Search Results (432)

The extensive use of conventional pesticides has been a fundamental strategy in modern agriculture for controlling pests and increasing crop productivity; however, their improper application poses significant risks to human health and environmental sustainability. This review compiles scientific evidence linking pesticide exposure to oxidative stress and genotoxic damage, particularly affecting rural populations and commonly consumed foods, even at levels exceeding the maximum permissible limits in fruits, vegetables, and animal products. Additionally, excessive pesticide use has been shown to alter soil microbiota, negatively compromising long-term agricultural fertility. In response to these challenges, recent advances in nanotechnology offer promising alternatives. This review highlights the development of nanopesticides designed for controlled release, improved stability, and targeted delivery of active ingredients, thereby reducing environmental contamination and increasing efficacy. Moreover, emerging nanobiosensor technologies, such as e-nose and e-tongue systems, have shown potential for real-time monitoring of pesticide residues and soil health. Although pesticides are still necessary, it is crucial to implement stricter laws and promote sustainable solutions that ensure safe and responsible agricultural practices. The need for evidence-based public policy is emphasized to regulate pesticide use and protect both human health and agricultural resources. Full article

Agriculture is the backbone of Punjab’s economy, and with much of India’s population dependent on agriculture, the requirement for accurate and timely monitoring of land has become even more crucial. Blending remote sensing with state-of-the-art machine learning algorithms enables the detailed classification of agricultural lands through thematic mapping, which is critical for crop monitoring, land management, and sustainable development. Here, a Hyper-tuned Deep Neural Network (Hy-DNN) model was created and used for land use and land cover (LULC) classification into four classes: agricultural land, vegetation, water bodies, and built-up areas. The technique made use of multispectral data from Sentinel-2 and Landsat-8, processed on the Google Earth Engine (GEE) platform. To measure classification performance, Hy-DNN was contrasted with traditional classifiers—Convolutional Neural Network (CNN), Random Forest (RF), Classification and Regression Tree (CART), Minimum Distance Classifier (MDC), and Naive Bayes (NB)—using performance metrics including producer’s and consumer’s accuracy, Kappa coefficient, and overall accuracy. Hy-DNN performed the best, with overall accuracy being 97.60% using Sentinel-2 and 91.10% using Landsat-8, outperforming all base models. These results further highlight the superiority of the optimised Hy-DNN in agricultural land mapping and its potential use in crop health monitoring, disease diagnosis, and strategic agricultural planning. Full article

Increased food production demands, labor shortages, and environmental concerns are driving the need for innovative agricultural technologies. However, effective adoption depends critically on aligning robot innovations with the needs of farmers. This paper examines the alignment between the needs of farmers and the robotic systems used in row crop farming. We review current commercial agricultural robots and research, and map these to the needs of farmers, as expressed in the literature, to identify the key issues holding back large-scale adoption. From initial pool of 184 research articles, 19 survey articles, and 82 commercial robotic solutions, we selected 38 peer-reviewed academic studies, 12 survey articles, and 18 commercially available robots for in-depth review and analysis for this study. We identify the key challenges faced by farmers and map them directly to the current and emerging capabilities of agricultural robots. We supplement the data gathered from the literature review of surveys and case studies with in-depth interviews with nine farmers to obtain deeper insights into the needs and day-to-day operations. Farmers reported mixed reactions to current technologies, acknowledging efficiency improvements but highlighting barriers such as capital costs, technical complexity, and inadequate support systems. There is a notable demand for technologies for improved plant health monitoring, soil condition assessment, and enhanced climate resilience. We then review state-of-the-art robotic solutions for row crop farming and map these technological capabilities to the farmers’ needs. Only technologies with field validation or operational deployment are included, to ensure practical relevance. These mappings generate insights that underscore the need for lightweight and modular robot technologies that can be adapted to diverse farming practices, as well as the need for farmers’ education and simpler interfaces to robotic operations and data analysis that are actionable for farmers. We conclude with recommendations for future research, emphasizing the importance of co-creation with the farming community to ensure the adoption and sustained use of agricultural robotic solutions. Full article

