Smart agricultural technology最新文献

{"title":"Static and dynamic cutting forces in coriander crop harvesting: Engineering insights for harvester optimization","authors":"Aruna T N ,&nbsp;Pramod Kumar Sahoo ,&nbsp;Dilip Kumar Kushwaha ,&nbsp;Nrusingh Charan Pradhan ,&nbsp;Kishan Kumar ,&nbsp;Soumya Krishnan V ,&nbsp;Madhusudan B S ,&nbsp;Rohit Bhojyareddy Gaddamwar ,&nbsp;Abhishek Pandey ,&nbsp;Avesh Kumar Singh ,&nbsp;Zoltan Orban ,&nbsp;Ali Salem","doi":"10.1016/j.atech.2025.100772","DOIUrl":"10.1016/j.atech.2025.100772","url":null,"abstract":"<div><div>The study investigates the mechanical requirements for harvesting coriander (<em>Coriandrum sativum</em> L.) by analyzing static and dynamic cutting forces for three distinct varieties: SIMCO, GCr1, and GCr2. Through controlled laboratory experiments, the static cutting force was measured using a texture analyzer across variations in blade speed (2, 4, 6, 8, and 10 mm/s), stem number (1–5), cutting height (50, 75, 100, 125, and 150 mm), and moisture content (23 %, 30 %, and 37 %). The static cutting force for SIMCO was found to be the highest (151.6 N), followed by GCr1 (145.68 N) and GCr2 (140.48 N), primarily due to stem structure and diameter differences. The dynamic cutting force was also measured in the indoor soil bin using a reciprocating cutter bar by simulating the field conditions at varied forward speeds (0.3, 0.6, 0.9, and 1.2 m/s), cutter bar speeds (2, 8, 14, and 20 strokes/s), and cutting heights (50, 75, 100, 125, and 150 mm). For dynamic cutting, the SIMCO variety required an average maximum force of 33.14 N, which was 6.85 % and 7.06 % higher than GCr1 and GCr2 respectively. The dynamic cutting forces were influenced most significantly by cutter bar speed and forward speed, with optimal cutting achieved at 20 strokes/s cutter bar speed and 0.3 m/s forward speed. Response Surface Methodology (RSM) models with R² values above 0.99 effectively predicted both static and dynamic cutting forces, indicating strong model adequacy and providing detailed insights into the interactions between parameters. The analysis revealed that the number of stems and blade speed were the primary influencers on static cutting force, while the dynamic force was most affected by cutter bar speed and forward speed. This study highlights the importance of customized parameter settings to enhance harvester efficiency, reduce energy consumption, and minimize seed damage during harvest.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100772"},"PeriodicalIF":6.3,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183254","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":"Taxonomic hierarchical loss function for enhanced crop and weed phenotyping in multi-task semantic segmentation","authors":"Artzai Picon ,&nbsp;Daniel Mugica ,&nbsp;Itziar Eguskiza ,&nbsp;Arantza Bereciartua-Perez ,&nbsp;Javier Romero ,&nbsp;Carlos Javier Jimenez ,&nbsp;Christian Klukas ,&nbsp;Laura Gomez-Zamanillo ,&nbsp;Till Eggers ,&nbsp;Ramon Navarra-Mestre","doi":"10.1016/j.atech.2024.100761","DOIUrl":"10.1016/j.atech.2024.100761","url":null,"abstract":"<div><div>Herbicide research and development necessitate specific trials to monitor the effects of various herbicide formulations, quantities, and protocols on different plant species and growth stages. These trials are necessary to ensure the safety and efficacy of the developed products. Currently, these tests are conducted manually and assessed visually, making the process time-consuming and labor-intensive. Developing a computer model to characterize species, damage, and growth stages is challenging due to the fine-grained differences between species and damage, significant intra-class variability, and difficulties in manual annotations. Additionally, manually annotated datasets for semantic segmentation are often imperfect. The presence of non-target or unknown species, where only the genus or family is known, complicates the management and scalability of these datasets.</div><div>In this work, we propose a new hierarchical loss function, suitable for semantic segmentation tasks, capable to take advantage for the hierarchical taxonomy relationships between species, plant damages and other relationships and thus, reduce the need for annotated data. The proposed loss function support datasets with varying granularity and annotation heterogeneity, including for partial annotations at the pixel level. We validated this loss function using a multi-task semantic segmentation neural network to simultaneously detect plant species and quantify the damage of each species. The proposed hierarchical loss function improves model performance, increasing the F1-Score for species detection from 0.41 to 0.52, for damage detection from 0.23 to 0.28. This enhancement forces the model to learn richer hierarchical representations, enabling the support of heterogeneous and partially annotated scalable datasets, which are common in real-world AI applications.