Journal of Agriculture and Food Research最新文献

{"title":"Assessment of nitrate levels in greenhouse-grown spinaches by Raman spectroscopy: A tool for sustainable agriculture and food security","authors":"Paolo Matteini ,&nbsp;Carmelo Distefano ,&nbsp;Marella de Angelis ,&nbsp;Giovanni Agati","doi":"10.1016/j.jafr.2025.101839","DOIUrl":"10.1016/j.jafr.2025.101839","url":null,"abstract":"<div><div>Excessive use of nitrogen-based fertilizers in vegetable cultivation has raised significant environmental and health concerns, driving the need for sustainable fertilization practices. Spinach, known for its propensity to accumulate nitrates, demands precise monitoring tools. This study investigates the potential of Raman spectroscopy for quantifying nitrate levels in greenhouse-grown spinach, utilizing multiple linear regression (MLR) and partial least squares regression (PLSR) models. Both models exhibit strong predictive performance, with R² exceeding 0.8. Furthermore, integrating Raman spectroscopy with optical sensors like the Dualex can further enhance nitrate prediction accuracy. These findings underscore the feasibility of this non-destructive, scalable approach, offering a promising solution for sustainable agriculture, food security, and environmental protection.</div></div>","PeriodicalId":34393,"journal":{"name":"Journal of Agriculture and Food Research","volume":"21 ","pages":"Article 101839"},"PeriodicalIF":4.8,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hyperparameter optimization of apple leaf dataset for the disease recognition based on the YOLOv8","authors":"Yong-Suk Lee ,&nbsp;Maheshkumar Prakash Patil ,&nbsp;Jeong Gyu Kim ,&nbsp;Yong Bae Seo ,&nbsp;Dong-Hyun Ahn ,&nbsp;Gun-Do Kim","doi":"10.1016/j.jafr.2025.101840","DOIUrl":"10.1016/j.jafr.2025.101840","url":null,"abstract":"<div><div>Apple leaf diseases have a major influence on apple productivity and quality, demanding a precise and efficient recognition system. Using the YOLOv8 family of object detection models, we created a disease recognition model for the apple leaf dataset in this study. The developed model was fine-tuned extensively by hyperparameter optimization to identify the best variant for practical deployment. Firstly, fine-tuning with different YOLOv8 series was conducted on an apple leaf dataset including various types of images. Among them, the YOLOv8s demonstrated the best balance with a fitness of 0.97171, a precision of 0.97082, a recall of 0.96837, a [email protected] of 0.98016, and an image processing speed of 1.58 ms. Further hyperparameter optimization was conducted using the One-Factor-At-a-Time (OFAT) and Random Search (RS) methods. In this case, the optimal settings determined as per the OFAT method were a batch size of 48, a learning rate of 0.01, a weight decay of 0.0005, a momentum of 0.963, and 200 epochs. These settings were adopted as the baseline for RS. RS then searched for 50 additional configurations; the best configuration, C34 (batch size of 48, learning rate of 0.0137, momentum of 0.9433, and weight decay of 0.0009), achieved a fitness score of 0.97688, a precision of 0.97797, a recall of 0.97295, and a [email protected] of 0.98257. The correlation analysis showed that learning rate and momentum significantly impacted the performance of the models. Overall, the C34 model demonstrates high accuracy, rapid processing speed, and robustness suitable for training real-time, large-scale apple leaf disease recognition.</div></div>","PeriodicalId":34393,"journal":{"name":"Journal of Agriculture and Food Research","volume":"21 ","pages":"Article 101840"},"PeriodicalIF":4.8,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impacts of climate and non-climate factors on cereal crop yield in East Africa: A generalized method of moments (GMM) panel data analysis","authors":"Yadeta Bedasa ,&nbsp;Adeba Gemechu ,&nbsp;Amsalu Bedemo ,&nbsp;Bacha Gebissa ,&nbsp;Bai Xiuguang","doi":"10.1016/j.jafr.2025.101829","DOIUrl":"10.1016/j.jafr.2025.101829","url":null,"abstract":"<div><div>Crop yields and productivity are low in East Africa due to the climatic and non-climate factors that affect cereal crop yields. In contrast to the East African countries, which only produce 2 t/ha on average, industrialized nations produce an average yield of 10.77 t/ha. The large productivity gap in East Africa is the main topic of this proposed study. East Africa's cereal yield productivity has failed because of its genetic potential. There has not been much previous study on how climate and non-climate