Banana Fusarium wilt (BFW, also known as Panama disease) is a highly infectious and destructive disease that threatens global banana production, requiring early recognition for timely prevention and control. Current monitoring methods primarily rely on continuous variable features—such as band reflectances (BRs) and vegetation indices (VIs)—collectively referred to as basic features (BFs)—which are prone to noise during the early stages of infection and struggle to capture subtle spectral variations, thus limiting the recognition accuracy. To address this limitation, this study proposes a discretized enhanced feature (EF) construction method, the automated kernel density segmentation-based feature construction algorithm (AutoKDFC). By analyzing the differences in the kernel density distributions between healthy and diseased samples, the AutoKDFC automatically determines the optimal segmentation threshold, converting continuous BFs into binary features with higher discriminative power for early-stage recognition. Using UAV-based multi-spectral imagery, BFW recognition models are developed and tested with the random forest (RF), support vector machine (SVM), and Gaussian naïve Bayes (GNB) algorithms. The results show that EFs exhibit significantly stronger correlations with BFW’s presence than original BFs. Feature importance analysis via RF further confirms that EFs contribute more to the model performance, with VI-derived features outperforming BR-based ones. The integration of EFs results in average performance gains of 0.88%, 2.61%, and 3.07% for RF, SVM, and GNB, respectively, with SVM achieving the best performance, averaging over 90%. Additionally, the generated BFW distribution map closely aligns with ground observations and captures spectral changes linked to disease progression, validating the method’s practical utility. Overall, the proposed AutoKDFC method demonstrates high effectiveness and generalizability for BFW recognition. Its core concept of “automatic feature enhancement” has strong potential for broader applications in crop disease monitoring and supports the development of intelligent early warning systems in plant health management. Full article

Over the past few decades, scientific interest in mycotoxins—fungal metabolites that pose serious concern to food safety, crop health, and both human and animal health—has increased. While major mycotoxins such as aflatoxins, ochratoxins, deoxynivalenol, fumonisins, zearalenone, citrinin, patulin, and ergot alkaloids are well studied, emerging mycotoxins remain underexplored and insufficiently investigated. Among these, moniliformin (MON) is frequently detected in maize-based food and feed; however, the absence of regulatory limits and standardized detection methods limits effective monitoring and comprehensive risk assessment. The European Food Safety Authority highlights insufficient occurrence and toxicological data as challenges to regulatory development. This study compares three analytical methods—CE-DAD, HPLC-DAD, and HPLC-MS/MS—for moniliformin detection and quantification in maize, evaluating linear range, correlation coefficients, detection and quantification limits, accuracy, and precision. Results show that CE-DAD and HPLC-MS/MS provide reliable and comparable sensitivity and selectivity, while HPLC-DAD is less sensitive. Application to real samples enabled deterministic dietary exposure estimation based on consumption, supporting preliminary risk characterization. This research provides a critical comparison that supports the advancement of improved monitoring and risk assessment frameworks, representing a key step toward innovating the detection of under-monitored mycotoxins and laying the groundwork for future regulatory and preventive measures targeting MON. Full article

