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Achieving sustainability and efficiency in maintaining high crop yields while minimizing agricultural chemical pollution requires precise weed mapping to fully deploy site-specific weed management—a persistent, major hurdle in contemporary farming. This investigation tested the resilience of training epochs for the You Only Look Once (YOLO) v5s convolutional neural network (CNN) model in building an automated system for classifying crops and weeds from UAV-acquired imagery. The images were labeled with bounding boxes and trained on Google Colab across 100, 300, 500, 600, 700, and 1000 epochs. The network identified and sorted five distinct categories: sugarcane (Saccharum officinarum), banana trees (Musa), spinach (Spinacia oleracea), pepper (Capsicum), and weeds. Training proceeded over multiple epochs to pinpoint the peak test-set performance and was terminated once the test metrics (overall accuracy, precision, and recall) began to decline. The findings reveal a marked improvement in classifier capability as the epoch count climbed from 100 to 600. However, a modest reduction surfaced at 700 epochs, where overall accuracy, weed precision, and recall registered 65, 43, and 43%, respectively, contrasted with the 67, 78, and 34% achieved on these same metrics at 600 epochs. This erosion continued at 1000 epochs, producing an overall accuracy of 65%, a weak precision of 45%, and a recall of 40%. These outcomes confirm that the training epoch length in YOLOv5s substantially governs the model’s reliability for automated crop and weed discrimination, singling out 600 epochs as the optimal performance point.

In some Asian countries, jute is a fundamental source of revenue and contributes to Gross Domestic Product (GDP). Like other crops, jute is vulnerable to pest invasions, and detecting them often relies on visual inspection in countries such as Bangladesh, India, Myanmar, and China. This practice is also slow, difficult, and somewhat inaccurate, posing a significant economic risk. To mitigate this challenge, the investigation proposes a highly accurate and resilient transfer learning (TL)- based JutePestDetect model for detecting jute pests at their initial stages. To begin with, we assembled a jute pest collection comprising 17 categories and roughly 380 images per category, which were scrutinized after manual and automated preprocessing and refinement, including background removal and scaling. Following this, five distinguished pre-trained architectures—DenseNet201, InceptionV3, MobileNetV2, VGG19, and ResNet50—were picked from an earlier work to formulate the JutePestDetect model. Every architecture was adjusted by replacing the classification layer with a global average pooling layer and inserting a dropout layer for regularization. For performance measurement, a range of indicators, including precision, recall, F1 score, ROC curve, and confusion matrix, were used. These examinations provided a deeper understanding of the models’ capabilities. Within this set, the proposed JutePestDetect model, based on a tailored regularized DenseNet201, outperformed its counterparts, achieving an outstanding accuracy of 99%. Hence, our suggested method and tactics deliver a refined solution for pest recognition specific to jute, with the potential to substantially assist cultivators worldwide.

This paper delivers an interview-based investigation and literature analysis examining the potential benefits and barriers to adopting Artificial Intelligence (AI) in farming enterprises. Three branches of agriculture are examined: crop cultivation, dairy farming, and beef farming. As groundwork for the study, a literature analysis re-evaluates earlier work on intelligent farming. Subsequently, an interview study with 21 participants examines receptivity and readiness for intelligent farming tools and provides technical detail on three selected AI applications in agriculture. Though centered on circumstances in Sweden, the results apply to the wider European agricultural context and beyond. The study uncovers a broad range of elements that both promote and hinder the transition. Chief among the recognized opportunities are the strategic weight intelligent farming carries on the agendas of numerous sector players, the overall shift toward software-as-a-service models through co-owned equipment, the enormous stockpiles of existing data, and the substantial eagerness among farmers for novel technology. Conversely, the study highlights primary obstacles, including technical and legal difficulties related to data ownership, looming cybersecurity threats, the need for a clearly articulated economic rationale, and the occasional lack of technical competence within the sector. The paper finds that the overarching direction signals a shift toward intelligent farming, but the pace of change will depend on how well the identified barriers are resolved.