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100761"},"PeriodicalIF":6.3,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183304","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":"Evaluation of deep learning models for RGB image-based detection of potato virus y strain symptoms (O, NO, and NTN) in potato plants","authors":"Charanpreet Singh ,&nbsp;Gurjit S. Randhawa ,&nbsp;Aitazaz A. Farooque ,&nbsp;Yuvraj S. Gill ,&nbsp;Lokesh Kumar KM ,&nbsp;Mathuresh Singh ,&nbsp;Khalil Al-Mughrabi","doi":"10.1016/j.atech.2024.100755","DOIUrl":"10.1016/j.atech.2024.100755","url":null,"abstract":"<div><div>Potato virus Y (PVY) has been a long-standing problem for potato growers over the world, due to its ability to cause significant reductions in crop yields. The yield losses due to PVY may range from 10% to 80%, depending on the severity of the infection and the potato variety. The new necrotic strains of PVY cause mild symptoms in the foliage, making it challenging to detect infected plants. Consequently, identifying and disposing of infected plants (known as “roguing”) has become more difficult. There is a growing demand to create solutions that aid growers in identifying potato plants that have been infected with PVY. In past studies, deep learning-based convolutional neural networks (CNNs) have shown the ability to successfully make distinctions between various healthy plants, disease plants, and weeds. In this study, the use of these models for the detection of infected plants with different strains of PVY has been explored and extended. Different deep learning models, specifically EfficientNet, VGGNet-19, DenseNet-201 and ResNet-101 are trained on the imagery dataset of healthy and PVY-infected potato plants grown under greenhouse conditions. The evaluation metrics used were accuracy, precision, recall, and F1 Score. The trained models achieved classification accuracy scores of 85% while classifying the healthy and PVY-infected potato plants. The models were also able to accurately detect PVY-infected plants even when the symptoms were mild, which is essential for early detection and prevention of the spread of the virus. These models may assist roguers in the real-time identification of PVY-infected plants that may help in controlling the disease spread and improving the crop yield.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100755"},"PeriodicalIF":6.3,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181514","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":"Classifying the freshness of large yellow croaker (Larimichthys crocea) at 12- and 24-hour intervals using computer vision technique and convolutional neural network","authors":"Yao Zheng ,&nbsp;Quantong Zhang ,&nbsp;Xin Wang ,&nbsp;Quanyou Guo","doi":"10.1016/j.atech.2025.100767","DOIUrl":"10.1016/j.atech.2025.100767","url":null,"abstract":"<div><div>To develop a rapid and non-destructive method for assessing the freshness of large yellow croaker, a computer vision technique combined with a convolutional neural network (CNN) was utilized. Sixty fish were stored on ice, and images were captured using a smartphone at intervals of 0, 12, 24, 36, 48, 72, and 96 h. A modified ResNeXt architecture was applied to automatically extract features and establish a freshness classification model. The CNN model was able to identify imperceptible visual changes, and achieved classification accuracies of 84.0 % and 72.0 % for 24- and 12 h intervals, respectively. Furthermore, potential mechanisms for the model's performance were discussed, indicating that changes in skin, eyes, and other image features contribute to the freshness classification. In summary, this method is effective for real-time, non-destructive, low-cost, and environmentally friendly fish freshness evaluation, particularly during the early stages of storage.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100767"},"PeriodicalIF":6.3,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181127","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":"Advanced BLB disease assessment in paddy fields using multispectral UAV data and patch fragmentation metrics","authors":"Arif K Wijayanto ,&nbsp;Lilik B Prasetyo ,&nbsp;Sahid Agustian Hudjimartsu ,&nbsp;Gunardi Sigit ,&nbsp;Chiharu Hongo","doi":"10.1016/j.atech.2024.100766","DOIUrl":"10.1016/j.atech.2024.100766","url":null,"abstract":"<div><div>This study introduces an innovative method for assessing bacterial leaf blight (BLB) in paddy fields using multispectral UAV (Unmanned Aerial Vehicle) data and patch fragmentation analysis. Unlike traditional pixel-based approaches, which often lack spatial context, our method treats pixels as objects and evaluates their spatial relationships to determine BLB severity. Seven patch fragmentation metrics were derived from binarized vegetation indices to quantify BLB damage scores, carefully selected for their ability to describe the spatial arrangement and connectivity of potentially affected patches. This metric-driven approach captures the scale and intensity of BLB damage, facilitating precise assessment. The method demonstrated high accuracy, achieving an AUC of 0.938 with a 0.5-meter sampling window. This advancement enhances the precision of BLB damage assessment, particularly for applications such as crop insurance.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100766"},"PeriodicalIF":6.