According to Victor Ernest Shelford’s ‘Law of Tolerance,’ organisms within ecosystems thrive optimally when environmental conditions are favorable. Applying this principle to ecosystems and agro-ecosystems facing water scarcity or environmental challenges can significantly enhance their productivity. In these ecosystems, phytocenosis adjusts its conditions by utilizing water with varying salinity levels. Moreover, establishing optimal drinking water conditions for human populations within an ecosystem can help mitigate future negative succession processes. The purpose of this study is to evaluate the quality of two distinct water sources in the Amudarya district of the Republic of Karakalpakstan, Uzbekistan: collector-drainage water and groundwater at depths of 10 to 25 m. This research is highly relevant in the context of climate change, as improper management of water salinity, particularly in collector-drainage water, may exacerbate soil salinization and degrade drinking water quality. The primary methodology of this study is as follows: The Food and Agriculture Organization of the United Nations (FAO) standard for collector-drainage water is applied, and the water quality index is assessed using the CCME WQI model. The Canadian Council of Ministers of the Environment (CCME) model is adapted to assess groundwater quality using Uzbekistan’s national drinking water quality standards. The results of two years of collected data, i.e., 2021 and 2023, show that the water quality index of collector-drainage water indicates that it has limited potential for use as secondary water for the irrigation of sensitive crops and has been classified as ‘Poor’. As a result, salinity increased by 8.33% by 2023. In contrast, groundwater quality was rated as ‘Fair’ in 2021, showing a slight deterioration by 2023. Moreover, a comparative analysis of CCME WQI values for collector-drainage and groundwater in the region, in conjunction with findings from Ethiopia, India, Iraq, and Turkey, indicates a consistent decline in water quality, primarily due to agriculture and various other anthropogenic pollution sources, underscoring the critical need for sustainable water resource management. This study highlights the need to use organic fertilizers in agriculture to protect drinking water quality, improve crop yields, and promote soil health, while reducing reliance on chemical inputs. Furthermore, adopting WQI models under changing climatic conditions can improve agricultural productivity, enhance groundwater quality, and provide better environmental monitoring systems. Full article

The detection of plant constituents is a crucial issue in precision agriculture, as monitoring these enables the automatic analysis of factors such as growth rate, health status, and crop yield. Tomatoes (Solanum sp.) are an economically and nutritionally important crop in Mexico and worldwide, which is why automatic monitoring of these plants is of great interest. Detecting leaves on images of outdoor tomato plants is challenging due to the significant variability in the visual appearance of leaves. Factors like overlapping leaves, variations in lighting, and environmental conditions further complicate the task of detection. This paper proposes modifications to the Yolov11n architecture to improve the detection of tomato leaves in images of complex outdoor environments by incorporating attention modules, transformers, and WIoUv3 loss for bounding box regression. The results show that our proposal led to a 26.75% decrease in the number of parameters and a 7.94% decrease in the number of FLOPs compared with the original version of Yolov11n. Our proposed model outperformed Yolov11n and Yolov12n architectures in recall, F1-measure, and mAP@50 metrics. Full article

The demand for rapid, portable, and cost-effective analytical tools has driven advances in smartphone-based microfluidic sensors. By combining microfluidic precision with the accessibility and processing power of smartphones, these devices offer real-time and on-site diagnostic capabilities. This review explores recent developments in smartphone-integrated microfluidic sensors, focusing on their design, fabrication, smartphone integration, and analytical functions with the applications in forensic science, agriculture, and environmental monitoring. In forensic science, these sensors provide fast, field-based alternatives to traditional lab methods for detecting substances like DNA, drugs, and explosives, improving investigation efficiency. In agriculture, they support precision farming by enabling on-demand analysis of soil nutrients, water quality, and plant health, enhancing crop management. In environmental monitoring, these sensors allow the timely detection of pollutants in air, water, and soil, enabling quicker responses to hazards. Their portability and user-friendliness make them particularly valuable in resource-limited settings. Overall, this review highlights the transformative potential of smartphone-based microfluidic sensors in enabling accessible, real-time diagnostics across multiple disciplines. Full article

Timely and accurate identification of plant diseases is critical to mitigating crop losses and enhancing yield in precision agriculture. This paper proposes AgriFusionNet, a lightweight and efficient deep learning model designed to diagnose plant diseases using multimodal data sources. The framework integrates RGB and multispectral drone imagery with IoT-based environmental sensor data (e.g., temperature, humidity, soil moisture), recorded over six months across multiple agricultural zones. Built on the EfficientNetV2-B4 backbone, AgriFusionNet incorporates Fused-MBConv blocks and Swish activation to improve gradient flow, capture fine-grained disease patterns, and reduce inference latency. The model was evaluated using a comprehensive dataset composed of real-world and benchmarked samples, showing superior performance with 94.3% classification accuracy, 28.5 ms inference time, and a 30% reduction in model parameters compared to state-of-the-art models such as Vision Transformers and InceptionV4. Extensive comparisons with both traditional machine learning and advanced deep learning methods underscore its robustness, generalization, and suitability for deployment on edge devices. Ablation studies and confusion matrix analyses further confirm its diagnostic precision, even in visually ambiguous cases. The proposed framework offers a scalable, practical solution for real-time crop health monitoring, contributing toward smart and sustainable agricultural ecosystems. Full article

