Clara de Goes Monteiro de Carvalho Guimaraes, Pablo Jose Francisco Pena Rodrigues
Human activity has an enormous impact on Earth, changing organisms, environments and landscapes, leading to the decline of original ecosystems and irreversible changes that create new combinations of living beings and materials. As a result, ecosystems with new properties and new species pools are emerging. Here, we explore a set of transformative drivers, which can act either individually or in synergy. The expansion of novel ecosystems (hybrids of natural and agricultural systems) is a sign of irreversible, human-induced change. Human growth, adaptation to climate change, urban expansion and geoengineering are powerful transformative drivers which are expected to have a high impact, creating novel ecosystems. In contrast, less transformative drivers such as degrowth, biocentrism, ecological restoration and low-impact agriculture can mitigate human impacts, leading to adaptation, resilience and sustainability, while conserving original ecosystems. This requires a new approach, incorporating new ecological, ethical and cultural perspectives, to keep ecosystems functional and healthy.
Mohammadreza Narimani, Alireza Pourreza, Ali Moghimi
et al.
Accurate and timely crop yield estimation is critical for global food security, agricultural policy, and farm management. The Copernicus Sentinel-2 satellite constellation, with high spatial, temporal, and spectral resolution, has transformed agricultural monitoring by enabling field- and sub-field-scale analysis. This review synthesizes recent advances in Sentinel-2-based crop yield estimation. A key trend is the shift from regional models to high-resolution field-level assessments driven by three main approaches: (i) empirical models using vegetation indices combined with machine and deep learning methods such as Random Forest and Convolutional Neural Networks; (ii) integration of process-based crop growth models (e.g., WOFOST, SAFY) via data assimilation of Sentinel-2-derived variables like Leaf Area Index (LAI); and (iii) data fusion techniques combining Sentinel-2 optical data with Sentinel-1 SAR to mitigate cloud-related limitations. The review shows that machine learning, deep learning, and hybrid modeling frameworks can explain substantial within-field yield variability across crops and regions. However, performance remains constrained by limited ground-truth data, cloud-induced gaps, and challenges in model transferability across years and locations. Future directions include tighter integration of multi-modal data and improved in-season observations to support robust, operational decision-making in precision agriculture and sustainable intensification.
Soybean mosaic virus (SMV), a pathogen responsible for inducing leaf mosaic or necrosis symptoms, significantly compromises soybean seed yield and quality. According to the classification system in the United States, SMV is categorized into seven distinct strains (G1 to G7). In this study, we performed a genome-wide association study (GWAS) in GAPIT3 using four analytical models (MLM, MLMM, FarmCPU, and BLINK) on 218 soybean accessions. We identified 22 SNPs significantly associated with G1 resistance across chromosomes 1, 2, 3, 12, 13, 17, and 18. Notably, a major quantitative trait locus (QTL) spanning 873 kb (29.85–30.73 Mb) on chromosome 13 exhibited strong association with SMV G1 resistance, including the four key SNP markers: Gm13_29459954_ss715614803, Gm13_29751552_ss715614847, Gm13_30293949_ss715614951, and Gm13_30724301_ss715615024. Within this QTL, four candidate genes were identified: Glyma.13G194100, Glyma.13G184800, Glyma.13G184900, and Glyma.13G190800 (3Gg2). The genomic prediction (GP) accuracies ranged from 0.60 to 0.83 across three GWAS-derived SNP sets using five models, demonstrating the feasibility of GP for SMV-G1 resistance. These findings could provide a useful reference in soybean breeding targeting SMV-G1 resistance.
Nur Fariha Amir, Aslizah Mohd-Aris, Tuan Norhafizah Tuan-Zakaria
et al.
Background: Volvariella volvacea is a highly nutritious edible mushroom grown mainly in Southeast Asian countries. However, the low yield of V. volvacea has discouraged farmers from engaging in its production. Objective: The study was conducted to observe the improvement of V. volvacea yield depending on various physiochemical parameters of V. volvacea growth. Methods: The parameters tested in this study include the weight of the substrate, i.e., 2 kg (W1) and 6 kg (W2); the surface area of the substrate: A1 (1218 cm2), A2 (1530 cm2) and A3 (2000 cm2); and four different substrate formulations (F1, F2, F3 and F4). Results: Substrate weight and surface area were found to be important, but not critical, factors in determining fruiting bodies formation, total fungal mass, and BE rate. However, the formulation media showed a significant contribution that could help in the induction of fruiting bodies. According to the results, the culture medium with a mixture of EFB substrate and black soil showed the highest BE percentage of 17.75 % (at optimised substrate weights = 2 kg). Conclusion: The results of this study can be used as a reference for further studies to improve the cultivation of V. volvacea, especially when EFB fibres are used as the main substrate. Future studies to identify genes involved in the formation of fruiting bodies are strongly recommended.
