Porcine Breeding Management in a Large-scale Piggery with Microbial Fermentation Bed

[Objective] The behavior of eating, drinking, defecating and peeing of 1 500 pigs in a large-scale microbial fermentation bed-equipped piggery was observed. We hoped to find some simple indicators that could reflect the health status of swinery and to provide experience for the swinery performance management in large-scale microbial fermentation bed-equipped piggery. [Method] The body weight （BW）, daily BW gain, feed intake and other indicators of different-day-old pigs were recorded in details. Based on the recorded data, the models between BW, BW gain, average daily feed intake and feed/gain ratio and growth days （d） were established. In addition, the incidences of pox-like macula （dermatitis）, diarrhea （gastrointestinal disease）, cough （respiratory disease）, stiff pig （malnutrition）, conjunctivitis （eye disease） and foot inflection （trauma） among fattening pigs were also investigated. [Result] The BW range, average BW, daily BW gain, breeding days, daily feed intake range, average daily feed intake, staged feed intake, accumulated feed intake, feed/gain ratio and accumulated feed/gain ratio of different-day-old pigs were studied, respectively. Four dynamic models were established for the growth of pigs： （1） the BW （y）-age （x） mod- el： y=0.758 9x-19.883 （3=0.993 7）; （2） the BW gain （y）-age （x） model： y=1.039 5x05051 （F=0.885 4）; （3） the average daily feed intake （y）-age （x） model： y=0.023 5x-0.334 3 （F=0.991 7）; （4） the feed/gain ratio （y）-age （x） model： y=0.022x＋0.427 8 （P=0.988 5）. Based on these models, the corresponding theoretical growth value of pigs at different growth stage could be predicted. The main diseases occurred among the swinery in the large-scale microbial fermentation bed piggery included pox-like macula （dermatitis）, diarrhea （gastrointestinal disease）, cough （respiratory disease）, stiff pig （mal- nutrition）, conjunctivitis （eye disease） and foot inflection （trauma）. The deadly infec- tious diseases had been not found among the pigs. [Conclusion] When the actual BW, BW gain, average daily feed intake and feed/gain ratio were all lower than the theoretical values predicted by the models, the management should be enhanced. The average daily feed intake of 60 to 65-day-old pigs was lower than the theoretic value, indicating that the pigs could not adapt nicely to the fermentation bed at the very early stage. When the pigs grew up to 70 to 75 d old, the average daily feed intake was higher than the theoretical value, indicating that the pigs had adapted to the fermentation bed. In particularly, average daily feed intake of 75-day-old pigs was higher than the theoretical value by 21%. It was suggested the fermentation bed was conducive to the growth of pigs. Considering the occurrence of diseases among pigs, the overall incidence was relatively low. The incidence of each disease was all lower than 10% with little difficulty in treating. If the management of mattress was strength- ened, such as paying attention to feeding and keeping water clean, many diseases could heal by themselves.

Corrigendum to“Migration pattern of a population of Barn Swallows(Hirundo rustica)breeding in East Asian tropical region”[Avian Res.15(2024)100192]

The authors regret that the caption of Fig.4 should be replaced as below.

Assessment of Wet Season Precipitation in the Central United States by the Regional Climate Simulation of the WRFG Member in NARCCAP and Its Relationship with Large-Scale Circulation Biases

Assessment of past-climate simulations of regional climate models(RCMs)is important for understanding the reliability of RCMs when used to project future regional climate.Here,we assess the performance and discuss possible causes of biases in a WRF-based RCM with a grid spacing of 50 km,named WRFG,from the North American Regional Climate Change Assessment Program(NARCCAP)in simulating wet season precipitation over the Central United States for a period when observational data are available.The RCM reproduces key features of the precipitation distribution characteristics during late spring to early summer,although it tends to underestimate the magnitude of precipitation.This dry bias is partially due to the model’s lack of skill in simulating nocturnal precipitation related to the lack of eastward propagating convective systems in the simulation.Inaccuracy in reproducing large-scale circulation and environmental conditions is another contributing factor.The too weak simulated pressure gradient between the Rocky Mountains and the Gulf of Mexico results in weaker southerly winds in between,leading to a reduction of warm moist air transport from the Gulf to the Central Great Plains.The simulated low-level horizontal convergence fields are less favorable for upward motion than in the NARR and hence,for the development of moist convection as well.Therefore,a careful examination of an RCM’s deficiencies and the identification of the source of errors are important when using the RCM to project precipitation changes in future climate scenarios.