factors affect the yield of cereal crops in East Africa using panel data. Further, this study seeks to fill a gap in existing research by employing a generalized method of moments (GMM) panel model. This study examines the impacts of climatic and non-climatic factors on cereal yield in East Africa, analyzing data from seven nations (Burundi, Somalia, Ethiopia, Kenya, Eritrea, Tanzania, and Uganda) from 1993 to 2018. The World Bank Development Indicators provided the data for cereal yield, seed, fertilizer, and carbon dioxide emissions, while the Climate Change Knowledge Portal provided the data for mean annual temperature and mean annual precipitation. The results of the investigation suggest that there is expected to be a continuous decline in cereal yield in East Africa due to the effects of both climatic and non-climate factors. Key findings indicate that the amount of fertilizer consumed and the amount of seed applied have positive effects. The yield of cereals increases by 0.833 kg/ha for every 1 percent increase in precipitation. Moreover, the yield of cereals is decreased by 4.354 kg/ha for every 1 percent increase in temperature. Utilizing high-temperature and drought-resistant cereal crop varieties is also advised to lessen the adverse impacts of climate change and non-climate factors. Adaptive strategies are needed in policy to alleviate the effects of climate and non-climate factors. This study has significant policy implications for the need to assist farmers in implementing new agricultural technologies, breeding stress-tolerant plants, and altering their production and farm management practices.</div></div>","PeriodicalId":34393,"journal":{"name":"Journal of Agriculture and Food Research","volume":"21 ","pages":"Article 101829"},"PeriodicalIF":4.8,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pork-YOLO: Automated collection of pork quality traits","authors":"Jiacheng Wei,&nbsp;Xi Tang,&nbsp;Jinxiu Liu,&nbsp;Ting Luo,&nbsp;Yan Wu,&nbsp;Junhui Duan,&nbsp;Shijun Xiao,&nbsp;Zhiyan Zhang","doi":"10.1016/j.jafr.2025.101838","DOIUrl":"10.1016/j.jafr.2025.101838","url":null,"abstract":"<div><div>As consumer demand for high-quality meat rises, efficient and precise phenotypic measurement of pork traits is essential for improving quality. Key parameters include marbling density and longissimus thoracis area. This study proposes an automated system utilizing high-resolution cameras to rapidly and efficiently measure eye muscle area and marbling scores on a large scale. A novel algorithm called \"Pork-YOLO\" was developed. First, a lightweight segmentation head network, Pork-Seg, was introduced, which utilizes shared convolution and group normalization to minimize parameters and enhance generalization, with prototype learning incorporated to reduce complexity. Next, the StarNet backbone was integrated, employing star-shaped operations for high-dimensional feature representation while maintaining computational efficiency, along with a hierarchical convolutional structure to boost performance. Lastly, the C2f-SCA module combines contextual anchor attention with star-shaped operations to improve long-range dependency capture. Verification experiments demonstrated that Pork-YOLO achieved a mean Intersection over Union (mIoU) of 97.86 % and a frame rate of 160.4 FPS (6.23 ms per image), with GFLOPs reduced to 30.1. When compared with the ground truth, the Pork-YOLO model exhibited satisfactory segmentation accuracy, closely aligning with the ground truth. The coefficient of determination (R²) between the results obtained by the Pork-YOLO model and the gold standard method was 0.9717. For marbling scoring, an image classification task yielded an average accuracy of 98.9 %, with a strong correlation (R² = 0.999) between predicted and actual values. This study presents an innovative method for rapid automated assessment of pork quality traits, offering valuable insights for future phenotypic measurement automation.