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182773","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":"Design and development of an IoT-based dendrometer system for real-time trunk diameter monitoring of Christmas trees","authors":"Thomas Rose,&nbsp;Nawab Ali,&nbsp;Younsuk Dong","doi":"10.1016/j.atech.2024.100765","DOIUrl":"10.1016/j.atech.2024.100765","url":null,"abstract":"<div><div>Real-time assessment of trunk growth is vital for understanding tree growth fluctuation, especially under irrigation application and other environmental factors. Accurate trunk diameter assessment is crucial for optimizing water use and tree health improvement, and its cost-effectiveness is needed for widespread adoption in agriculture. This study focused on the development of an accurate and low-cost IoT-based dendrometer system for real-time trunk diameter measurement of Christmas trees. The dendrometer sensor was calibrated (R² = 0.99) to ensure the accurate conversion of sensor voltage to trunk diameter fluctuations. This IoT-based dendrometer system consists of a platform that enables wireless data transmission, cloud-based storage and real-time analysis of the trunk diameter. Temperature fluctuation influenced the sensor readings with no impact, which validated the system's reliability in open field conditions. Christmas tree diameter monitoring showed significant trunk expansion and contraction under irrigation application and water stress, respectively, which signifies the system ability to monitor the real-time trunk growth responses. Cost analysis makes this technology economical and reliable for widespread application in precision agriculture. Therefore, this low-cost IoT-based dendrometer system is reliable, accurate, and economically viable for improving irrigation management, tree health monitoring, and supporting farmers through data-driven agricultural practices.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100765"},"PeriodicalIF":6.3,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181123","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":"Configuration of a multisensor platform for advanced plant phenotyping and disease detection: Case study on Cercospora leaf spot in sugar beet","authors":"Facundo R. Ispizua Yamati ,&nbsp;Jonas Bömer ,&nbsp;Niklas Noack ,&nbsp;Thomas Linkugel ,&nbsp;Stefan Paulus ,&nbsp;Anne-Katrin Mahlein","doi":"10.1016/j.atech.2024.100740","DOIUrl":"10.1016/j.atech.2024.100740","url":null,"abstract":"<div><div>Plant phenotyping, which involves measuring and analysing plant traits, has seen significant advances in recent years by integrating autonomous platforms and sophisticated sensor systems. In contrast to traditional methods, modern unmanned ground vehicles (UGVs) provide robust and accurate phenotyping capabilities by enabling close, detailed and continuous monitoring of crops under different environmental conditions. This study presents the configuration and validation of a multi-sensor platform (MSP) integrated with a UGV to improve plant phenotyping through advanced data fusion and co-registration techniques. The platform incorporates red, green, and blue channel (RGB), hyperspectral from visible light (VIS) and near-infrared light (NIR) spectrum, thermal sensors, and a three-dimensional (3D) light detection and ranging (LiDAR), all subjected to extensive calibration to ensure precise temporal and spatial alignment. Intrinsic calibration was applied, including correcting the spectral signatures of VIS and NIR. Additionally, timestamps were synchronised using the VIS sensor as the primary reference due to its central position and higher data acquisition frequency. Homography matrices were computed using checkerboard patterns for geometric alignment across sensors, and motion corrections accounted for UGV movement and ground sample distance. LiDAR point clouds were transformed into depth-maps (DMs) using radial basis function interpolation, enriching the spatial data for further analysis. The co-registered and synchronised MSP was tested for detecting Cercospora leaf spot (CLS) in sugar beet plants during a field experiment. Two models were implemented: (1) a soil and plant segmentation model based on the DeepLabV3+ architecture, achieving an F1-score of 0.85 and an accuracy of 0.95, and (2) a CLS severity scoring model using a custom convolutional neural network (CNN). The severity model, leveraging NIR and DM channels, achieved an F1-score of 0.7066, accuracy of 0.7104, and recall of 0.7167, with NIR wavelengths between 814<!--> <figure><img></figure> and 851<!--> <figure><img></figure> contributing significantly to performance. These results highlight the importance of accurate data fusion and synchronisation in multi-sensor systems for plant phenotyping. Moreover, the study demonstrates the potential of integrating multiple sensors on a UGV for precision agriculture, thereby enhancing MSP effectiveness in crop monitoring and disease detection.