European Journal of Agronomy最新文献

{"title":"Organic management and local genotypes for elevating yield and seed quality to confront climate change challenges","authors":"Arantza del-Canto ,&nbsp;Nuria De Diego ,&nbsp;Álvaro Sanz-Sáez ,&nbsp;Nikola Štefelová ,&nbsp;Usue Pérez-López ,&nbsp;Amaia Mena-Petite ,&nbsp;Maite Lacuesta","doi":"10.1016/j.eja.2025.127613","DOIUrl":"10.1016/j.eja.2025.127613","url":null,"abstract":"<div><div>Drought, exacerbated by climate change, is a challenge in agricultural production, especially in connection with nutrient-rich legumes like common beans, essential for sustainable food security. Selecting drought-adapted genotypes across various agricultural managements is a viable strategy to mitigate the impact of drought. This study aimed to evaluate different common bean genotypes, locally adapted and commercial ones, under different environmental factors, management practices, and water regimes to understand how the various growth conditions impact their performance and seed biochemical composition. We conducted a pioneering three-year field experiment with twelve genotypes grown under irrigated and rainfed conditions within conventional and organic farming systems. Physiological responses, seed yield, and quality parameters were evaluated and correlated to identify possible biomarkers that can be used for identifying resilient genotypes. The research found that drought and farming practices significantly affect bean yield and quality, with extreme temperatures being a key factor. Organic farming was as productive as conventional under irrigation and improved seed quality in rainfed conditions. The landrace Arrocina de Álava stood out for its tolerance and high-quality seeds under rainfed conditions, underlining the importance of locally adapted genotypes for climate resilience. The study confirmed the seed carbon isotope discrimination (Δ¹³C) as a reliable marker for selecting stress-tolerant genotypes and highlighted the impact of extreme temperatures on seed fat and energy content. It underscores the need for climate-adapted agriculture, highlighting organic farming as a sustainable method and the importance of incorporating climate resilience in crop breeding and management<strong>.</strong></div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"168 ","pages":"Article 127613"},"PeriodicalIF":4.5,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving the transferability of potato nitrogen concentration estimation models based on hybrid feature selection and Gaussian process regression","authors":"Hang Yin ,&nbsp;Haibo Yang ,&nbsp;Yuncai Hu ,&nbsp;Fei Li ,&nbsp;Kang Yu","doi":"10.1016/j.eja.2025.127611","DOIUrl":"10.1016/j.eja.2025.127611","url":null,"abstract":"<div><div>Feature selection methods are widely used to improve the performance of plant nitrogen concentration (PNC) estimation models. However, the performance of individual feature selection methods can vary across different environments due to various uncertainties. This study aimed to propose a hybrid feature selection method to accurately identify the sensitive bands for the PNC estimation. Field experiments with different potato cultivars and N treatments were carried out in the Inner Mongolia during 2018, 2019, and 2021. The results showed that the hybrid feature selection method can effectively identify the sensitive bands for PNC. When combined with variational heteroscedastic Gaussian process regression (VHGPR), the hybrid method significantly improves the prediction accuracy of potato PNC. Validation using an independent dataset demonstrated that the hybrid feature selection method achieved the highest prediction accuracy compared to traditional feature selection methods, with the mean coefficient of determination (R²) increasing by 16.27 %. Additionally, the performance of VHGPR was benchmarked against partial least squares regression (PLSR). The results indicated that the VHGPR model outperforms the PLSR model across various data types, with a mean R² improvement of 8.92 %. In conclusion, combining the hybrid feature selection method with VHGPR can facilitate real-time PNC estimation in the field, thereby assisting farmers in accurately applying nitrogen fertilization strategies.