Tafesse Kibatu, Sebsebe Demissew, Diriba Muleta
et al.
Enset is a staple food for approximately 25% of Ethiopia’s population. It is threatened by a range of biotic and abiotic stress, of which bacterial wilt is the most significant. This study investigated the enset bacterial wilt (EBW) status on farms in Gedeo, Kembata Tembaro, Gurage, Hadiya zones, and the Basketo special woreda of Southern Ethiopia. In addition, infected enset plant samples were collected from Hadiya, Kembata Tembaro, and Gedeo zones to assess bacterial strain diversity using physicochemical and morphological approaches. Representative Kebeles were selected using purposive sampling based on their agroecological conditions. Data was collected through in-depth interviews, questionnaires, group discussions, and field observation. The morphology of bacterial wilt isolates was characterized by color, texture, form, elevation, margin, and motility. In addition, a combination of oxidase, aesculin hydrolysis, catalase, gram reaction, hydrogen sulfide (H2S), gelatin liquefaction, and fructose, lactose, mannitol, and sorbitol utilization tests were conducted to capture physiochemical differences. Tolerance to salt and high temperatures was also evaluated. The bacterial wilt impact varies significantly across enset growing regions, with highlands experiencing the highest. This research emphasizes the importance of assessing both spatial and temporal variation, as well as integrating local knowledge and robust scientific approaches for effective bacterial wilt management and enset landrace conservation efforts. The research also provides valuable insights into the characteristics of bacterial wilt isolates in Southern Ethiopia. Analyses of morphology, potassium hydroxide solubility, catalase activity, and carbohydrate utilization were consistent, however, variations in bacterial isolates response to tests of easculin, oxidase, gelatin liquid, H2S tests and response to osmotic and temperature exposures. This study reveals a strong association between the bacterial wilt effect and the enset growing regions. EBW exhibits seasonal fluctuations. Bacterial wilt isolates displayed consistent morphological characteristics. All isolates similarly utilized sorbitol, mannitol, lactose, and fructose carbohydrates. All isolates exhibited positive potassium hydroxide solubility and catalase activity. However, the isolates displayed variations in responses to easculin, oxidase, gelatin liquefaction, and H2S production. The isolates also displayed variations in tolerance to salt and high temperatures. These variations can be valuable for understanding disease epidemiology and management.
Bacillus bacteria are critical functional strains during fermentation of Sichuan bran vinegar that contribute to maintain the quality of vinegar. In this study, some functional Bacillus bacteria were isolated from the Cupei (grains undergoing acetic acid fermentation) of Sichuan bran vinegar, and the strains were identified by biochemical tests and 16S rRNA sequencing. The fermentation properties of these strains were studied, and the 5 Bacillus strains with great fermentation performance were discovered. They were identified as Paenibacillus cookii BA-6, Bacillus velezensis BA-9, Bacillus tropicus BB-5, Bacillus subtilis BB-13, and Calidifontibacillus erzurumensis BC-11. The five strains grew well at 30~40 ℃ and pH5~7 (OD600=0.23~0.52), with a certain degree of tolerance for environmental factor. Some of the Bacillus strains had a strong ability to hydrolyze protein (proteolytic ring of B. velezensis BA-9: 3.7±0.3) and starch (starch hydrolysis ring of P. cookie BA-6: 3.8±0.5). The simulated solid-state fermentation of inoculation with Bacillus and Acetobacter was carried out to investigate their potential use in industrial bran vinegar fermentation. Results showed that inoculation of P. cookie BA-6, B. velezensis BA-9, and C. erzurumensis BC-11 could increase total acid, lactic acid, acetic acid, and volatile flavor compounds content (e.g., phenyl ethanol, benzyl alcohol, isopentanol, and guaicol), further raise the overall fermentation quality of the Cupei. Overall, the 5 strains of Bacillus identified in the study were beneficial for the formation of flavor compounds during the Cupei fermentation of bran vinegar. The study provides a theoretical foundation for enhancing the quality of Sichuan bran vinegar.