Digital twins are increasingly discussed as a next step in smart farming because they promise to connect physical agricultural systems with digital representations, sensor data, simulation models, analytics, and decision-support functions. However, the agricultural literature remains uneven in how it defines, architects, enables, and applies digital twins across crop, livestock, greenhouse, irrigation, and farm-management systems. This original scoping review maps the peer-reviewed literature on digital twins in smart farming with attention to architecture types, enabling technologies, data requirements, application domains, implementation challenges, and research gaps. The review follows a scoping logic rather than an effect-estimation or performance-benchmarking design: it charts how studies conceptualize digital twins, distinguishes full or emerging digital twin capability from static models and monitoring dashboards, and classifies evidence by architecture, technology, data pipeline, analytics function, feedback mechanism, implementation stage, and limitation. The synthesis shows that agricultural digital twins are commonly positioned as cyber-physical and decision-support infrastructures that may combine IoT sensing, remote sensing, machinery and environmental data, cloud or edge computing, simulation, AI analytics, visualization, and farm-management interfaces. Application clusters are strongest in controlled-environment agriculture, irrigation and water management, crop monitoring, aquaponics, livestock monitoring, greenhouse visualization, and emerging optimization or automation settings. Across the mapped literature, the main contribution is a structured classification of digital twin architectures and application patterns while preserving conceptual caution: real-time data, AI prediction, or visualization alone does not create a digital twin. The review identifies future research needs in architecture standardization, semantic interoperability, field validation, real-time updating, uncertainty communication, data governance, farmer-centered usability, scalability, and sustainability assessment.

Data science has become a central analytical foundation of precision agriculture, enabling researchers and practitioners to transform heterogeneous farm, crop, soil, climate, machinery, sensor, and remote-sensing data into decision-relevant knowledge. This original bibliometric and thematic review examines how precision agriculture data science has evolved as a research field, how its knowledge structures are organized, and which methodological and application frontiers are shaping its future direction. The review follows a transparent bibliometric logic by distinguishing database searching, eligibility screening, metadata cleaning, keyword normalization, bibliometric indicators, thematic mapping, and interpretive synthesis. Because verified database-export counts were not available in the present working environment, the manuscript does not report invented publication totals, citation rankings, cluster sizes, or network metrics. Instead, it develops a cautious bibliometric-thematic framework grounded in the approved reference set and in clearly stated methodological safeguards. The review focuses on research evolution from early sensor analytics and remote observation toward machine learning, deep learning, big data, IoT-enabled systems, data fusion, decision support, and sustainable smart farming. It also examines knowledge structures around journals, keywords, co-occurrence logic, thematic clusters, and emerging research fronts. The main contribution is an integrated synthesis of precision agriculture data science as a field shaped by technical innovation, methodological fragmentation, data-quality constraints, interoperability challenges, explainability needs, reproducibility demands, and farmer-centered implementation concerns. The review provides a future agenda for more robust, transparent, usable, and responsible data science in precision agriculture.

Sustainable precision agriculture increasingly depends on the convergence of artificial intelligence, robotics, automation, and Internet of Things technologies rather than on isolated digital tools. This integrative review synthesizes how these technology domains interact across sensing, data transmission, analytics, decision support, robotic or automated actuation, and feedback within sustainability-oriented farming systems. The review uses a curated peer-reviewed literature selection and an integrative evidence-charting logic to map technology domains, agricultural applications, integration mechanisms, decision functions, sustainability relevance, and system-level challenges. The AI focus includes machine learning, deep learning, computer vision, predictive analytics, data fusion, and decision-support models used for crop monitoring, yield estimation, input optimization, pest and disease detection, and risk forecasting. The robotics and automation focus covers autonomous field robots, UAVs, smart machinery, task execution, and automated farm operations. The IoT and sensing focus includes wireless sensor networks, distributed monitoring, machinery telemetry, remote sensing, cloud and edge computing, and data platforms. The synthesis shows that technology convergence may support resource-efficient, climate-sensitive, and more responsive farm management, but only when data quality, interoperability, field robustness, explainability, affordability, governance, usability, and farmer or advisor feedback are addressed. The review contributes a system-level framing of sustainable precision agriculture as a socio-technical feedback system in which digital sensing, AI interpretation, automated action, and human decision-making must be jointly designed. Future research should prioritize validated, interoperable, transparent, and farmer-centered integration pathways rather than assuming that more automation or more data automatically improves sustainability outcomes.