Rice Heat Tolerance Breeding: A Comprehensive Review and Forward Gaze

The yield potential of rice is seriously affected by heat stress due to climate change. Since rice is a staple food globally, it is imperative to develop heat-resistant rice varieties. Thus, a thorough understanding of the complex molecular mechanisms underlying heat tolerance and the impact of high temperatures on various critical stages of the crop is needed. Adoption of both conventional and innovative breeding strategies offers a long-term advantage over other methods, such as agronomic practices, to counter heat stress. In this review, we summarize the effects of heat stress, regulatory pathways for heat tolerance, phenotyping strategies, and various breeding methods available for developing heat-tolerant rice. We offer perspectives and knowledge to guide future research endeavors aimed at enhancing the ability of rice to withstand heat stress and ultimately benefit humanity.

A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization

Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.

Drought-Tolerant Rice at Molecular Breeding Eras:An Emerging Reality

Rice(Oryza sativa L.)stands as the most significantly influential food crop in the developing world,with its total production and yield stability affected by environmental stress.Drought stress impacts about 45%of the world’s rice area,affecting plants at molecular,biochemical,physiological,and phenotypic levels.The conventional breeding method,predominantly employing single pedigree selection,has been widely utilized in breeding numerous drought-tolerant rice varieties since the Green Revolution.With rapid progress in plant molecular biology,hundreds of drought-tolerant QTLs/genes have been identified and tested in rice crops under both indoor and field conditions.Several genes have been introgressed into elite germplasm to develop commercially accepted drought-tolerant varieties,resulting in the development of several drought-tolerant rice varieties through marker-assisted selection and genetically engineered approaches.This review provides up-to-date information on proof-of-concept genes and breeding methods in the molecular breeding era,offering guidance for rice breeders to develop drought-tolerant rice varieties.

Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems

Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational resources.Furthermore,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables efficiently.This paper is dedicated to addressing the challenges posed by SLMOPs.To start,we introduce innovative objective functions customized to mine maximum and minimum candidate sets.This substantial enhancement dramatically improves the efficacy of frequent pattern mining.In this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific dimensions.Additionally,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero variables.This novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function value.We extensively tested our algorithm across eight benchmark problems and four real-world SLMOPs.The results demonstrate that our approach achieves competitive solutions across various challenges.

Large-scale model testing of high-pressure grouting reinforcement for bedding slope with rapid-setting polyurethane

Bedding slope is a typical heterogeneous slope consisting of different soil/rock layers and is likely to slide along the weakest interface.Conventional slope protection methods for bedding slopes,such as retaining walls,stabilizing piles,and anchors,are time-consuming and labor-and energy-intensive.This study proposes an innovative polymer grout method to improve the bearing capacity and reduce the displacement of bedding slopes.A series of large-scale model tests were carried out to verify the effectiveness of polymer grout in protecting bedding slopes.Specifically,load-displacement relationships and failure patterns were analyzed for different testing slopes with various dosages of polymer.Results show the great potential of polymer grout in improving bearing capacity,reducing settlement,and protecting slopes from being crushed under shearing.The polymer-treated slopes remained structurally intact,while the untreated slope exhibited considerable damage when subjected to loads surpassing the bearing capacity.It is also found that polymer-cemented soils concentrate around the injection pipe,forming a fan-shaped sheet-like structure.This study proves the improvement of polymer grouting for bedding slope treatment and will contribute to the development of a fast method to protect bedding slopes from landslides.