Abstract Streptococcus mutans mediates enamel demineralization through acid production via glycolysis, while Streptococcus salivarius, as a commensal bacterium, promotes caries progression by enhancing biofilm formation. Their synergistic interaction amplifies cariogenicity. Therefore, developing strategies to inhibit both bacterial species is imperative. This study investigated the extraction and characterization of a polysaccharide from mangosteen scarfskin (MSP) and its antimicrobial potential against cariogenic bacteria. Using ultrasonic-assisted extraction, MSP was obtained with a yield of (9.93 ± 0.5696)%, presenting light brown coloration. Antimicrobial assays demonstrated strong anti-efficacy activity against Streptococcus mutans and Streptococcus salivarius, showing a MIC of 1 mg/mL and significant bactericidal effects at 1×MIC and 2×MIC concentrations. Biofilm metabolism analysis showed that MSP caused dose-dependent suppression of bacterial metabolism, while its inhibitory effect on EPS production increased proportionally with concentration. Molecular docking identified specific hydrogen-bond interactions between arabinose (the primary component of MSP) and key residues (THR-315, SER-10, and SER-247) of glucosyltransferase-C (GTF-C), while molecular dynamics simulations demonstrated that arabinose disrupted the structural stability of GTF-C. These findings collectively suggest MSP’s promising application as a novel food additive for caries prevention through oral streptococcal control. Graphical abstract
Abstract Cotton has, in recent years, become one of the most important cash crops worldwide while being impacted in yield from leaf disease which generally goes unnoticed in the early stage. Detection methods depend on manual efforts producing slow processes and human errors. Automated detection methods establish low accuracies, limited scalability and real time applications. To tackle the research issue, this study proposes the CLD-Net which stands for Cotton Leaf Disease Detection Network a novel deep learning-based framework which combines Faster-RCNN and YOLOv5 algorithms into a single action to achieve ultimately real time detection of accurate diseases the combination helps identify both the high detection speed of YOLOv5 along with Faster-RCNN regional proposal accuracy. The new method is that the compilation of these two modern object detection methods has been compiled and designed specifically for detecting leaf disease across varying environmental conditions. Notable contributions to this method include increases in classification accuracy, processing speed, real time detection making these methods suitable for farmers agronomists and sensor deployment. CLD-Net integrates YOLOv5 and Faster R-CNN, combining real-time detection capability with precise classification, to deliver robust cotton leaf disease identification. Experimental validation on a curated dataset of cotton leaf images demonstrates the superiority of CLD-Net, achieving an accuracy of 96.7%, which surpasses that of traditional models. These results confirm the potential of the proposed approach to revolutionize crop disease detection, leading to timely intervention and increased yield.
Weed detection is a critical component of precision agriculture, facilitating targeted herbicide application and reducing environmental impact. However, deploying accurate object detection models on resource-limited platforms remains challenging, particularly when differentiating visually similar weed species commonly encountered in plant phenotyping applications. In this work, we investigate Channel-wise Knowledge Distillation (CWD) and Masked Generative Distillation (MGD) to enhance the performance of lightweight models for real-time smart spraying systems. Utilizing YOLO11x as the teacher model and YOLO11n as both reference and student, both CWD and MGD effectively transfer knowledge from the teacher to the student model. Our experiments, conducted on a real-world dataset comprising sugar beet crops and four weed types (Cirsium, Convolvulus, Fallopia, and Echinochloa), consistently show increased AP50 across all classes. The distilled CWD student model achieves a notable improvement of 2.5% and MGD achieves 1.9% in mAP50 over the baseline without increasing model complexity. Additionally, we validate real-time deployment feasibility by evaluating the student YOLO11n model on Jetson Orin Nano and Raspberry Pi 5 embedded devices, performing five independent runs to evaluate performance stability across random seeds. These findings confirm CWD and MGD as an effective, efficient, and practical approach for improving deep learning-based weed detection accuracy in precision agriculture and plant phenotyping scenarios.
Abdulrahman Bukhari, Bullo Mamo, Mst Shamima Hossain
et al.
With rising demands for efficient disease and salinity management in agriculture, early detection of plant stressors is crucial, particularly for high-value crops like avocados. This paper presents a comprehensive evaluation of low-cost sensors deployed in the field for early stress and disease detection in avocado plants. Our monitoring system was deployed across 72 plants divided into four treatment categories within a greenhouse environment, with data collected over six months. While leaf temperature and conductivity measurements, widely used metrics for controlled settings, were found unreliable in field conditions due to environmental interference and positioning challenges, leaf spectral measurements produced statistically significant results when combined with our machine learning approach. For soil data analysis, we developed a two-level hierarchical classifier that leverages domain knowledge about treatment characteristics, achieving 75-86\% accuracy across different avocado genotypes and outperforming conventional machine learning approaches by over 20\%. In addition, performance evaluation on an embedded edge device demonstrated the viability of our approach for resource-constrained environments, with reasonable computational efficiency while maintaining high classification accuracy. Our work bridges the gap between theoretical potential and practical application of low-cost sensors in agriculture and offers insights for developing affordable, scalable monitoring systems.
