Artificial intelligence technologies gradually replace traditional plant disease diagnosis methods due to their time-consuming,costly,inefficient,and subjective disadvantages.As a mainstream AI method,deep learning has substantially improved plant disease detection and diagnosis for precision agriculture.In the meantime,most of the existing plant disease diagnosis methods usually adopt a pre-trained deep learning model to support diagnosing diseased leaves.However,the commonly used pre-trained models are from the computer vision dataset,not the botany dataset,which barely provides the pre-trained models sufficient domain knowledge about plant disease.Furthermore,this pre-trained way makes the final diagnosis model more difficult to distinguish between different plant diseases and lowers the diagnostic precision.To address this issue,we propose a series of commonly used pre-trained models based on plant disease images to promote the performance of disease diagnosis.In addition,we have experimented with the plant disease pre-trained model on plant disease diagnosis tasks such as plant disease identification,plant disease detection,plant disease segmentation,and other subtasks.The extended experiments prove that the plant disease pre-trained model can achieve higher accuracy than the existing pre-trained model with less training time,thereby supporting the better diagnosis of plant diseases.In addition,our pre-trained models will be open-sourced at https://pd.samlab.cn/and Zenodo platform https://doi.org/10.5281/zenodo.7856293.

Knowledge Distillation Facilitates the Lightweight and Efficient Plant Diseases Detection Model

Plant disease diagnosis in time can inhibit the spread of the disease and prevent a large-scale drop in production,which benefits food production.Object detection-based plant disease diagnosis methods have attracted widespread attention due to their accuracy in classifying and locating diseases.However,existing methods are still limited to single crop disease diagnosis.More importantly,the existing model has a large number of parameters,which is not conducive to deploying it to agricultural mobile devices.Nonetheless,reducing the number of model parameters tends to cause a decrease in model accuracy.To solve these problems,we propose a plant disease detection method based on knowledge distillation to achieve a lightweight and efficient diagnosis of multiple diseases across multiple crops.In detail,we design 2 strategies to build 4 different lightweight models as student models:the YOLOR-Light-v1,YOLOR-Light-v2,Mobile-YOLOR-v1,and Mobile-YOLOR-v2 models,and adopt the YOLOR model as the teacher model.We develop a multistage knowledge distillation method to improve lightweight model performance,achieving 60.4%mAP@.5 in the PlantDoc dataset with small model parameters,outperforming existing methods.Overall,the multistage knowledge distillation technique can make the model lighter while maintaining high accuracy.Not only that,the technique can be extended to other tasks,such as image classification and image segmentation,to obtain automated plant disease diagnostic models with a wider range of lightweight applicability in smart agriculture.Our code is available at https://github.com/QDH/MSKD.

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.This drawback obstructed the accurate extraction of phenotypic parameters.Hence,this study presented a solution for incomplete flowering Chinese Cabbage point clouds using Point Fractal Network-based techniques.The study performed experiments on flowering Chinese Cabbage by constructing a point cloud dataset of their leaves and training the network.The findings demonstrated that our network is stable and robust,as it can effectively complete diverse leaf point cloud morphologies,missing ratios,and multi-missing scenarios.A novel framework is presented for 3D plant reconstruction using a single-view RGB-D(Red,Green,Blue and Depth)image.This method leveraged deep learning to complete localized incomplete leaf point clouds acquired by RGB-D cameras under occlusion conditions.Additionally,the extracted leaf area parameters,based on triangular mesh,were compared with the measured values.The outcomes revealed that prior to the point cloud completion,the R^(2)value of the flowering Chinese Cabbage's estimated leaf area(in comparison to the standard reference value)was 0.9162.The root mean square error(RMSE)was 15.88 cm^(2),and the average relative error was 22.11%.However,post-completion,the estimated value of leaf area witnessed a significant improvement,with an R^(2)of 0.9637,an RMSE of 6.79 cm^(2),and average relative error of 8.82%.The accuracy of estimating the phenotypic parameters has been enhanced significantly,enabling efficient retrieval of such parameters.This development offers a fresh perspective for non-destructive identification of plant phenotypes.