</div></div>","PeriodicalId":34393,"journal":{"name":"Journal of Agriculture and Food Research","volume":"21 ","pages":"Article 101838"},"PeriodicalIF":4.8,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bimodal data analysis for early detection of lameness in dairy cows using artificial intelligence","authors":"Yashan Dhaliwal ,&nbsp;Hangqing Bi ,&nbsp;Suresh Neethirajan","doi":"10.1016/j.jafr.2025.101837","DOIUrl":"10.1016/j.jafr.2025.101837","url":null,"abstract":"<div><div>Lameness remains a leading cause of economic loss in Canadian dairy herds while also compromising animal welfare. To address the urgent need for early detection, we introduce a novel bimodal artificial intelligence (AI) framework that leverages both facial biometric data and accelerometer-based movement metrics. Over a 21-day period, six Holstein cows were monitored to capture variations in facial expressions and locomotion, and a multimodal model was built by combining DenseNet-121 for image analysis with Long Short-Term Memory (LSTM) networks for time-series data. Crucially, our model employs a multi-head attention mechanism to fuse visual and movement features, enabling it to overcome confounding factors such as lighting conditions, barn environments, and individual behavioral differences. This approach achieved a 99.55 % accuracy—substantially exceeding single-modality baselines—and Grad-CAM interpretations revealed key facial cues (orbital tightening, ear posture, muzzle tension) linked to lameness. Lame cows also exhibited prolonged resting times, especially during peak activity hours, underscoring their discomfort. These findings illustrate how integrating facial and accelerometer data can promote timely interventions, significantly enhancing cow welfare and reducing medical expenditures and productivity losses. Moreover, our results highlight how tie-stall barn systems can exacerbate lameness by restricting natural movement, further supporting recommendations to transition toward more open, movement-friendly housing. In doing so, producers not only protect cow well-being but also safeguard vital economic returns.</div></div>","PeriodicalId":34393,"journal":{"name":"Journal of Agriculture and Food Research","volume":"21 ","pages":"Article 101837"},"PeriodicalIF":4.8,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sustainable responses to open field tomato (Solanum lycopersicum L.) stress impacts","authors":"Mohammed Mustafa ,&nbsp;Ruth W. Mwangi ,&nbsp;Zita Szalai ,&nbsp;Noémi Kappel ,&nbsp;László Csambalik","doi":"10.1016/j.jafr.2025.101825","DOIUrl":"10.1016/j.jafr.2025.101825","url":null,"abstract":"<div><div>Integration of breeding innovations and epigenetic modifications offers the potential to boost productivity and promote sustainable agricultural practices, particularly in tomato production, which accounts for 16 % of global vegetable production. They are susceptible to various stress factors, Both abiotic (light, temperature, water, humidity, nutrients) and biotic (pests, diseases), which can impact fruit quality and reduce yield quantity by 50–70 %leading to food insecurity and economic losses.</div><div>Climatic factors impact the traditional farming of tomatoes in the open field; innovative technologies aim to tackle the adverse effects of both abiotic and biotic stress factors. It highlights advancements in crop productivity and stress tolerance, including increased phytochemicals biosynthesis, improved water use efficiency, and soil salinity tolerance. However, challenges like photooxidative damage and downregulation of anthocyanin biosynthetic genes persist. This review provides highlights of promising technologies to mitigate the impact of stress factors on open field tomato production, highlighting both qualitative and quantitative losses.</div><div>Besides sustainable systematic solutions, such as agroforestry systems, the advantages of using beneficial microbial endophytes, nanomaterials, and exogenous phytohormones in agriculture are discussed.</div></div>","PeriodicalId":34393,"journal":{"name":"Journal of Agriculture and Food Research","volume":"21 ","pages":"Article 101825"},"PeriodicalIF":4.8,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-destructive assessment of hemp seed vigor using machine learning and deep learning models with hyperspectral imaging","authors":"Damrongvudhi Onwimol ,&nbsp;Pongsan Chakranon ,&nbsp;Kris Wonggasem ,&nbsp;Papis Wongchaisuwat","doi":"10.1016/j.jafr.2025.101836","DOIUrl":"10.1016/j.jafr.2025.101836","url":null,"abstract":"<div><div>Hyperspectral imaging was employed to capture spectral information from entire trays of hemp seeds. Individual seed spectral data was extracted using a region-of-interest analysis, isolating each seed for detailed examination. To simplify the analysis and reduce computational complexity, a subset of key spectral wavelengths was selected using a successive projection algorithm. Deep learning models were trained on these selected wavelengths to directly learn patterns from the raw spectral data. The performance of these deep learning models