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"168 ","pages":"Article 127611"},"PeriodicalIF":4.5,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating machine learning with agroecosystem modelling: Current state and future challenges","authors":"Meshach Ojo Aderele ,&nbsp;Amit Kumar Srivastava ,&nbsp;Klaus Butterbach-Bahl ,&nbsp;Jaber Rahimi","doi":"10.1016/j.eja.2025.127610","DOIUrl":"10.1016/j.eja.2025.127610","url":null,"abstract":"<div><div>Machine learning (ML), especially deep learning (DL), is gaining popularity in the agroecosystem modelling community due to its ability to improve the efficiency of computationally intensive tasks. By reviewing previous modelling studies using the PRISMA technique, we present several examples of ML applications in this domain. The potential of using such models is highligthed. The different types of integration and model-building methods are categorized into process-based modelling (PBMs) and data-driven modelling (DDMs), which simulate different aspects of agroecosystem dynamics. While PBMs excel at capturing complex biophysical and biogeochemical processes, they are computationally intensive and may not always be solvable using analytical methods. To address these challenges, machine learning (ML) techniques, including deep learning (DL), are increasingly being integrated into agroecosystem modelling. This integration involves replacing PBMs with data-driven models, using hybrid models that combine PBMs and ML, or constructing simplified versions of PBMs through meta-modelling. ML-based meta-models offer computational efficiency and can capture intricate patterns and non-linear relationships in complex agricultural systems. However, challenges such as interpretability and data requirements remain. This review highlights the importance of addressing gaps and challenges to fully realize the potential of ML to identify the most promising ways of field management in promoting sustainable agricultural systems. It also highlights specific considerations such as data requirements, interpretability, model validation, and scalability for the successful integration of ML with PBMs in agriculture and the transformative potential of combining ML with PBMs, particularly in extending simulations from field to global scales and streamlining data collection processes through advanced sensor technologies based on their applications.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"168 ","pages":"Article 127610"},"PeriodicalIF":4.5,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combined application of nitrogen and phosphorus fertilizers increases soil organic carbon storage in cropland soils","authors":"Jianyu Tao,&nbsp;Xiaoyuan Liu","doi":"10.1016/j.eja.2025.127607","DOIUrl":"10.1016/j.eja.2025.127607","url":null,"abstract":"<div><div>Inorganic fertilization is indispensable in modern agriculture, yet its effects on soil organic carbon (SOC) storage and the underlying driving factors remain uncertain due to natural and anthropogenic interferences. In this study, bootstrap and random forest algorithms were employed to examine the effects of various inorganic fertilization regimes on SOC and crop yield, using a comprehensive dataset derived from 332 peer-reviewed publications. Moreover, the responses of SOC storage to agricultural management practices, climatic conditions, and initial soil properties under combined nitrogen (N) and phosphorus (P) fertilization were analyzed. Results indicated that inorganic fertilization generally increased crop yield and enhanced SOC sequestration. The increases in SOC and crop yield were significantly higher under combined N and P fertilization (i.e., NP and NPK fertilization) than under N fertilization alone. Straw return was the only agricultural management practice that significantly enhanced the annual SOC change rates. However, combined N and P fertilization increased SOC storage even without straw return, probably due to the enhanced plant-derived C inputs. Additionally, soil nutrient conditions, particularly soil P availability, were the key regulators of SOC turnover and storage under combined N and P fertilization. Microbial P limitation constrains the magnitude of SOC sequestration in cropland soils. In conclusion, our findings highlight the pivotal role of soil P availability in promoting SOC sequestration under combined N and P fertilization. Therefore, further efforts are required to determine the optimal amounts and ratios of N and P fertilizers to achieve higher soil C sequestration while sustaining crop yield.