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100740"},"PeriodicalIF":6.3,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181128","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":"Reinforcement learning-based model predictive control for greenhouse climate control","authors":"Samuel Mallick ,&nbsp;Filippo Airaldi ,&nbsp;Azita Dabiri ,&nbsp;Congcong Sun ,&nbsp;Bart De Schutter","doi":"10.1016/j.atech.2024.100751","DOIUrl":"10.1016/j.atech.2024.100751","url":null,"abstract":"<div><div>Greenhouse climate control is concerned with maximizing performance in terms of crop yield and resource efficiency. One promising approach is model predictive control (MPC), which leverages a model of the system to optimize the control inputs, while enforcing physical constraints. However, prediction models for greenhouse systems are inherently inaccurate due to the complexity of the real system and the uncertainty in predicted weather profiles. For model-based control approaches such as MPC, this can degrade performance and lead to constraint violations. Existing approaches address uncertainty in the prediction model with robust or stochastic MPC methodology; however, these necessarily reduce crop yield due to conservatism and often bear higher computational loads. In contrast, learning-based control approaches, such as reinforcement learning (RL), can handle uncertainty naturally by leveraging data to improve performance. This work proposes an MPC-based RL control framework to optimize the climate control performance in the presence of prediction uncertainty. The approach employs a parametrized MPC scheme that learns directly from data, in an online fashion, the parametrization of the constraints, prediction model, and optimization cost that minimizes constraint violations and maximizes climate control performance. Simulations show that the approach can learn an MPC controller that significantly outperforms the current state-of-the-art in terms of constraint violations and efficient crop growth.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100751"},"PeriodicalIF":6.3,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181131","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":"Deep learning driven methodology for the prediction of mushroom moisture content using a novel LED-based portable hyperspectral imaging system","authors":"Kai Yang,&nbsp;Ming Zhao,&nbsp;Dimitrios Argyropoulos","doi":"10.1016/j.atech.2024.100747","DOIUrl":"10.1016/j.atech.2024.100747","url":null,"abstract":"<div><div>This study proposes a deep-learning driven methodology for the analysis of mushroom moisture content (MC) datasets acquired using a novel portable hyperspectral imaging (HSI) system. One-dimensional convolutional neural network (1D-CNN) was developed and validated to process the raw HSI data of white button mushrooms <em>(Agaricus bisporus)</em> for MC prediction. For comparison purposes, state-of-the-art machine learning algorithms, i.e., support vector machine regression (SVMR) and partial least squares regression (PLSR) were also investigated for the model development based on five spectra pre-processed methods using two different lighting systems i.e., enhanced light-emitting diode (LED) and tungsten halogen (TH). Overall, the predictive models based on the HSI data acquired using the LED lights (R_p² of 0.977, RMSEP of 4.27 %, and RPD_p of 6.89) exhibited better performances on the prediction of mushroom MC than those models developed using the TH-HSI data (R_p² of 0.868, RMSEP of 10.69 %, and RPD_p of 2.75). Specifically, the 1D-CNN model based on the raw LED-HSI data (R_p² of 0.972, RMSEP of 4.70 % and RPD_p of 6.29) and the SVMR model based on multiplicative scatter correction (MSC) pretreated LED-HSI data (R_p² of 0.977, RMSEP of 4.27 %, and RPD_p of 6.89) achieved exceptional predictive accuracy for mushroom MC. This finding highlights the effectiveness of the 1D-CNN model in the analysis of HSI data, which performed similarly to the SVMR model without requiring complex data preprocessing steps. In addition, the feasibility of employing a novel LED illumination system in conjunction with a portable HSI camera for the precise MC monitoring of button mushrooms was demonstrated in the present work.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100747"},"PeriodicalIF":6.3,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182767","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":"Whispers in the air: Designing acoustic classifiers to detect fruit flies from afar","authors":"Alia Khalid ,&nbsp;Muhammad Latif Anjum ,&nbsp;Salman Naveed ,&nbsp;Wajahat Hussain","doi":"10.1016/j.atech.2024.100738","DOIUrl":"10.1016/j.atech.2024.100738","url":null,"abstract":"<div><div>Detecting weak wingbeats of a flying bug is a challenging problem in uncontrolled outdoor settings. In this work, we show that proper treatment of environmental noise is a key factor in robust acoustic classifier design and propose a novel environmental noise treatment method. Our proposed method generalizes over different classifiers and features. Our algorithm provides robust detection and classification of multiple bugs, over longest ranges reported, using simple microphones. In order to benchmark research in this area, we release a novel dataset containing acoustic data of four bugs (Guava fly, Melon fly, Blue bottle fly, and mosquitoes). We additionally investigate the feasibility of deploying our acoustic classifier on a noisy mobile platform, i.e., a drone. To this end, we expose the limitations of signal processing techniques to deal with loud drone noise. We demonstrate how soundproofing can be used to design acoustic sensing for drones.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100738"},"PeriodicalIF":6.3,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182768","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}