A Combined Genomics and Phenomics Approach is Needed to Boost Breeding in Sugarcane

Sugarcane is a major food and bioenergy crop globally.It pro-duces~80%of sugar consumed worldwide,with Brazil and India together accounting for 61%of world sugarcane produc-tion in 2021[1].Globally,sugarcane is the 5th largest crop by production value and acreage,and it is also the second largest bioenergy crop[1,2].Modern sugarcane is an interspecific hybrid(Saccharum species hybrid)of wild progenitor species Saccharum officinarum(2n=80;x=10)andSaccharumspon-taneum(2n=40 to 130;x=8)[3].This genetically complex polyploid crop with varied chromosome numbers(100 to 130)has one of the largest genomes(~10 kb)among plants,making sugarcane breeding considerably slow and challenging.

Quantification of Photosynthetic Pigments in Neopyropia yezoensis Using Hyperspectral Imagery

Phycobilisomes and chlorophyll-a(Chla)play important roles in the photosynthetic physiology of red macroalgae and serve as the primary light-harvesting antennae and reaction center for photosystem Ⅱ.Neopyropia is an economically important red macroalga widely cultivated in East Asian countries.The contents and ratios of 3 main phycobiliproteins and Chla are visible traits to evaluate its commercial quality.The traditional analytical methods used for measuring these components have several limitations.Therefore,a high-throughput,nondestructive,optical method based on hyperspectral imaging technology was developed for phenotyping the pigments phycoerythrin(PE),phycocyanin(PC),allophycocyanin(APC),and Chla in Neopyropia thalli in this study.The average spectra from the region of interest were collected at wavelengths ranging from 400 to 1000 nm using a hyperspectral camera.Following different preprocessing methods,2 machine learning methods,partial least squares regression(PLSR)and support vector machine regression(SVR),were performed to establish the best prediction models for PE,PC,APC,and Chla contents.The prediction results showed that the PLSR model performed the best for PE(R_(Test^(2))=0.96,MAPE=8.31%,RPD=5.21)and the SVR model performed the best for PC(R_(Test^(2))=0.94,MAPE=7.18%,RPD=4.16)and APC(R_(Test^(2))=0.84,MAPE=18.25%,RPD=2.53).Two models(PLSR and SVR)performed almost the same for Chla(PLSR:R_(Test^(2))=0.92,MAPE=12.77%,RPD=3.61;SVR:R_(Test^(2))=0.93,MAPE=13.51%,RPD=3.60).Further validation of the optimal models was performed using field-collected samples,and the result demonstrated satisfactory robustness and accuracy.The distribution of PE,PC,APC,and Chla contents within a thallus was visualized according to the optimal prediction models.The results showed that hyperspectral imaging technology was effective for fast,accurate,and noninvasive phenotyping of the PE,PC,APC,and Chla contents of Neopyropia in situ.This could benefit the efficiency of macroalgae breeding,phenomics research,and other related applications.

Application of Improved UNet and EnglightenGAN for Segmentation and Reconstruction of In Situ Roots

The root is an important organ for crops to absorb water and nutrients.Complete and accurate acquisition of root phenotype information is important in root phenomics research.The in situ root research method can obtain root images without destroying the roots.In the image,some of the roots are vulnerable to soil shading,which severely fractures the root system and diminishes its structural integrity.The methods of ensuring the integrity of in situ root identification and establishing in situ root image phenotypic restoration remain to be explored.Therefore,based on the in situ root image of cotton,this study proposes a root segmentation and reconstruction strategy,improves the UNet model,and achieves precise segmentation.It also adjusts the weight parameters of EnlightenGAN to achieve complete reconstruction and employs transfer learning to implement enhanced segmentation using the results of the former two.The research results show that the improved UNet model has an accuracy of 99.2%,mIOU of 87.03%,and F1 of 92.63%.The root reconstructed by EnlightenGAN after direct segmentation has an effective reconstruction ratio of 92.46%.This study enables a transition from supervised to unsupervised training of root system reconstruction by designing a combination strategy of segmentation and reconstruction network.It achieves the integrity restoration of in situ root system pictures and offers a fresh approach to studying the phenotypic of in situ root systems,also realizes the restoration of the integrity of the in situ root image,and provides a new method for in situ root phenotype study.