Calvin Kammerlander, Viola Kolb, Marinus Luegmair
et al.
Efficient nutrient management and precise fertilization are essential for advancing modern agriculture, particularly in regions striving to optimize crop yields sustainably. The AgroLens project endeavors to address this challenge by develop ing Machine Learning (ML)-based methodologies to predict soil nutrient levels without reliance on laboratory tests. By leveraging state of the art techniques, the project lays a foundation for acionable insights to improve agricultural productivity in resource-constrained areas, such as Africa. The approach begins with the development of a robust European model using the LUCAS Soil dataset and Sentinel-2 satellite imagery to estimate key soil properties, including phosphorus, potassium, nitrogen, and pH levels. This model is then enhanced by integrating supplementary features, such as weather data, harvest rates, and Clay AI-generated embeddings. This report details the methodological framework, data preprocessing strategies, and ML pipelines employed in this project. Advanced algorithms, including Random Forests, Extreme Gradient Boosting (XGBoost), and Fully Connected Neural Networks (FCNN), were implemented and finetuned for precise nutrient prediction. Results showcase robust model performance, with root mean square error values meeting stringent accuracy thresholds. By establishing a reproducible and scalable pipeline for soil nutrient prediction, this research paves the way for transformative agricultural applications, including precision fertilization and improved resource allocation in underresourced regions like Africa.
The increasing complexity and temporal variability of workloads on MIG-enabled GPUs challenge the scalability of traditional centralized scheduling. Building upon the SJA concept, this paper introduces JASDA-a novel paradigm that extends SJA from a largely centralized scheduling model toward a fully decentralized negotiation process. In JASDA, jobs actively generate and score feasible subjobs in response to scheduler-announced execution windows, while the scheduler performs policy-driven clearing that balances utilization, fairness, and temporal responsiveness. This bidirectional, iterative interaction embeds feedback, calibration, and probabilistic safety directly into the scheduling loop, enabling adaptive and transparent decision-making. By coupling principles from auction theory and online optimization with the temporal granularity of GPU workloads, JASDA provides a scalable foundation for market-aware and fairness-driven resource management-bridging theoretical scheduling models with practical deployment in modern MIG-enabled environments relevant to Artificial Intelligence and Agriculture 4.0.
India, as a predominantly agrarian economy, faces significant challenges in agriculture, including substantial crop losses caused by diseases, pests, and environmental stress. Early detection and accurate identification of diseases across different crops are critical for improving yield and ensuring food security. This paper proposes a deep learning based solution for detecting multiple diseases in multiple crops, aimed to cover India's diverse agricultural landscape. We first create a unified dataset encompassing images of 17 different crops and 34 different diseases from various available repositories. Proposed deep learning model is trained on this dataset and outperforms the state-of-the-art in terms of accuracy and the number of crops, diseases covered. We achieve a significant detection accuracy, i.e., 99 percent for our unified dataset which is 7 percent more when compared to state-of-the-art handling 14 crops and 26 different diseases only. By improving the number of crops and types of diseases that can be detected, proposed solution aims to provide a better product for Indian farmers.
Md Zahurul Haquea, Yeahyea Sarker, Muhammed Farhan Sadique Mahi
et al.
Dragon fruit, renowned for its nutritional benefits and economic value, has experienced rising global demand due to its affordability and local availability. As dragon fruit cultivation expands, efficient pre- and post-harvest quality inspection has become essential for improving agricultural productivity and minimizing post-harvest losses. This study presents DragonFruitQualityNet, a lightweight Convolutional Neural Network (CNN) optimized for real-time quality assessment of dragon fruits on mobile devices. We curated a diverse dataset of 13,789 images, integrating self-collected samples with public datasets (dataset from Mendeley Data), and classified them into four categories: fresh, immature, mature, and defective fruits to ensure robust model training. The proposed model achieves an impressive 93.98% accuracy, outperforming existing methods in fruit quality classification. To facilitate practical adoption, we embedded the model into an intuitive mobile application, enabling farmers and agricultural stakeholders to conduct on-device, real-time quality inspections. This research provides an accurate, efficient, and scalable AI-driven solution for dragon fruit quality control, supporting digital agriculture and empowering smallholder farmers with accessible technology. By bridging the gap between research and real-world application, our work advances post-harvest management and promotes sustainable farming practices.