Smart agricultural technology最新文献

{"title":"Artificial Intelligence-based Rice Variety Classification: A State-of-the-art Review and Future Directions","authors":"Md. Masudul Islam ,&nbsp;Galib Muhammad Shahriar Himel ,&nbsp;Md. Golam Moazzam ,&nbsp;Mohammad Shorif Uddin","doi":"10.1016/j.atech.2025.100788","DOIUrl":"10.1016/j.atech.2025.100788","url":null,"abstract":"<div><div>Rice is a staple food for a significant portion of the global population, making accurate classification of rice varieties essential for farming and consumer protection. This review provides a focused analysis of the current advancements and challenges in applying computer vision (CV) techniques to rice variety classification. The study examines key steps in the automation process, including image acquisition, pre-processing, feature extraction, and classification algorithms, with particular emphasis on machine learning and deep learning methods such as Convolutional Neural Networks (CNNs), which have demonstrated exceptional performance in recent research. However, practical implementation faces challenges, including the availability of high-quality datasets, the impact of environmental variations on image quality, and the computational demands of complex models. Our study discusses these obstacles and highlights the importance of developing resilient and scalable systems for real-world applications. By synthesizing findings from various studies, this review proposes future directions for advancing rice variety classification, focusing on improved feature extraction techniques, enhanced dataset management, and integrating innovative machine learning paradigms. This work is a valuable resource for researchers and practitioners aiming to advance rice classification technologies and contribute to food security and agricultural sustainability.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100788"},"PeriodicalIF":6.3,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182135","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":"Smart glasses in the chicken barn: Enhancing animal welfare through mixed reality","authors":"Dorian Baltzer ,&nbsp;Shannon Douglas ,&nbsp;Jan-Henrik Haunert ,&nbsp;Youness Dehbi ,&nbsp;Inga Tiemann","doi":"10.1016/j.atech.2025.100786","DOIUrl":"10.1016/j.atech.2025.100786","url":null,"abstract":"<div><div>Livestock production requires a thorough understanding of animal welfare to increase productivity and ensure appropriate housing conditions. The expanding availability of consumer-grade virtual and augmented reality devices opens new possibilities for precision livestock farming (PLF), where sensor technology traditionally monitors real-time animal data. In poultry farming, monitoring each bird individually is often not economically feasible due to the large flock sizes. To address this issue, we propose a novel method to evaluate housing conditions by focusing on the visual and temperature preferences of domestic chickens, considering these factors within a broader environmental context. Chickens perceive light at a wider range of wavelengths than humans, which significantly influences their behavior. Additionally, temperature variations, such as heat leaks and accumulations, can contribute to stress and negative behaviors in the flock. We developed a device comprising smart glasses equipped with specialized cameras to capture thermal infrared, ultraviolet, and visible RGB (red, green, blue) light, alongside real-time user position tracking. Points of interest (POIs) can be added to the logged tracking data along with captured content. The data collected by the glasses can be used to create virtual tours embedded in a 3D model of the barn, providing a comprehensive view of on-site conditions.</div><div>We also introduce a streamlined pipeline for building these virtual tours using the Unity game engine, making the content accessible for agricultural education. This approach enables users to remotely gain insights into the housing conditions of poultry without needing a physical visit, enhancing both learning and engagement in animal welfare practices.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100786"},"PeriodicalIF":6.3,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182127","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":"Synthetic data generation for anomaly detection on table grapes","authors":"Ionut M. Motoi ,&nbsp;Valerio Belli ,&nbsp;Alberto Carpineto ,&nbsp;Daniele Nardi,&nbsp;Thomas A. Ciarfuglia","doi":"10.1016/j.atech.2025.100787","DOIUrl":"10.1016/j.atech.2025.100787","url":null,"abstract":"<div><div>Early detection of illnesses and pest infestations in fruit cultivation is critical for maintaining yield quality and plant health. Computer vision and robotics are increasingly employed for the automatic detection of such issues, particularly using data-driven solutions. However, the rarity of these problems makes acquiring and processing the necessary data to train such algorithms a significant obstacle. One solution to this scarcity is the generation of synthetic high-quality anomalous samples. While numerous methods exist for this task, most require highly trained individuals for setup.