FCOS-LSC:A Novel Model for Green Fruit Detection in a Complex Orchard Environment

To better address the difficulties in designing green fruit recognition techniques in machine vision systems,a new fruit detection model is proposed.This model is an optimization of the FCOS(full convolution one-stage object detection)algorithm,incorporating LSC(level scales,spaces,channels)attention blocks in the network structure,and named FCOS-LSC.The method achieves efficient recognition and localization of green fruit images affected by overlapping occlusions,lighting conditions,and capture angles.Specifically,the improved feature extraction network ResNet50 with added deformable convolution is used to fully extract green fruit feature information.The feature pyramid network(FPN)is employed to fully fuse low-level detail information and high-level semantic information in a cross-connected and top-down connected way.Next,the attention mechanisms are added to each of the 3 dimensions of scale,space(including the height and width of the feature map),and channel of the generated multiscale feature map to improve the feature perception capability of the network.Finally,the classification and regression subnetworks of the model are applied to predict the fruit category and bounding box.In the classification branch,a new positive and negative sample selection strategy is applied to better distinguish supervised signals by designing weights in the loss function to achieve more accurate fruit detection.The proposed FCOS-LSC model has 38.65M parameters,38.72G floating point operations,and mean average precision of 63.0%and 75.2%for detecting green apples and green persimmons,respectively.In summary,FCOS-LSC outperforms the state-of-the-art models in terms of precision and complexity to meet the accurate and efficient requirements of green fruit recognition using intelligent agricultural equipment.Correspondingly,FCOS-LSC can be used to improve the robustness and generalization of the green fruit detection models.

Phenotyping of Salvia miltiorrhiza Roots Reveals Associations between Root Traits and Bioactive Components

Plant phenomics aims to perform high-throughput,rapid,and accurate measurement of plant traits,facilitating the identification of desirable traits and optimal genotypes for crop breeding.Salvia miltiorrhiza(Danshen)roots possess remarkable therapeutic effect on cardiovascular diseases,with huge market demands.Although great advances have been made in metabolic studies of the bioactive metabolites,investigation for S.miltiorrhiza roots on other physiological aspects is poor.Here,we developed a framework that utilizes image feature extraction software for in-depth phenotyping of S.miltiorrhiza roots.By employing multiple software programs,S.miltiorrhiza roots were described from 3 aspects:agronomic traits,anatomy traits,and root system architecture.Through K-means clustering based on the diameter ranges of each root branch,all roots were categorized into 3 groups,with primary root-associated key traits.As a proof of concept,we examined the phenotypic components in a series of randomly collected S.miltiorrhiza roots,demonstrating that the total surface of root was the best parameter for the biomass prediction with high linear regression correlation(R^(2)=0.8312),which was sufficient for subsequently estimating the production of bioactive metabolites without content determination.This study provides an important approach for further grading of medicinal materials and breeding practices.

Concise Cascade Methods for Transgenic Rice Seed Discrimination using Spectral Phenotyping

Currently,the presence of genetically modified(GM)organisms in agro-food markets is strictly regulated by enacted legislation worldwide.It is essential to ensure the traceability of these transgenic products for food safety,consumer choice,environmental monitoring,market integrity,and scientific research.However,detecting the existence of GM organisms involves a combination of complex,time-consuming,and labor-intensive techniques requiring high-level professional skills.In this paper,a concise and rapid pipeline method to identify transgenic rice seeds was proposed on the basis of spectral imaging technologies and the deep learning approach.The composition of metabolome across 3 rice seed lines containing the cry1Ab/cry1Ac gene was compared and studied,substantiating the intrinsic variability induced by these GM traits.Results showed that near-infrared and terahertz spectra from different genotypes could reveal the regularity of GM metabolic variation.The established cascade deep learning model divided GM discrimination into 2 phases including variety classification and GM status identification.It could be found that terahertz absorption spectra contained more valuable features and achieved the highest accuracy of 97.04%for variety classification and 99.71%for GM status identification.Moreover,a modified guided backpropagation algorithm was proposed to select the task-specific characteristic wavelengths for further reducing the redundancy of the original spectra.The experimental validation of the cascade discriminant method in conjunction with spectroscopy confirmed its viability,simplicity,and effectiveness as a valuable tool for the detection of GM rice seeds.This approach also demonstrated its great potential in distilling crucial features for expedited transgenic risk assessment.