Creating large EMS populations for functional genomics and breeding in wheat

Wheat germplasm is a fundamental resource for basic research,applied studies,and wheat breeding,which can be enriched normally by several paths,such as collecting natural lines,accumulating breeding lines,and introducing mutagenesis materials.Ethyl methane sulfonate(EMS)is an alkylating agent that can effectively introduce genetic variations in a wide variety of plant species.In this study,we created a million-scale EMS population(MEP)that started with the Chinese wheat cultivars‘Luyan 128’,‘Jimai 38’,‘Jimai 44’,and‘Shannong 30’.In the M1 generation,the MEP had numerous phenotypical variations,such as>3,000 chlorophyll-deficient mutants,2,519 compact spikes,and 1,692 male sterile spikes.There were also rare mutations,including 30 independent tillers each with double heads.Some M1 variations of chlorophyll-deficiency and compact spikes were inheritable,appearing in the M2 or M3 generations.To advance the entire MEP to higher generations,we adopted a single-seed descendent(SSD)approach.All other seed composites of M2 were used to screen other agronomically important traits,such as the tolerance to herbicide quizalofop-P-methyl.The MEP is available for collaborative projects,and provides a valuable toolbox for wheat genetics and breeding for sustainable agriculture.

Breeding and Seed Reproduction Techniques of a New Tetraploid Common Buckwheat Variety‘Pintianqiao 3’

[Objectives]This study was conducted to actively carry out the breeding of new tetraploid common buckwheat varieties and its supporting breeding techniques.[Methods]Pintianqiao 3 is a new tetraploid common buckwheat variety developed by College of Agriculture of Shanxi Agricultural University and Agricultural Genetic Resources Center of Shanxi Agricultural University,using‘Pintianqiao 1’as the parent,through mutation treatment with 0.2%colchicine aqueous solution,grain selection,plant selection,isolation and identification,variety comparison,regional test and field investigation.The variety has chromosomes 2n=4X=32,and shows a spring sowing period of 101 d and a summer sowing period of 80 d,large flowers and seeds(with a 1000-grain weight of 41.4 g),and good resistance to lodging.[Results]From 2021 to 2022,Pintianqiao 3 participated in the independent joint regional test of common buckwheat varieties in Shanxi Province,and the average yield in 10 test positions was 1.8 kg,equivalent to 1800 kg/hm^(2),which was 8.4%higher than the control.It passed the field investigation conducted by Shanxi provincial expert group for identification of non-major crop varieties in Dongyang and Kelan experimental sites on September 2-3,2022.On January 4,2024,it passed the preliminary examination of Shanxi Provincial Crop Variety Approval Committee.The seed reproduction technique of Pintianqiao 3 including land selection,preparation before sowing,sowing,field management and timely harvesting has been developed.[Conclusions]This study provides technical support for the demonstration and popularization of this new variety.

Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics

This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.

A semantic vector map-based approach for aircraft positioning in GNSS/GPS denied large-scale environment

Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.

Artificial selection of the Green Revolution gene Semidwarf 1 is implicated in upland rice breeding

Semidwarf breeding has boosted crop production and is a well-known outcome from the first Green Revolution. The Green Revolution gene Semidwarf 1(SD1), which modulates gibberellic acid(GA) biosynthesis, plays a principal role in determining rice plant height. Mutations in SD1 reduce rice plant height and promote lodging resistance and fertilizer tolerance to increase grain production. The plant height mediated by SD1 also favors grain yield under certain conditions. However, it is not yet known whether the function of SD1 in upland rice promotes adaptation and grain production. In this study, the plant height and grain yield of irrigated and upland rice were comparatively analyzed under paddy and dryland conditions. In response to dryland environments, rice requires a reduction in plant height to cope with water deficits. Upland rice accessions had greater plant heights than their irrigated counterparts under both paddy and dryland conditions, and appropriately reducing plant height could improve adaptability to dryland environments and maintain high grain yield formation. Moreover, upland rice cultivars with thicker stem diameters had stronger lodging resistance, which addresses the lodging problem. Knockout of SD1 in the upland rice cultivar IRAT104 reduced the plant height and grain yield, demonstrating that the adjustment of plant height mediated by SD1 could increase grain production in dryland fields. In addition, an SD1 genetic diversity analysis verified that haplotype variation causes phenotypic variation in plant height. During the breeding history of rice, SD1 allelic mutations were selected from landraces to improve the grain yield of irrigated rice cultivars, and this selection was accompanied by a reduction in plant height. Thus, five known mutant alleles were analyzed to verify that functional SD1 is required for upland rice production. All these results suggest that SD1 might have undergone artificial positive selection in upland rice, which provides further insights concerning greater plant height in upland rice breeding.