was compared to traditional machine learning approaches. Particularly, an EfficientNetB0 convolutional neural networks achieved the most impressive results, demonstrating a high sensitivity of 98.85, a specificity of 99.22, and a Matthews correlation coefficient of 0.98. It indicated its ability to accurately distinguish between high-vigor and low-vigor hemp seeds. Our findings demonstrated the potential of data-driven models trained on hyperspectral imaging data for non-destructive assessment of hemp seed vigor. This approach offers an advantage over traditional methods, which often involve destructive testing or time-consuming manual evaluation. By enabling rapid and objective seed selection, this technology can improve the efficiency of hemp seed production and ultimately lead to higher crop yields. This innovative approach has the potential to revolutionize the agricultural industry by providing a powerful tool for assessing seed quality and optimizing crop production.</div></div>","PeriodicalId":34393,"journal":{"name":"Journal of Agriculture and Food Research","volume":"21 ","pages":"Article 101836"},"PeriodicalIF":4.8,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Formulation and assessment of modified biryani using Indian rennet, fenugreek seed, and jamun seed powder for modulating glucose indices","authors":"Misha Arooj ,&nbsp;Saher Naveed ,&nbsp;Nauman Khalid","doi":"10.1016/j.jafr.2025.101835","DOIUrl":"10.1016/j.jafr.2025.101835","url":null,"abstract":"<div><div>The prevalence of diabetes has risen globally as a result of shifting dietary habits, physical activity levels, and lifestyle choices. Several factors contribute to this prevalence, including increased consumption of local carbohydrate-rich cuisine. This study explored the antioxidant and hypoglycemic potential of herbs such as fenugreek seeds, Indian rennet, and jamun seed powder, which were later used to formulate biryani (Indo-Pakistan rice cuisine). For <em>in vitro</em> analysis, different formulations (2 %, 5 %, 7 %, and 10 % (w/v)) were prepared to assess selected herbs' antioxidant and hypoglycemic activity. The boiled and unboiled herds were evaluated for their total phenolic content, free radical scavenging activity, α−amylase, and α−glucosidase inhibitory activity. After the assessment, different treatments for biryani were prepared for sensory analysis. The results showed that the herbs exhibited good antioxidant and hypoglycemic activity in both boiled and raw aqueous extracts in a dose-dependent manner. Out of three herbs, 10 % (w/v) Fenugreek seed powder extract showed highest DPPH activity (26.9 ± 0.7 %) and phenolic contents (105.5 ± 0.2 mg GAE 100g ⁻¹). For enzyme inhibitory assays, Indian rennet powder at 10 % (w/v) demonstrated the highest α-amylase inhibitory activity of 33.8 ± 0.1 % and α-glucosidase inhibitory activity of 32.6 ± 0.5 %. The sensory evaluation results showed that all the treatments were acceptable for every sensory parameter, but T₁ (2 g) was preferred over the other treatments. Incorporation of these herbs in locally made cuisines and conventional recipes can prove beneficial in managing postprandial blood glucose levels.</div></div>","PeriodicalId":34393,"journal":{"name":"Journal of Agriculture and Food Research","volume":"21 ","pages":"Article 101835"},"PeriodicalIF":4.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unraveling the potential of microbial diversity in pesticide remediation: An eco-friendly approach for environmental sustainability","authors":"Adhi Singh ,&nbsp;Kailash Chand Kumawat","doi":"10.1016/j.jafr.2025.101832","DOIUrl":"10.1016/j.jafr.2025.101832","url":null,"abstract":"<div><div>The hap hazardous and inappropriate application of pesticides and their deposition in the soil lowers agricultural productivity and increases disease tolerance to these pesticides. The pesticide treatment at recommended and higher dosages causes a severe reduction in the numbers of nitrogen-fixing, phosphate, and zinc-solubilizing microbial communities. The uptake of pesticides by plants adversely affects the growth and productivity of crops, electron transport reactions of chloroplasts, and reduction in antioxidant defense enzymes. These are elements that agronomists find quite disturbing in intensive cropping systems under changing climatic conditions. Plant Growth-Promoting Rhizobacteria (PGPR) in the rhizosphere degrades the pesticide and uses it as a nutrient source for their growth. They are capable of producing different types of growth-enhancing