ExtSpecR:An R Package and Tool for Extracting Tree Spectra from UAV-Based Remote Sensing

The development of unmanned aerial vehicle(UAV)remote sensing has been increasingly applied in forestry for high-throughput and rapid acquisition of tree phenomics traits for various research areas.However,the detection of individual trees and the extraction of their spectral data remain a challenge,often requiring manual annotation.Although several software-based solutions have been developed,they are far from being widely adopted.This paper presents ExtSpecR,an open-source tool for spectral extraction of a single tree in forestry with an easy-to-use interactive web application.ExtSpecR reduces the time required for single tree detection and annotation and simplifies the entire process of spectral and spatial feature extraction from UAV-based imagery.In addition,ExtSpecR provides several functionalities with interactive dashboards that allow users to maximize the quality of information extracted from UAV data.ExtSpecR can promote the practical use of UAV remote sensing data among forest ecology and tree breeding researchers and help them to further understand the relationships between tree growth and its physiological traits.

From Prototype to Inference:A Pipeline to Apply Deep Learning in Sorghum Panicle Detection

Head(panicle)density is a major component in understanding crop yield,especially in crops that produce variable numbers of tillers such as sorghum and wheat.Use of panicle density both in plant breeding and in the agronomy scouting of commercial crops typically relies on manual counts observation,which is an inefficient and tedious process.Because of the easy availability of red–green–blue images,machine learning approaches have been applied to replacing manual counting.However,much of this research focuses on detection per se in limited testing conditions and does not provide a general protocol to utilize deep-learning-based counting.In this paper,we provide a comprehensive pipeline from data collection to model deployment in deep-learning-assisted panicle yield estimation for sorghum.This pipeline provides a basis from data collection and model training,to model validation and model deployment in commercial fields.Accurate model training is the foundation of the pipeline.However,in natural environments,the deployment dataset is frequently different from the training data(domain shift)causing the model to fail,so a robust model is essential to build a reliable solution.Although we demonstrate our pipeline in a sorghum field,the pipeline can be generalized to other grain species.Our pipeline provides a high-resolution head density map that can be utilized for diagnosis of agronomic variability within a field,in a pipeline built without commercial software.

The Gray Mold Spore Detection of Cucumber Based on Microscopic Image and Deep Learning

Rapid and accurate detection of pathogen spores is an important step to achieve early diagnosis of diseases in precision agriculture.Traditional detection methods are time-consuming,laborious,and subjective,and image processing methods mainly rely on manually designed features that are difficult to cope with pathogen spore detection in complex scenes.Therefore,an MG-YOLO detection algorithm(Multi-head self-attention and Ghost-optimized YOLO)is proposed to detect gray mold spores rapidly.Firstly,Multi-head self-attention is introduced in the backbone to capture the global information of the pathogen spores.Secondly,we combine weighted Bidirectional Feature Pyramid Network(BiFPN)to fuse multiscale features of different layers.Then,a lightweight network is used to construct GhostCSP to optimize the neck part.Cucumber gray mold spores are used as the study object.The experimental results show that the improved MG-YOLO model achieves an accuracy of 0.983 for detecting gray mold spores and takes 0.009 s per image,which is significantly better than the state-of-the-art model.The visualization of the detection results shows that MG-YOLO effectively solves the detection of spores in blurred,small targets,multimorphology,and high-density scenes.Meanwhile,compared with the YOLOv5 model,the detection accuracy of the improved model is improved by 6.8%.It can meet the demand for high-precision detection of spores and provides a novel method to enhance the objectivity of pathogen spore detection.