Seed Storability in Rice: Physiological Foundations, Molecular Mechanisms, and Applications in Breeding

Long-term storage of crop seeds is critical for the conservation of germplasm resources, ensuring food supply, and supporting sustainable production. Rice, as a major food staple, has a substantial stock for consumption and production worldwide. However, its food value and seed viability tend to decline during storage. Understanding the physiological responses and molecular mechanisms of aging tolerance forms the basis for enhancing seed storability in rice. This review outlines the latest progress in influential factors, evaluation methods, and identification indices of seed storability. It also discusses the physiological consequences, molecular mechanisms, and strategies for breeding aging-tolerant rice in detail. Finally, it highlights challenges in seed storability research that require future attention. This review offers a theoretical foundation and research direction for uncovering the mechanisms behind seed storability and breeding aging-tolerant rice.

Advances in Understanding Cadmium Stress and Breeding of Cadmium-Tolerant Crops

Cadmium(Cd) pollution has emerged as a critical global environmental concern, due to its significant toxicity, environmental persistence, and the pervasiveness of contamination. Significantly, the bioaccumulation of Cd in agricultural crops constitutes a primary vector for its entry into the human diet. This issue warrants urgent attention from both the scientific community and policymakers to develop and implement effective mitigation strategies. This review delves into the physiological impacts of Cd stress on plants, including the suppression of photosynthetic activity, amplification of oxidative stress, and disruptions in mineral nutrient homeostasis. Additionally, the resistance mechanisms deployed by plants in response to Cd stress have been explored, and the prospective contributions of molecular breeding strategies in augmenting crop tolerance to Cd and minimizing its bioaccumulation have been assessed. By integrating and analyzing these findings, we seek to inform future research trajectories and proffer strategic approaches to enhance agricultural sustainability, safeguard human health, and protect environmental integrity.

Long-term trends in the abundance and breeding performance in Adélie penguins:the Argentine Ecosystem Monitoring Program

In this work,we report long-term trends in the abundance and breeding performance of Adélie penguins(Pygoscelis adeliae)nesting in three Antarctic colonies(i.e.,at Martin Point,South Orkneys Islands;Stranger Point/Cabo Funes,South Shetland Islands;and Esperanza/Hope Bay in the Antarctic Peninsula)from 1995/96 to 2022/23.Using yearly count data of breeding groups selected,we observed a decline in the number of breeding pairs and chicks in crèche at all colonies studied.However,the magnitude of change was higher at Stranger Point than that in the remaining colonies.Moreover,the index of breeding success,which was calculated as the ratio of chicks in crèche to breeding pairs,exhibited no apparent trend throughout the study period.However,it displayed greater variability at Martin Point compared to the other two colonies under investigation.Although the number of chicks in crèche of Adélie penguins showed a declining pattern,the average breeding performance was similar to that reported in gentoo penguin colonies,specifically,those undergoing a population increase(even in sympatric colonies facing similar local conditions).Consequently,it is plausible to assume a reduction of the over-winter survival as a likely cause of the declining trend observed,at least in the Stranger Point and Esperanza colonies.However,we cannot rule out local effects during the breeding season affecting the Adélie population of Martin Point.