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"168 ","pages":"Article 127607"},"PeriodicalIF":4.5,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Co-implementation of deficit irrigation and nutrient management strategies to strengthen soil-plant-seed nexus, water use efficiency, and yield sustainability in fodder corn","authors":"Hanamant M. Halli ,&nbsp;B.G. Shivakumar ,&nbsp;V.K. Wasnik ,&nbsp;Prabhu Govindasamy ,&nbsp;V.K. Yadav ,&nbsp;Sunil Swami ,&nbsp;Vinod Kumar ,&nbsp;E. Senthamil ,&nbsp;Vinay M. Gangana Gowdra ,&nbsp;P.S. Basavaraj ,&nbsp;K.M. Boraiah ,&nbsp;C.B. Harisha","doi":"10.1016/j.eja.2025.127609","DOIUrl":"10.1016/j.eja.2025.127609","url":null,"abstract":"<div><div>Water scarcity-induced nutrient deficiency, low feed quality, and unsustainable fodder yields are important challenges for livestock production in tropical and subtropical countries, jeopardizing sustainable development goal-2: zero hunger. In this context, optimizing the co-benefits of deficit irrigation and fertilizer rates is crucial for strengthening the soil–plant–seed nexus, yield sustainability, water use efficiency (WUE), and the viability of progeny seed. Field experiments were carried for three years (2018–2021) in a split-plot design on a sandy loam soil of central India. Results revealed that moderate irrigation (I2) favored fodder corn root surface architecture (improved root length; 26.85–32.2 %, root weight; 24.5–31.45 %, and surface density; 24.51–32.87 %) and nutrients uptake (N, P, and K) due to increased nutrient accessibility. Likewise, balanced application of N, P, K, and Zn (N4; 120:60:40:20 kg ha⁻¹) had improved the corn roots and nutrient uptake (N; 93.56 kg ha⁻¹, P; 40.33 kg ha⁻¹, and K; 101.5 kg ha⁻¹). As a result, the integration of I2 × N4 had greater leaf area, seed (4.86 t ha⁻¹) and stover (9.62 t ha⁻¹) yields, WUE, and sustainable yield index (0.90). Furthermore, I2 × N4 enhanced the relative feed value and relative feed quality of corn seed and stover. Thus, maintained the vigor of progeny seedling (29.76 %). Therefore, the co-implementation of moderate deficit irrigation and balanced nutrition (I2 × N4) could optimize functional associations, minimize yield variations while improving WUE (by 28.6 %), root activity, optimize nutritional quality of corn feed (seed + stover), and increase the vigor of progeny seeds by strengthening soil–plant–seed nexus in limited conditions. By examining the interactions between soil, plant, and seed health, the research provides valuable insights into how irrigation and fertilization can work together to improve overall crop and feed quality.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"168 ","pages":"Article 127609"},"PeriodicalIF":4.5,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LVR: A language and vision fusion method for rice diseases segmentation under complex environment","authors":"Tianrui Zhao,&nbsp;Honglin Zhou,&nbsp;Miying Yan,&nbsp;Guoxiong Zhou,&nbsp;Chaoying He,&nbsp;Yang Hu,&nbsp;Xiaoyangdi Yan,&nbsp;Meixi Pan,&nbsp;Yunlong Yu,&nbsp;Yiting Liu","doi":"10.1016/j.eja.2025.127599","DOIUrl":"10.1016/j.eja.2025.127599","url":null,"abstract":"<div><div>Accurate identification of rice diseases depends on high-quality disease segmentation. However, challenges such as the complexity of the rice field environment, interference from redundant information, and slow model convergence can hinder effective segmentation. To address these issues, we propose A Language and Vision Fusion Method for Rice Diseases Segmentation under complex environment (LVR), which combines CNN and Transformer architectures. First, we present the Efficient Wavelet-based Multi-scale Attention (EWWL) module, designed to enhance the model’s ability to capture fine details of disease regions in complex environments. Next, to mitigate information redundancy, we design the KAN-segmentation (KAN-seg) module for efficient feature extraction. Additionally, we propose a Self-Adaptive Gradient Enhancement (SAGE) algorithm that dynamically adjusts the network’s learning rate, thereby accelerating convergence. Experimental results demonstrate that the LVR method achieves exceptional accuracy and robustness in rice disease segmentation, even under challenging field conditions. This provides substantial technical support for intelligent agricultural disease management and offers promising applications, particularly in the realm of smart agricultural disease monitoring and management.