Efficient Noninvasive FHB Estimation using RGB Images from a Novel Multiyear,Multirater Dataset

Fusarium head blight(FHB)is one of the most prevalent wheat diseases,causing substantial yield losses and health risks.Efficient phenotyping of FHB is crucial for accelerating resistance breeding,but currently used methods are time-consuming and expensive.The present article suggests a noninvasive classification model for FHB severity estimation using red–green–blue(RGB)images,without requiring extensive preprocessing.The model accepts images taken from consumer-grade,low-cost RGB cameras and classifies the FHB severity into 6 ordinal levels.In addition,we introduce a novel dataset consisting of around 3,000 images from 3 different years(2020,2021,and 2022)and 2 FHB severity assessments per image from independent raters.We used a pretrained EfficientNet(size b0),redesigned as a regression model.The results demonstrate that the interrater reliability(Cohen’s kappa,κ)is substantially lower than the achieved individual network-to-rater results,e.g.,0.68 and 0.76 for the data captured in 2020,respectively.The model shows a generalization effect when trained with data from multiple years and tested on data from an independent year.Thus,using the images from 2020 and 2021 for training and 2022 for testing,we improved the F_(1)^(w) score by 0.14,the accuracy by 0.11,κ by 0.12,and reduced the root mean squared error by 0.5 compared to the best network trained only on a single year’s data.The proposed lightweight model and methods could be deployed on mobile devices to automatically and objectively assess FHB severity with images from low-cost RGB cameras.The source code and the dataset are available at https://github.com/cvims/FHB_classification.

Bio-Master:Design and Validation of a High-Throughput Biochemical Profiling Platform for Crop Canopies

Accurate assessment of crop biochemical profiles plays a crucial role in diagnosing their physiological status.The conventional destructive methods,although reliable,demand extensive laboratory work for measuring various traits.On the other hand,nondestructive techniques,while efficient and adaptable,often suffer from reduced precision due to the intricate interplay of the field environment and canopy structure.Striking a delicate balance between efficiency and accuracy,we have developed the Bio-Master phenotyping system.This system is capable of simultaneously measuring four vital biochemical components of the canopy profile:dry matter,water,chlorophyll,and nitrogen content.Bio-Master initiates the process by addressing structural influences,through segmenting the fresh plant and then further chopping the segment into uniform small pieces.Subsequently,the system quantifies hyperspectral reflectance and fresh weight over the sample within a controlled dark chamber,utilizing an independent light source.The final step involves employing an embedded estimation model to provide synchronous estimates for the four biochemical components of the measured sample.In this study,we established a comprehensive training dataset encompassing a wide range of rice varieties,nitrogen levels,and growth stages.Gaussian process regression model was used to estimate biochemical contents utilizing reflectance data obtained by Bio-Master.Leave-one-out validation revealed the model's capacity to accurately estimate these contents at both leaf and plant scales.With Bio-Master,measuring a single rice plant takes approximately only 5 min,yielding around 10 values for each of the four biochemical components across the vertical profile.Furthermore,the Bio-Master system allows for immediate measurements near the field,mitigating potential alterations in plant status during transportation and processing.As a result,our measurements are more likely to faithfully represent in situ values.To summarize,the Bio-Master phenotyping system offers an efficient tool for comprehensive crop biochemical profiling.It harnesses the benefits of remote sensing techniques,providing significantly greater efficiency than conventional destructive methods while maintaining superior accuracy when compared to nondestructive approaches.