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.

A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.

Feeding Practices and Use of Lysine and Methionine in Pigrationing on Intensified Pig Breeding in the West Center and Hauts Bassins Regions of Burkina Faso

The aim of the study was to assess feeding practices and the use of lysine and methionine in pig rationing on intensified and semi-intensive pig breeding in the Koudougou and Bobo-Dioulasso areas. To this end, a cross-sectional survey was carried out on 87 breeding in these towns. A Discriminant Factorial Analysis (DFA) confirming a k-means classification of the data collected was used to retain 71 breeding divided into three breeding classes: Class A (32.4% of breeding), Class B (14.08%) and Class C (53.52%). The results show that the majority of pig breeders were men between the ages of 36 and 59. Average herd sizes were 35 ± 28;79 ± 42 and 89 ± 21 pigs for Classes A, B and C respectively. The main breeds of pig found on the breeding were crossbred, Large white, local, Landrace and Duroc. Class A (26.1%), B (30%) and C (15.8%) breeders were familiar with both lysine and methionine. Class A breeders distributed feed staggered (65.2%) and in rations (34.8%). Lysine (13%) and methionine (8.7%) were purchased at 5250 FCFA/kg. Those in class B distributed feed staggered (50%) and in the form of rations (50%), in which they incorporated lysine (30%) and methionine (30%) purchased at a cost of 2500 FCFA/kg and 3000 FCFA/kg respectively. Rationing and staggered feeding were practiced by 23.7% and 76.3% of Class C breeders respectively. Only lysine purchased at 3400 FCFA/kg was incorporated into rations by 10.5% of breeders. The high cost of lysine and methionine was incriminated by Class A (100%), B (33.3%) and C (50%) breeders. In conclusion, intensive pig breeding, the practice of rationing and the incorporation of the amino acids lysine and methionine are of ascending importance from classes C, A to B. The high cost of feedstuffs, particularly lysine and methionine, compromises their use in rations, which could have a negative impact on expected breeding performance. The screening and use of feeds rich in and/or enriched with these amino acids, through the development or adaptation of technologies, could improve the efficiency of rations and the productivity of intensive pig breeding in Burkina Faso.

Estimating Key Phenological Dates of Multiple Rice Accessions Using Unmanned Aerial Vehicle-Based Plant Height Dynamics for Breeding

Efficient and high-quality estimation of key phenological dates in rice is of great significance in breeding work. Plant height(PH) dynamics are valuable for estimating phenological dates. However, research on estimating the key phenological dates of multiple rice accessions based on PH dynamics has been limited. In 2022, field traits were collected using unmanned aerial vehicle(UAV)-based images across 435 plots, including 364 rice varieties. PH, dates of initial heading(IH) and full heading(FH), and panicle initiation(PI), and growth period after transplanting(GPAT) were collected during the rice growth stage. PHs were extracted using a digital surface model(DSM) and fitted using Fourier and logistic models. Machine learning algorithms, including multiple linear regression, random forest(RF), support vector regression, least absolute shrinkage and selection operator, and elastic net regression, were employed to estimate phenological dates. Results indicated that the optimal percentile of the DSM for extracting rice PH was the 95th(R^(2) = 0.934, RMSE = 0.056 m). The Fourier model provided a better fit for PH dynamics compared with the logistic models. Additionally, curve features(CF) and GPAT were significantly associated with PI, IH, and FH. The combination of CF and GPAT outperformed the use of CF alone, with RF demonstrating the best performance among the algorithms. Specifically, the combination of CF extracted from the logistic models, GPAT, and RF yielded the best performance for estimating PI(R^(2) = 0.834, RMSE = 4.344 d), IH(R^(2) = 0.877, RMSE = 2.721 d), and FH(R^(2) = 0.883, RMSE = 2.694 d). Overall, UAV-based rice PH dynamics combined with machine learning effectively estimated the key phenological dates of multiple rice accessions, providing a novel approach for investigating key phenological dates in breeding work.