Nanomaterials have emerged as a transformative technology in agricultural science, offering innovative solutions to improve plant–microbe interactions and crop productivity. The unique properties, such as high surface area, tunability, and reactivity, of nanomaterials, including nanoparticles, carbon-based materials, and electrospun fibers, render them ideal for applications such as nutrient delivery systems, microbial inoculants, and environmental monitoring. This review explores various types of nanomaterials employed in agriculture, focusing on their role in enhancing microbial colonization and soil health and optimizing plant growth. Key nanofabrication techniques, including top-down and bottom-up manufacturing, electrospinning, and nanoparticle synthesis, are discussed in relation to controlled release systems and microbial inoculants. Additionally, the influence of surface properties such as charge, porosity, and hydrophobicity on microbial adhesion and colonization is examined. Moreover, the potential of nanocoatings and electrospun fibers to enhance seed protection and promote beneficial microbial interactions is investigated. Furthermore, the integration of nanosensors for detecting pH, reactive oxygen species, and metabolites offers real-time insights into the biochemical dynamics of plant–microbe systems, applicable to precision farming. Finally, the environmental and safety considerations regarding the use of nanomaterials, including biodegradability, nanotoxicity, and regulatory concerns, are addressed. This review emphasizes the potential of nanomaterials to revolutionize sustainable agricultural practices by improving crop health, nutrient efficiency, and environmental resilience. Full article

Crop growth monitoring at various growth stages is essential for optimizing agricultural inputs and enhancing crop yield. Nitrogen plays a critical role in plant development; however, its improper application can reduce productivity and, in the long term, degrade soil health. The aim of this study was to develop a non-invasive approach for nitrogen estimation through proxies (Nitrogen Flavanol Index) in Mentha arvensis using UAV-derived multispectral vegetation indices and machine learning models. Support Vector Regression, Random Forest, and Gradient Boosting were used to predict the Nitrogen Flavanol Index (NFI) across different growth stages. Among the tested models, Random Forest achieved the highest predictive accuracy (R² = 0.86, RMSE = 0.32) at 75 days after planting (DAP), followed by Gradient Boosting (R² = 0.75, RMSE = 0.43). Model performance was lowest during early growth stages (15–30 DAP) but improved markedly from mid to late growth stages (45–90 DAP). The findings highlight the significance of UAV-acquired data coupled with machine learning approaches for non-destructive nitrogen flavanol estimation, which can immensely contribute to improving real-time crop growth monitoring. Full article

The integration of computer vision (CV) and generative artificial intelligence (GenAI) into smart agriculture has revolutionised traditional farming practices by enabling real-time monitoring, automation, and data-driven decision-making. This review systematically examines the applications of CV in key agricultural domains, such as crop health monitoring, precision farming, harvesting automation, and livestock management, while highlighting the transformative role of GenAI in addressing data scarcity and enhancing model robustness. Advanced techniques, including convolutional neural networks (CNNs), YOLO variants, and transformer-based architectures, are analysed for their effectiveness in tasks like pest detection, fruit maturity classification, and field management. The survey reveals that generative models, such as generative adversarial networks (GANs) and diffusion models, significantly improve dataset diversity and model generalisation, particularly in low-resource scenarios. However, challenges persist, including environmental variability, edge deployment limitations, and the need for interpretable systems. Emerging trends, such as vision–language models and federated learning, offer promising avenues for future research. The study concludes that the synergy of CV and GenAI holds immense potential for advancing smart agriculture, though scalable, adaptive, and trustworthy solutions remain critical for widespread adoption. This comprehensive analysis provides valuable insights for researchers and practitioners aiming to harness AI-driven innovations in agricultural ecosystems. Full article