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"168 ","pages":"Article 127599"},"PeriodicalIF":4.5,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of intercrop design on weed suppression of species mixtures: A model-based exploration","authors":"Lammert Bastiaans,&nbsp;Wopke van der Werf","doi":"10.1016/j.eja.2025.127563","DOIUrl":"10.1016/j.eja.2025.127563","url":null,"abstract":"<div><div>Intercropping has frequently been reported to enhance weed suppression. A recent study combining a plant competition model and empirical data demonstrated that improved weed suppression results from a so-called selection effect, whereby the more weed suppressive crop species contributes disproportionate to the weed suppressive ability of intercrops. Here, we build on this finding and used the plant competition model to explore how species composition, mixing ratio, planting density and spatial arrangement influence the weed suppressive ability of annual intercropping systems. Analysis identified species composition as the principal design factor, since a difference in weed suppressive ability between crop species appeared the prime driver responsible for the above-average weed suppression of intercrops: the larger this difference the stronger the effect. With greatly differing levels of weed suppressive ability between crop species, even a small proportion of the stronger suppressive species greatly enhanced the intercrop’s ability to suppress weeds. In such a situation, mixing ratio can thus be used to regulate the trade-off between weed suppressiveness and the risk of the less competitive crop species being overgrown. Plant density was found to be a useful modulator if crop species displayed similar levels of weed suppression. In this case, intercrops in additive design were the only option to enhance weed suppression. Proximity of component species proved a prerequisite for superior weed suppressiveness. Consequently, in strip cropping systems, the improved weed suppressive ability rapidly declined with wider strips. The acquired quantitative insights form a theoretical foundation for considering weed suppression when designing multifunctional annual intercropping systems.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"168 ","pages":"Article 127563"},"PeriodicalIF":4.5,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143600156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combined effects of saline irrigation and genotype on the growth, grain yield and mineral concentration of durum wheat in hot arid areas","authors":"Ayesha Rukhsar ,&nbsp;Osama Kanbar ,&nbsp;Henda Mahmoudi ,&nbsp;Salima Yousfi ,&nbsp;José L. Araus ,&nbsp;Maria D. Serret","doi":"10.1016/j.eja.2025.127585","DOIUrl":"10.1016/j.eja.2025.127585","url":null,"abstract":"<div><div>Durum wheat cultivation in many parts of the Middle East is viable only under irrigation, often with saline water. This study evaluated the effects of salinity, season, and genotype on durum wheat grain yield and quality. Ten durum wheat genotypes were grown for two consecutive seasons under different irrigation salinities (2.6, 10, and 15 dSm⁻¹) in sandy soils at the International Center for Biosaline Agriculture (Dubai, UAE). Various traits were evaluated, including grain yield (GY), biomass, plant height, number of spikes per plant, thousand grain weight (TGW), chlorophyll content, and grain isotope composition. Salinity reduced GY, agronomic traits, and chlorophyll content, while increasing δ¹³C and sodium (Na) concentration in grains. The season effect significantly impacted GY, biomass, TGW, and some mineral concentrations, potentially due to heat waves during grain filling. The genotypic effect was significant for GY, agronomic traits, and concentrations of nitrogen and most minerals. A negative phenotypic correlation was found between GY and both Na and δ¹³C, suggesting that better water status and lower Na accumulation were linked to genotypes with improved performance. However, there was no negative trade-off across genotypes between grain yield and concentrations of most minerals. Moreover, the accumulation of N and several nutrients (P, Mg, Mn, Fe, Zn, Cu, S) in grains followed a similar pattern, with positive correlations observed. We conclude that genotypic variability is crucial to improving yield and modulating mineral content in durum wheat grown under saline irrigation in hot arid areas.