bio-active molecules, including plant-hormones such as auxins, cytokinins, gibberellins, <em>etc</em>. PGPR are known to solubilize insoluble phosphate and zinc, indirectly enhancing plants' growth and expansion by synthesizing siderophore production. These numerous PGPR's activities enhance the soil's fertility, soil health, and functioning, which either directly or indirectly gain plant growth in normal and pesticide-stressful conditions. Since pesticides have disastrous effects on plants and rhizosphere biology, there is a growing interest in a variety of stress-resilient PGPR's. Their subsequent use in contemporary agriculture for pesticides breakdown highlights the need of promoting pesticide stress tolerance. The functions of soil-dwelling PGPR's in reducing pesticide stress, the supply of nutrients (nitrogen fixation and phosphorus solubilization), the generation of phytohormones, and the variables that may significantly impact their efficacy. The role of pesticide-tolerating PGPR's and the molecular pathways underlying the rhizobacteria's development of pesticide tolerance needs more investigations. Therefore, this analysis fills the void and provides an overview of PGPR's as a bio-fertilizer for agricultural sustainability under agro-chemicals stressed condition. Giving a better understanding how PGPR's tolerates and degrade agro-chemicals reduces environmental pollution brought on by overuse of pesticides increasing plant nutrient availability by means of phosphate and zinc solubilization, indole acetic acid production and etc. This review primarily focuses on the significance and necessity of pesticide-tolerant PGPR's for environmentally responsible and sustainable practices in our farming systems, particularly in pesticide-stressed conditions that will likely worsen soon due to the pesticides' residual effects. Therefore, fostering plant well-being and offering a sustainable substitute for artificial fertilizers.</div></div>","PeriodicalId":34393,"journal":{"name":"Journal of Agriculture and Food Research","volume":"21 ","pages":"Article 101832"},"PeriodicalIF":4.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genome-wide exploration of beneficial Bacillus subtilis isolate from resistant banana cultivar Anaikomban towards the management of Fusarium wilt in banana","authors":"B.R. Ajesh ,&nbsp;P. Renukadevi ,&nbsp;N. Saranya ,&nbsp;N. Vidhyashri ,&nbsp;S. Varanavasiappan ,&nbsp;S. Vellaikumar ,&nbsp;Suhail Ashraf ,&nbsp;S. Haripriya ,&nbsp;Mohammad Raish ,&nbsp;S. Nakkeeran","doi":"10.1016/j.jafr.2025.101834","DOIUrl":"10.1016/j.jafr.2025.101834","url":null,"abstract":"<div><div><em>Fusarium</em> wilt, caused by <em>Fusarium oxysporum</em> f. sp. <em>cubense</em> (<em>Foc</em>), severely impacts global banana production, urging the development of sustainable management strategies. This study conducted a comprehensive genome-wide exploration of a beneficial <em>Bacillus subtilis</em> isolate AKPS2 obtained from the resistant banana cultivar Anaikomban, with potential biocontrol capabilities against <em>Foc</em>. Through an integrated approach combining <em>in vitro</em> screening, whole genome sequencing, pangenome analysis, and advanced molecular docking techniques, we investigated the antimicrobial mechanisms of this bacterial endophyte. Initial <em>in vitro</em> dual culture assays revealed the isolate's robust antagonistic activity against <em>Foc,</em> inhibiting the fungal growth by 61.11 %, which prompted further genomic investigation. Whole genome and pangenome analyses identified the genetic repertoire underlying the isolate's biocontrol traits including antimicrobial peptides (AMPs) such as surfactin, bacillibactin, fengycin. Molecular docking identified surfactin as the most potent AMP, exhibiting the highest binding affinity (−12.1 kcal/mol) to key <em>Foc</em> target proteins. Furthermore, screening using the poison food technique confirmed the antifungal activity of surfactin, achieving an inhibition rate of 85 % against <em>Foc in vitro</em>, thereby validating the computational predictions. This comprehensive study highlights the genome-driven discovery and functional characterization of <em>B. subtilis</em> as a promising biocontrol agent, paving the way for sustainable management of <em>Fusarium</em> wilt in banana cultivation.</div></div>","PeriodicalId":34393,"journal":{"name":"Journal of Agriculture and Food Research","volume":"21 ","pages":"Article 101834"},"PeriodicalIF":4.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}