Опубликована: Янв. 1, 2024
Язык: Английский
Remote Sensing, Год журнала: 2025, Номер 17(2), С. 279 - 279
Опубликована: Янв. 15, 2025
The potential of precision agriculture (PA) in forage and grassland management should be more extensively exploited to meet the increasing global food demand on a sustainable basis. Monitoring biomass yield quality traits directly impacts fertilization irrigation practises frequency utilization (cuts) grasslands. Therefore, main goal review is examine techniques for using PA applications monitor productivity To achieve this, authors discuss several monitoring technologies plant stand characteristics (including quality) that make it possible adopt digital farming forages management. provides an overview about mass flow impact sensors, moisture remote sensing-based approaches, near-infrared (NIR) spectroscopy, mapping field heterogeneity promotes decision support systems (DSSs) this field. At small scale, advanced sensors such as optical, thermal, radar mountable drones; LiDAR (Light Detection Ranging); hyperspectral imaging can used assessing soil characteristics. larger we coupling sensing with weather data (synergistic modelling), Sentinel-2 radiative transfer modelling (RTM), Sentinel-1 backscatter, Catboost–machine learning methods terms harvesting site-specific decisions. It known delineation sward difficult mixed grasslands due spectral similarity among species. Thanks Diversity-Interactions models, jointly various species interactions under allowed. Further, understanding complex might feasible by integrating un-mixing super-pixel segmentation technique, multi-level fusion procedure, combined NIR spectroscopy neural network models. This offers option enhancing implementing recommend future research direction inclusion costs economic returns fodder production.
Язык: Английский
Процитировано
2
Agriculture, Год журнала: 2024, Номер 14(11), С. 1977 - 1977
Опубликована: Ноя. 4, 2024
Neuromorphic computing has received more and attention recently since it can process information interact with the world like human brain. Agriculture is a complex system that includes many processes of planting, breeding, harvesting, processing, storage, logistics, consumption. Smart devices in association artificial intelligence (AI) robots Internet Things (IoT) systems have been used also need to be improved accommodate growth computing. great potential promote development smart agriculture. The aim this paper describe current principles neuromorphic technology, explore examples applications agriculture, consider future route synapses, neurons, neural networks (ANNs). A expected improve agricultural production efficiency ensure food quality safety for nutrition health agriculture future.
Язык: Английский
Процитировано
11
Results in Engineering, Год журнала: 2024, Номер 24, С. 103392 - 103392
Опубликована: Ноя. 10, 2024
Язык: Английский
Процитировано
11
Agronomy, Год журнала: 2024, Номер 14(9), С. 1975 - 1975
Опубликована: Сен. 1, 2024
Due to current global population growth, resource shortages, and climate change, traditional agricultural models face major challenges. Precision agriculture (PA), as a way realize the accurate management decision support of production processes using modern information technology, is becoming an effective method solving these In particular, combination remote sensing technology machine learning algorithms brings new possibilities for PA. However, there are relatively few comprehensive systematic reviews on integrated application two technologies. For this reason, study conducts literature search Web Science, Scopus, Google Scholar, PubMed databases analyzes in PA over last 10 years. The found that: (1) because their varied characteristics, different types data exhibit significant differences meeting needs PA, which hyperspectral most widely used method, accounting more than 30% results. UAV offers greatest potential, about 24% data, showing upward trend. (2) Machine displays obvious advantages promoting development vector algorithm 20%, followed by random forest algorithm, 18% methods used. addition, also discusses main challenges faced currently, such difficult problems regarding acquisition processing high-quality model interpretation, generalization ability, considers future trends, intelligence automation, strengthening international cooperation sharing, sustainable transformation achievements. summary, can provide ideas references combined with promote
Язык: Английский
Процитировано
9
Computers and Electronics in Agriculture, Год журнала: 2025, Номер 230, С. 109933 - 109933
Опубликована: Янв. 10, 2025
Язык: Английский
Процитировано
1
Discover Sustainability, Год журнала: 2025, Номер 6(1)
Опубликована: Фев. 17, 2025
Язык: Английский
Процитировано
1
Sensors, Год журнала: 2025, Номер 25(2), С. 453 - 453
Опубликована: Янв. 14, 2025
Efficient management of soil nutrients is essential for optimizing crop production, ensuring sustainable agricultural practices, and addressing the challenges posed by population growth environmental degradation. Smart agriculture, using advanced technologies, plays an important role in achieving these goals enabling real-time monitoring precision nutrients. In open-field cultivation, spatial variability properties demands site-specific nutrient integration with variable-rate technology (VRT) to optimize fertilizer application, reduce losses, enhance yields. Hydroponic solution on other hand, requires precise control solutions maintain optimal conditions plant growth, efficient use water fertilizers. This review aims explore recent trends sensing technologies facilitated hydroponic highlighting advancements that promote efficiency sustainability. Key include electrochemical optical sensors, Internet Things (IoT)-enabled monitoring, machine learning (ML) artificial intelligence (AI) predictive modeling. Blockchain also emerging as a tool transparency traceability management, promoting compliance standards practices. support targeted application accounting variability, minimizing risks such runoff eutrophication. ensures balance, health productivity. By advancing smart agriculture can achieve improved resource efficiency, protection, fostering resilient food system.
Язык: Английский
Процитировано
0
Journal of Cleaner Production, Год журнала: 2025, Номер unknown, С. 144980 - 144980
Опубликована: Фев. 1, 2025
Язык: Английский
Процитировано
0
Computers and Electronics in Agriculture, Год журнала: 2025, Номер 231, С. 110001 - 110001
Опубликована: Фев. 13, 2025
Язык: Английский
Процитировано
0
AgriEngineering, Год журнала: 2025, Номер 7(2), С. 45 - 45
Опубликована: Фев. 17, 2025
In animal facilities, monitoring and controlling the thermal environment are essential in ensuring productivity sustainability. However, many production units face challenges implementing maintaining effective control systems. Given need for Smart Livestock Farming systems, this study aimed to develop validate an easy-to-use, low-cost embedded system (ESLC) real-time of dry-bulb air temperature (Tdb, °C) relative humidity (RH, %) facilities. The ESLC consists data collection/transmission modules a server Internet Things (IoT) storage. standard recording sensors (SRS) were installed prototype Over 21 days, their performance was evaluated based on Data Transmission Success Rate (DTSR, Interval (DTI, minutes). Additionally, agreement between SRS assessed using daily mean root square error (RMSE) (RE) across different Tdb RH ranges. successfully collected transmitted cloud server, achieving average DTSR 94.04% predominant DTI one minute. Regarding measurement agreement, distinct RMSE values obtained (0.26–2.46 (4.37–16.20%). Furthermore, four sensor exhibited RE below 3.00% all ranges, while showed progressively increasing as levels rose. Consequently, calibration curves established each module, high correlation raw corrected (determination coefficient above 0.98). It concluded that is promising solution enabling continuous reliable collection transmission.
Язык: Английский
Процитировано
0