</div><div>This work addresses the challenge of generating synthetic anomalies in an automatic fashion that requires only an initial collection of normal and anomalous samples from the user, a task that is straightforward for farmers. We demonstrate the approach in the context of table grape cultivation. Specifically, based on the observation that normal berries present relatively smooth surfaces, while defects result in more complex textures, we introduce a Dual-Canny Edge Detection (DCED) filter. This filter emphasizes the additional texture indicative of diseases, pest infestations, or other defects. Using segmentation masks provided by the Segment Anything Model, we then select and seamlessly blend anomalous berries onto normal ones. We show that the proposed dataset augmentation technique improves the accuracy of an anomaly classifier for table grapes and that the approach can be generalized to other fruit types.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100787"},"PeriodicalIF":6.3,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182130","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":"Smart technologies for sustainable pasture-based ruminant systems: A review","authors":"Sara Marchegiani ,&nbsp;Giulia Gislon ,&nbsp;Rosaria Marino ,&nbsp;Mariangela Caroprese ,&nbsp;Marzia Albenzio ,&nbsp;William E Pinchak ,&nbsp;Gordon E Carstens ,&nbsp;Luigi Ledda ,&nbsp;Maria Federica Trombetta ,&nbsp;Anna Sandrucci ,&nbsp;Marina Pasquini ,&nbsp;Paola Antonia Deligios ,&nbsp;Simone Ceccobelli","doi":"10.1016/j.atech.2025.100789","DOIUrl":"10.1016/j.atech.2025.100789","url":null,"abstract":"<div><div>Ruminant livestock farming is essential for providing high-value protein foods for humanity. Nevertheless, the environmental impact and sustainability of ruminant farming systems are under increasing scrutiny due to factors such as climate change and land degradation. Extensive pasture-based farming systems can mitigate these challenges, as they are associated with range of ecosystem services, although they are characterized by low efficiency and are labor-intensive and time-consuming. This review investigates the potential of Precision Livestock Farming technologies (PLF) to enhance the health, welfare, and productivity of grazing ruminants while minimizing environmental impacts. Precision Livestock Farming tools, such as GPS tracking, accelerometers, and virtual fencing, enable real-time monitoring of animal behavior, health, and pasture management, offering smart solutions to challenges such as overgrazing and greenhouse gas emissions. These technologies also enhance the integration of sustainable agronomic practices, like rotational grazing and nitrogen-fixing crops, which can improve soil health and reduce emissions. Despite these benefits, the adoption of PLF technologies in extensive pasture-based systems remains limited due to economic, technical, and infrastructural barriers. Further research is required to optimize PLF applications for various ruminant species, improve data accuracy, and scale these technologies for broader implementation in sustainable livestock farming. Additionally, future efforts should prioritize the integration of animal and pasture management practices to fully harness the potential of PLF in mitigating climate impacts and improving the efficiency of livestock systems.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100789"},"PeriodicalIF":6.3,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181949","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":"Forecasting gaps in sugarcane fields containing weeds using low-resolution UAV imagery based on a machine-learning approach","authors":"Wipawadee Thamoonlest ,&nbsp;Jetsada Posom ,&nbsp;Kanda Saikaew ,&nbsp;Arthit Phuphaphud ,&nbsp;Adulwit Chinapas ,&nbsp;Lalita Panduangnat ,&nbsp;Khwantri Saengprachatanarug","doi":"10.1016/j.atech.2025.100780","DOIUrl":"10.1016/j.atech.2025.100780","url":null,"abstract":"<div><div>Effective gap assessment is crucial for guiding sugarcane farmers in decisions about replanting versus maintaining ratoons. This study explores the use of low-resolution multispectral aerial imagery to enhance cost-efficiency and field management practices. Reflectance images captured during the germination phase were employed to develop predictive models, assessing five machine learning algorithms for their effectiveness in detecting sugarcane in fields with unmanaged weed populations. The optimal buffer distance for predicting canopy size during the tillering phase was identified, and this model was applied to sugarcane areas during germination. Gap identification was achieved by intersecting buffered sugarcane areas with planted rows. The Linear Discriminant Analysis (LDA) model emerged as the most effective, utilizing reflectance bands from the red, green, blue, and red-edge spectra, and achieving an accuracy of 84%. Notably, the blue reflectance band proved particularly important for distinguishing between sugarcane and non-sugarcane classifications. The gap detection model achieved a mean absolute error of 6.19%. These findings provide valuable insights for farmers, sugar mills, service providers, and other stakeholders, enabling informed decision-making regarding ratoon management. This research supports the strategic allocation of machinery and labor, thereby enhancing operational efficiency in alignment with the planting season.