A Novel Intelligent System for Dynamic Observation of Cotton Verticillium Wilt

Verticillium wilt is one of the most critical cotton diseases,which is widely distributed in cotton-producing countries.However,the conventional method of verticillium wilt investigation is still manual,which has the disadvantages of subjectivity and low efficiency.In this research,an intelligent vision-based system was proposed to dynamically observe cotton verticillium wilt with high accuracy and high throughput.Firstly,a 3-coordinate motion platform was designed with the movement range 6,100 mm×950 mm×500 mm,and a specific control unit was adopted to achieve accurate movement and automatic imaging.Secondly,the verticillium wilt recognition was established based on 6 deep learning models,in which the VarifocalNet(VFNet)model had the best performance with a mean average precision(mAP)of 0.932.Meanwhile,deformable convolution,deformable region of interest pooling,and soft non-maximum suppression optimization methods were adopted to improve VFNet,and the mAP of the VFNet-Improved model improved by 1.8%.The precision–recall curves showed that VFNet-Improved was superior to VFNet for each category and had a better improvement effect on the ill leaf category than fine leaf.The regression results showed that the system measurement based on VFNet-Improved achieved high consistency with manual measurements.Finally,the user software was designed based on VFNet-Improved,and the dynamic observation results proved that this system was able to accurately investigate cotton verticillium wilt and quantify the prevalence rate of different resistant varieties.In conclusion,this study has demonstrated a novel intelligent system for the dynamic observation of cotton verticillium wilt on the seedbed,which provides a feasible and effective tool for cotton breeding and disease resistance research.

Lightweight Deep Learning Models for High-Precision Rice Seedling Segmentation from UAV-Based Multispectral Images

Accurate segmentation and detection of rice seedlings is essential for precision agriculture and high-yield cultivation.However,current methods suffer from high computational complexity and poor robustness to different rice varieties and densities.This article proposes 2 Iightweight neural network architectures,LW-Segnet and LW-Unet,for high-precision rice seedling segmentation.The networks adopt an encoder-decoderstructure with hybrid lightweight convolutions and spatial pyramid dilated convolutions,achieving accurate segmentation while reducing model parameters.Multispectral imagery acquired by unmanned aerial vehicle(UAV)was used to train and test the models covering 3 rice varieties and different planting densities.Experimental results demonstrate that the proposed LW-Segnet and LW-Unet models achieve higher F1-scores and intersection over union values for seedling detection and row segmentation across varieties,indicating improved segmentation accuracy.Furthermore,the models exhibit stable performance when handling different varieties and densities,showing strong robustness.In terms of efficiency,the networks have lower graphics processing unit memory usage,complexity,and parameters but faster inference speeds,reflecting higher computational efficiency.In particular,the fast speed of LW-Unet indicates potential for real-time applications.The study presents lightweight yet effective neural network architectures for agricultural tasks.By handling multiple rice varieties and densities with high accuracy,efficiency,and robustness,the models show promise for use in edge devices and UAVs to assist precision farming and crop management.The findings provide valuable insights into designing lightweight deep learning models to tackle complex agricultural problems.

Predicting and Visualizing Citrus Color Transformation Using a Deep Mask-Guided Generative Network

Citrus rind color is a good indicator of fruit development,and methods to monitor and predict color transformation therefore help the decisions of crop management practices and harvest schedules.This work presents the complete workflow to predict and visualize citrus color transformation in the orchard featuring high accuracy and fidelity.A total of 107 sample Navel oranges were observed during the color transformation period,resulting in a dataset containing 7,535 citrus images.A framework is proposed that integrates visual saliency into deep learning,and it consists of a segmentation network,a deep mask-guided generative network,and a loss network with manually designed loss functions.Moreover,the fusion of image features and temporal information enables one single model to predict the rind color at different time intervals,thus effectively shrinking the number of model parameters.The semantic segmentation network of the framework achieves the mean intersection over a union score of 0.9694,and the generative network obtains a peak signal-to-noise ratio of 30.01 and a mean local style loss score of 2.710,which indicate both high quality and similarity of the generated images and are also consistent with human perception.To ease the applications in the real world,the model is ported to an Android-based application for mobile devices.The methods can be readily expanded to other fruit crops with a color transformation period.The dataset and the source code are publicly available at GitHub.