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"168 ","pages":"Article 127585"},"PeriodicalIF":4.5,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143600160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Band applied K increases agronomic and economic efficiency of K fertilization in a crop rotation under no-till in southern Brazil","authors":"Gustavo Pesini ,&nbsp;Dayana Jéssica Eckert ,&nbsp;João Pedro Moro Flores ,&nbsp;Lucas Aquino Alves ,&nbsp;Dionata Filippi ,&nbsp;Gabriela Naibo ,&nbsp;André Luis Vian ,&nbsp;Christian Bredemeier ,&nbsp;Danilo Rheinheimer dos Santos ,&nbsp;Tales Tiecher","doi":"10.1016/j.eja.2025.127595","DOIUrl":"10.1016/j.eja.2025.127595","url":null,"abstract":"<div><div>The effectiveness of potassium (K) fertilizer management strategies on sandy clay loam soils under no-till (NT) is essential to achieving economically viable yields. This study compared the agronomic and economic efficiency of K fertilization using band application versus broadcast distribution on a subtropical Acrisol under NT. From 2019–2023, five K rates (0, 50, 100, 150, and 200 kg ha⁻¹) were applied annually using band and broadcast methods during spring/summer at corn or soybean sowing. There was no increase in grain crop yield with K broadcast application. This resulted in economic loss and significantly increased the available K levels in the topsoil, hampering the use of this soil layer to diagnose K availability and the likelihood of response to K fertilizers. The optimum agronomic and economic K rate matched the output K rate when the fertilizer was banded. The application of 50 kg ha⁻¹ of K increased 20 % the partial factor productivity, 300 % the agronomy efficiency, 680 % the economic profit from applied K, and 100 % the value cost ratio compared to the broadcast application. Even with available K above the critical level in the soil of the 0–10 cm layer, low rates of K fertilizers should be applied banded in the seed furrow.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"168 ","pages":"Article 127595"},"PeriodicalIF":4.5,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leaf area index (LAI) prediction using machine learning and UAV based vegetation indices","authors":"Saddam Hussain ,&nbsp;Fitsum T. Teshome ,&nbsp;Boaz B. Tulu ,&nbsp;Girma Worku Awoke ,&nbsp;Niguss Solomon Hailegnaw ,&nbsp;Haimanote K. Bayabil","doi":"10.1016/j.eja.2025.127557","DOIUrl":"10.1016/j.eja.2025.127557","url":null,"abstract":"<div><div>As a critical indicator of plant growth and water use, accurately and promptly estimating leaf area index (LAI) is critical for improved crop management. However, measuring LAI requires substantial effort and time . The main objective of this study was to leverage vegetation indices (VIs) generated from unmanned aerial vehicle (UAV)-based images and machine learning (ML) techniques for estimating LAI of green beans and sweet corn. The research was conducted at the Tropical Research and Education Center (TREC), University of Florida, Homestead, Florida over three seasons from 2020-2023. The experiment for each crop consisted of four irrigation treatments, i.e., 100 % full irrigation (FI), 75 %, 50 %, and 25 % FI, with four replications. Destructive leaf samples were collected by cutting plants from 30 cm row length of two inner plot rows and leaf area (LA) was measured using a LI-3000C transparent belt conveyor. Plant height and canopy width were also measured bi-weekly. Moreover, a UAV-based RedEdge-MX sensor was employed throughout the seasons to collect high-resolution multispectral imageries that consist of five bands. Plant LAI was calculated using the plant density method from measured LA. A calibrated DSSAT model was used to simulate the LAI for both crops.. Simulated LAI from DSSAT was compared against measured LAI, and relationships were established between simulated LAI and 12 VIs generated from UAV images. Additionally, ML algorithms, i.e., random forest (RF), eXtreme gradient boosting (XGB), and light gradient boosting (LGB) models, were trained to predict LAI for both crops using VIs as input features. Results showed DSSAT's perfroamnce in simulating LAI was good for green beans and reasonable for sweet corn. Out of the 12 indices tested, six VIs, i.e., Enhanced Vegetation Index 2 (EVI2), Normalized Difference Vegetation Index (NDVI), Normalized Green Red Difference Index (NGRDI), NIR-RE Normalized Difference Vegetation Index (NIRRENDVI), Red-Edge Normalized Vegetation Index (RENDVI), and Soil Adjusted Vegetation Index (SAVI) showed a good agreement with simulated LAI for both crops. The LGB, RF, and XGB models predicted LAI with acceptable accuracy, achieving r² values of 0.78, 0.90, and 0.90 and RMSE values of 0.43, 0.29, and 0.28 for sweet corn and r² of 0.72, 0.79, and 0.80 and RMSE of 1.01, 0.86, and 0.85 for green beans, respectively. The findings indicate that ML models and VIs derived from UAV imagery could be used to predict LAI with acceptable accuracy for green beans and sweet corn.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"168 ","pages":"Article 127557"},"PeriodicalIF":4.5,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143591629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}