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100780"},"PeriodicalIF":6.3,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181952","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":"Identification of soybean planting gaps using machine learning","authors":"Flávia Luize Pereira de Souza ,&nbsp;Maurício Acconcia Dias ,&nbsp;Tri Deri Setiyono ,&nbsp;Sérgio Campos ,&nbsp;Luciano Shozo Shiratsuchi ,&nbsp;Haiying Tao","doi":"10.1016/j.atech.2025.100779","DOIUrl":"10.1016/j.atech.2025.100779","url":null,"abstract":"<div><div>The identification of planting gaps is essential for optimizing crop management in precision agriculture. Traditional methods, such as manual scouting, are limited in scale and precision. This study evaluates the performance of three machine learning algorithms—Decision Trees, Support Vector Machines (SVM), and Multilayer Perceptron (MLP) Neural Networks—for classifying planting gaps in soybean fields using UAV imagery during the V4 growth stage. The Neural Network and SVM models demonstrated similar results, with the Neural Network achieving an AUC of 0.984, accuracy of 94.5 %, F1 score of 0.945, precision of 94.5 %, and recall of 94.5 %. The SVM model with a Polynomial kernel achieved an AUC of 0.989, accuracy of 95.5 %, F1 score of 0.955, precision of 95.5 %, and recall of 95.5 %. In contrast, the Decision Tree model performed lower, with an AUC of 0.805 and accuracy of 79 %. These results demonstrate the effectiveness of machine learning algorithms, particularly Neural Networks and SVM, in improving planting gap detection, contributing to more precise crop management decisions.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100779"},"PeriodicalIF":6.3,"publicationDate":"2025-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182134","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":"Enhanced deep learning model for apple detection, localization, and counting in complex orchards for robotic arm-based harvesting","authors":"Tantan Jin ,&nbsp;Xiongzhe Han ,&nbsp;Pingan Wang ,&nbsp;Zhao Zhang ,&nbsp;Jie Guo ,&nbsp;Fan Ding","doi":"10.1016/j.atech.2025.100784","DOIUrl":"10.1016/j.atech.2025.100784","url":null,"abstract":"<div><div>The growing demand for automation in the apple-harvesting industry remains challenging due to the complex and dynamic nature of orchard environments. This study presents an enhanced deep learning model designed to improve the accuracy and adaptability of recognition algorithms for robotic arm-based harvesting. Specifically, an optimized You Only Look Once (YOLO) v8n model was developed by integrating a dilation-wise residual–dilated re-parameterization block module, a generalized feature pyramid network, and the Scylla Intersection-over-Union loss function. The enhanced model was trained and evaluated on a comprehensive dataset, achieving precision, recall, F1 score, and mAP50 values of 81.43 %, 68.48 %, 74.40 %, and 81.68 %, respectively. These results indicate improvements of 1.06 %, 1.42 %, 1.28 %, and 1.61 % over the original YOLOv8n, while preserving comparable model parameters, computational efficiency, and detection speed. Furthermore, the enhanced model demonstrated superior overall performance compared to YOLOv5, YOLOv6, and RT-DETR. To validate its adaptability and robustness, the enhanced model was rigorously tested against the original YOLOv8n model diverse conditions, including varying growth stage, lighting environments, field of view, and levels of occlusion. In outdoor field experiments conducted under cloudy, low-light, and artificial lighting conditions, the model achieved localization errors of 2.43 mm (X-axis), 3.70 mm (Y-axis), and 1.28 mm (Z-axis), representing reductions of 19.27 %, 12.67 %, and 23.05 %, respectively. Furthermore, counting accuracy improved to 69.39 %, reflecting a 2.42 % increase over the original model. The results demonstrate the enhanced model's reliable performance and heightened precision for robotic arm-based apple harvesting in complex and challenging orchard environments. The study also provides a comprehensive analysis of the model's strengths, limitations, and avenues for future research. Ultimately, this work contributes to advancing agricultural automation, paving the way for smarter, more efficient, and sustainable farming practices.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100784"},"PeriodicalIF":6.3,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181953","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":"3D neural architecture search to optimize segmentation of plant parts","authors":"Farah Saeed ,&nbsp;Chenjiao Tan ,&nbsp;Tianming Liu ,&nbsp;Changying Li","doi":"10.1016/j.atech.2025.100776","DOIUrl":"10.1016/j.atech.2025.100776","url":null,"abstract":"<div><div>Accurately segmenting plant parts from imagery is vital for improving crop phenotypic traits. However, current 3D deep learning models for segmentation in point cloud data require specific network architectures that are usually manually designed, which is both tedious and suboptimal. To overcome this issue, a 3D neural architecture search (NAS) was performed in this study to optimize cotton plant part segmentation. The search space was designed using Point Voxel Convolution (PVConv) as the basic building block of the network. The NAS framework included a supernetwork with weight sharing and an evolutionary search to find optimal candidates, with three surrogate learners to predict mean IoU, latency, and memory footprint. The optimal candidate searched from the proposed method consisted of five PVConv layers with either 32 or 512 output channels, achieving mean IoU and accuracy of over 90 % and 96 %, respectively, and outperforming manually designed architectures. Additionally, the evolutionary search was updated to search for architectures satisfying memory and time constraints, with searched architectures achieving mean IoU and accuracy of &gt;84 % and 94 %, respectively. Furthermore, a differentiable architecture search (DARTS) utilizing PVConv operation was implemented for comparison, and our method demonstrated better segmentation performance with a margin of &gt;2 % and 1 % in mean IoU and accuracy, respectively. Overall, the proposed method can be applied to segment cotton plants with an accuracy over 94 %, while adjusting to available resource constraints.