Aflatoxins, toxic secondary metabolites produced primarily by Aspergillus flavus and Aspergillus parasiticus, pose a significant global health concern due to their frequent presence in crops, food, and feed—especially under climate change conditions. This review addresses the growing threat of aflatoxins by analyzing recent advances in detection and mitigation. A comprehensive literature review was conducted, focusing on bioremediation, physical and chemical detoxification, and fungal growth inhibition strategies. The occurrence of aflatoxins in water systems was also examined, along with current detection techniques, removal processes, and regulatory frameworks. Emerging technologies such as molecular diagnostics, immunoassays, biosensors, and chromatographic methods are discussed for their potential to improve monitoring and control. Key findings highlight the increasing efficacy of integrative approaches combining biological and technological solutions and the potential of AI-based tools and portable devices for on-site detection. Intelligent packaging and transgenic crops are also explored for their role in minimizing contamination at the source. Overall, this review emphasizes the importance of continued interdisciplinary research and the development of sustainable, adaptive strategies to mitigate aflatoxin risks, thereby supporting food safety and public health in the face of environmental challenges. Full article

Hyperspectral imaging (HSI) plays a pivotal role in modern agriculture by capturing fine-grained spectral signatures that support crop classification, health assessment, and land-use monitoring. However, the transition from raw spectral data to reliable semantic understanding remains challenging—particularly under fragmented planting patterns, spectral ambiguity, and spatial heterogeneity. To address these limitations, we propose UniHSFormer-X, a unified transformer-based framework that reconstructs agricultural semantics through prototype-guided token routing and hierarchical context modeling. Unlike conventional models that treat spectral–spatial features uniformly, UniHSFormer-X dynamically modulates information flow based on class-aware affinities, enabling precise delineation of field boundaries and robust recognition of spectrally entangled crop types. Evaluated on three UAV-based benchmarks—WHU-Hi-LongKou, HanChuan, and HongHu—the model achieves up to 99.80% overall accuracy and 99.28% average accuracy, outperforming state-of-the-art CNN, ViT, and hybrid architectures across both structured and heterogeneous agricultural scenarios. Ablation studies further reveal the critical role of semantic routing and prototype projection in stabilizing model behavior, while parameter surface analysis demonstrates consistent generalization across diverse configurations. Beyond high performance, UniHSFormer-X offers a semantically interpretable architecture that adapts to the spatial logic and compositional nuance of agricultural imagery, representing a forward step toward robust and scalable crop classification. Full article

Aflatoxins, toxic secondary metabolites produced primarily by Aspergillus flavus and Aspergillus parasiticus, pose significant risks to food safety, public health, and global trade. These mycotoxins contaminate staple crops such as maize and peanuts, particularly in warm and humid regions, leading to economic losses and severe health effects, including hepatocellular carcinoma, immune suppression, and growth impairment. In addition to aflatoxins, Aspergillus species produce other toxic metabolites such as ochratoxin A, sterigmatocystin, and cyclopiazonic acid, which are associated with nephrotoxic, carcinogenic, and neurotoxic effects, respectively. This review provides a comprehensive analysis of aflatoxin toxicity, mitigation strategies, and chemical detection methods. The toxicity of aflatoxins is discussed in relation to their biochemical mechanisms, carcinogenicity, and synergistic effects with other mycotoxins. Various mitigation approaches, including pre-harvest biocontrol, post-harvest storage management, and novel detoxification methods such as enzymatic degradation and nanotechnology-based interventions, are evaluated. Furthermore, advances in aflatoxin detection, including chromatographic, immunoassay, and biosensor-based methods, are explored to improve regulatory compliance and food safety monitoring. This review underscores the need for integrated management strategies and global collaboration to reduce aflatoxin contamination and its associated health and economic burdens. Future research directions should focus on genetic engineering for resistant crop varieties, climate adaptation strategies, and improved risk assessment models. Full article