Dynamic UAV Phenotyping for Rice Disease Resistance Analysis Based on Multisource Data

Bacterial blight poses a threat to rice production and food security,which can be controlled through large-scale breeding efforts toward resistant cultivars.Unmanned aerial vehicle(UAV)remote sensing provides an alternative means for the infield phenotype evaluation of crop disease resistance to relatively time-consuming and laborious traditional methods.However,the quality of data acquired by UAV can be affected by several factors such as weather,crop growth period,and geographical location,which can limit their utility for the detection of crop disease and resistant phenotypes.Therefore,a more effective use of UAV data for crop disease phenotype analysis is required.In this paper,we used time series UAV remote sensing data together with accumulated temperature data to train the rice bacterial blight severity evaluation model.The best results obtained with the predictive model showed an R_(p)^(2) of 0.86 with an RMSE_(p) of 0.65.Moreover,model updating strategy was used to explore the scalability of the established model in different geographical locations.Twenty percent of transferred data for model training was useful for the evaluation of disease severity over different sites.In addition,the method for phenotypic analysis of rice disease we built here was combined with quantitative trait loci(QTL)analysis to identify resistance QTL in genetic populations at different growth stages.Three new QTLs were identified,and QTLs identified at different growth stages were inconsistent.QTL analysis combined with UAV high-throughput phenotyping provides new ideas for accelerating disease resistance breeding.

Process-Based Crop Modeling for High Applicability with Attention Mechanism and Multitask Decoders

Crop models have been developed for wide research purposes and scales,but they have low compatibility due to the diversity of current modeling studies.Improving model adaptability can lead to model integration.Since deep neural networks have no conventional modeling parameters,diverse input and output combinations are possible depending on model training.Despite these advantages,no process-based crop model has been tested in full deep neural network complexes.The objective of this study was to develop a process-based deep learning model for hydroponic sweet peppers.Attention mechanism and multitask learning were selected to process distinct growth factors from the environment sequence.The algorithms were modified to be suitable for the regression task of growth simulation.Cultivations were conducted twice a year for 2 years in greenhouses.The developed crop model,DeepCrop,recorded the highest modeling efficiency(=0.76)and the lowest normalized mean squared error(=0.18)compared to accessible crop models in the evaluation with unseen data.The t-distributed stochastic neighbor embedding distribution and the attention weights supported that DeepCrop could be analyzed in terms of cognitive ability.With the high adaptability of DeepCrop,the developed model can replace the existing crop models as a versatile tool that would reveal entangled agricultural systems with analysis of complicated information.

Application of Visible/Near-Infrared Spectroscopy and Hyperspectral Imaging with Machine Learning for High-Throughput Plant Heavy Metal Stress Phenotyping:A Review

Heavy metal pollution is becoming a prominent stress on plants.Plants contaminated with heavy metals undergo changes in external morphology and internal structure,and heavy metals can accumulate through the food chain,threatening human health.Detecting heavy metal stress on plants quickly,accurately,and nondestructively helps to achieve precise management of plant growth status and accelerate the breeding of heavy metal-resistant plant varieties.Traditional chemical reagent-based detection methods are laborious,destructive,time-consuming,and costly.The internal and external structures of plants can be altered by heavy metal contamination,which can lead to changes in plants'absorption and reflection of light.Visible/near-infrared(V/NIR)spectroscopy can obtain plant spectral information,and hyperspectral imaging(HSI)can obtain spectral and spatial information in simple,speedy,and nondestructive ways.These 2 technologies have been the most widely used high-throughput phenotyping technologies of plants.This review summarizes the application of V/NIR spectroscopy and HSI in plant heavy metal stress phenotype analysis as well as introduces the method of combining spectroscopy with machine learning approaches for high-throughput phenotyping of plant heavy metal stress,including unstressed and stressed identification,stress types identification,stress degrees identification,and heavy metal content estimation.The vegetation indexes,full-range spectra,and feature bands identified by different plant heavy metal stress phenotyping methods are reviewed.The advantages,limitations,challenges,and prospects of V/NIR spectroscopy and HSI for plant heavy metal stress phenotyping are discussed.Further studies are needed to promote the research and application of V/NIR spectroscopy and HSI for plant heavy metal stress phenotyping.