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100776"},"PeriodicalIF":6.3,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181948","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":"Economic and environmental benefits of digital agricultural technological solutions in livestock farming: A review","authors":"George Papadopoulos ,&nbsp;Maria-Zoi Papantonatou ,&nbsp;Havva Uyar ,&nbsp;Olga Kriezi ,&nbsp;Alexandros Mavrommatis ,&nbsp;Vasilis Psiroukis ,&nbsp;Aikaterini Kasimati ,&nbsp;Eleni Tsiplakou ,&nbsp;Spyros Fountas","doi":"10.1016/j.atech.2025.100783","DOIUrl":"10.1016/j.atech.2025.100783","url":null,"abstract":"<div><div>This review paper delved into the economic and environmental benefits of Digital Agricultural Technological Solutions (DATSs) in livestock farming systems. Synthesising data from 52 peer-reviewed papers it presents the outcomes of a systematic literature review on livestock farming DATSs, conducted with the use of the PRISMA methodology. The analysis highlighted the contribution of DATSs across three main livestock farming DATSs categories: Automated Milking Systems (AMS), Feed and Live Weight Measurement technologies, and Health Monitoring Systems. The results showed that AMS has the potential to boost cow productivity by up to 15 % while also reducing energy consumption by 35 %. Feed and Live Weight Measurement technologies contribute notably to sustainability and cost savings, with feed waste reductions of 75 % and feeding savings of 33 %. Health Monitoring Systems are especially effective in improving herd health and productivity through early detection of clinical issues, which directly enhances animal welfare and farm efficiency. Environmentally, AMS and health monitoring tools play a vital role in reducing greenhouse gas emissions, with AMS lowering global warming potential by up to 5.83 %. Overall, the findings of this review highlight the potentials of livestock DATSs towards economic viability and environmental sustainability, suggesting that the wider adoption could offer substantial benefits for the livestock farming sector. Up to now, DATSs have shown great potential in dairy cattle by improving milk yield, quality, and animal health, with advancements such as AMS increasing productivity and health monitoring systems enhancing early disease detection. In contrast, their application in sheep, goats, and pigs is still in its early stages, mainly limited to basic health monitoring and feeding technologies, despite the economic importance of these species, especially in the Mediterranean area, where most of the studies are conducted.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100783"},"PeriodicalIF":6.3,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182111","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-invasive prediction of sweet cherry soluble solids content using dielectric spectroscopy and down-sampling techniques","authors":"Kamil Sacilik ,&nbsp;Necati Cetin ,&nbsp;Burak Ozbey ,&nbsp;Fernando Auat Cheein","doi":"10.1016/j.atech.2025.100782","DOIUrl":"10.1016/j.atech.2025.100782","url":null,"abstract":"<div><div>The soluble solid content (SSC) in fruits significantly influences consumers' taste, aroma, and flavor preferences. It also plays a crucial role for farmers and wholesalers in determining the optimal harvest period for marketing. Dielectric spectroscopy, an innovative and non-invasive technique, has shown promise for various applications in the food and agriculture sectors. This study introduces an open-ended coaxial line probe measurement system to non-invasively determine the SSC of sweet cherries at different radio and microwave frequencies. Key parameters such as the dielectric constant (ε′), loss factor (ε′′), loss tangent (tan δ), and SSC of sweet cherries were measured across different harvest periods. The dielectric property frequency ranges were down-sampled from 300 MHz to 15 MHz. Using dielectric spectroscopy, we implemented predictive models: support vector regression (SVR) and multilayer perceptron (MLP), that demonstrated extremely low MAE and RMSE, with correlation coefficients (R) exceeding 0.97 for SVR and 0.96 for MLP. The down-sampled frequency ranges for dielectric properties yielded consistently high performance across all subsets, demonstrating comparable results. These findings suggest that a dielectric measurement system designed for SSC estimation using fewer frequencies could effectively reduce costs while maintaining accuracy.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100782"},"PeriodicalIF":6.3,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181510","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}