Search Results (90)

The inclusion of the Internet of Things (IoT) in indoor agricultural systems has become a fundamental tool for improving cultivation systems by providing key information for decision-making in pursuit of better performance. This article presents the design and implementation of an IoT-based agricultural system installed in a plant growth chamber for hydroponic cultivation under controlled conditions. The growth chamber is equipped with sensors for air temperature, relative humidity (RH), carbon dioxide (CO₂) and photosynthetically active photon flux, as well as control mechanisms such as humidifiers, full-spectrum Light Emitting Diode (LED) lamps, mini split air conditioner, pumps, a Wi-Fi surveillance camera, remote monitoring via a web application and three Nutrient Film Technique (NFT) hydroponic systems with a capacity of ten plants each. An ATmega2560 microcontroller manages the smart system using the MODBUS RS-485 communication protocol. To validate the proper functionality of the proposed system, a case study was conducted using lettuce crops, in which the impact of different nutrient solution concentrations (50%, 75% and 100%) on the phenotypic development and nutritional content of the plants was evaluated. The results obtained from the cultivation experiment, analyzed through analysis of variance (ANOVA), show that the treatment with 75% nutrient concentration provides an appropriate balance between resource use and nutritional quality, without affecting the chlorophyll content. This system represents a scalable and replicable alternative for protected agriculture. Full article

In precision cotton seeding, the toothed-disk precision seeder often experiences issues with missed seeding and multiple seeding. To promptly detect and address these abnormal seeding conditions, this study develops a modular photoelectric sensing monitoring system. Initially, the monitoring time window is divided using the capacitance sensing signal between two seed drop ports. Concurrently, a photoelectric monitoring circuit is designed to convert the time when seeds block the sensor into a level signal. Subsequently, threshold segmentation is performed on the time when seeds block the photoelectric path under different seeding states. The proposed spatiotemporal joint counting algorithm identifies, in real time, the threshold type of the photoelectric sensor’s output signal within the current monitoring time window, enabling the differentiation of seeding states and the recording of data. Additionally, an STM32 micro-controller serves as the core of the signal acquisition circuit, sending collected data to the PC terminal via serial port communication. The graphical display interface, designed with LVGL (Light and Versatile Graphics Library), updates the seeding monitoring information in real time. Compared to photoelectric monitoring algorithms that detect seed pickup at the seed metering disc, the monitoring node in this study is positioned posteriorly within the seed guide chamber. Consequently, the differentiation between single seeding and multiple seeding is achieved with greater accuracy by the spatiotemporal joint counting algorithm, thereby enhancing the monitoring precision of the system. Field test results indicate that the system’s average accuracy for single-seeding monitoring is 97.30%, for missed-seeding monitoring is 96.48%, and for multiple-seeding monitoring is 96.47%. The average probability of system misjudgment is 3.25%. These outcomes suggest that the proposed modular photoelectric sensing monitoring system can meet the monitoring requirements of precision cotton seeding at various seeding speeds. Full article

Winter wheat yield prediction is critical for optimizing field management plans and guiding agricultural production. To address the limitations of conventional manual yield estimation methods, including low efficiency and poor interpretability, this study innovatively proposes an intelligent yield estimation method based on a convolutional neural network (CNN). A comprehensive two-factor (fertilization × irrigation) controlled field experiment was designed to thoroughly validate the applicability and effectiveness of this method. The experimental design comprised two irrigation treatments, sufficient irrigation (C) at 750 m³ ha⁻¹ and deficit irrigation (M) at 450 m³ ha⁻¹, along with five fertilization treatments (at a rate of 180 kg N ha⁻¹): (1) organic fertilizer alone, (2) organic–inorganic fertilizer blend at a 7:3 ratio, (3) organic–inorganic fertilizer blend at a 3:7 ratio, (4) inorganic fertilizer alone, and (5) no fertilizer control. The experimental protocol employed a DJI M300 RTK unmanned aerial vehicle (UAV) equipped with a multispectral sensor to systematically acquire high-resolution growth imagery of winter wheat across critical phenological stages, from heading to maturity. The acquired multispectral imagery was meticulously annotated using the Labelme professional annotation tool to construct a comprehensive experimental dataset comprising over 2000 labeled images. These annotated data were subsequently employed to train an enhanced CNN model based on ResNet50 architecture, which achieved automated generation of panicle density maps and precise panicle counting, thereby realizing yield prediction. Field experimental results demonstrated significant yield variations among fertilization treatments under sufficient irrigation, with the 3:7 organic–inorganic blend achieving the highest actual yield (9363.38 ± 468.17 kg ha⁻¹) significantly outperforming other treatments (p < 0.05), confirming the synergistic effects of optimized nitrogen and water management. The enhanced CNN model exhibited superior performance, with an average accuracy of 89.0–92.1%, representing a 3.0% improvement over YOLOv8. Notably, model accuracy showed significant correlation with yield levels (p < 0.05), suggesting more distinct panicle morphological features in high-yield plots that facilitated model identification. The CNN’s yield predictions demonstrated strong agreement with the measured values, maintaining mean relative errors below 10%. Particularly outstanding performance was observed for the organic fertilizer with full irrigation (5.5% error) and the 7:3 organic-inorganic blend with sufficient irrigation (8.0% error), indicating that the CNN network is more suitable for these management regimes. These findings provide a robust technical foundation for precision farming applications in winter wheat production. Future research will focus on integrating this technology into smart agricultural management systems to enable real-time, data-driven decision making at the farm scale. Full article

This paper presents a load spectrum acceleration editing method based on wavelet transform. The principle of the method is to decompose the target signal using wavelet transform to obtain high-frequency wavelet components, which are classified and combined based on their frequency components for accelerated editing. During the damage segment identification stage, a threshold selection method based on the pseudo-damage gradient of the segment identification results is proposed. An envelope-based damage identification method is used to extract high-damage segments from the original signal, which are then concatenated to form an accelerated signal. Using the stress signal on the chassis of a combine harvester as a case study, the effectiveness of various accelerated editing methods is compared, with a discussion on the selection of wavelet function parameters. The results indicate that, compared to the time-domain damage retention method and the traditional wavelet transform accelerated editing method, the proposed improvement enhances the acceleration effect of the time-domain signal by 7.76% and 15.92%, respectively. The accelerated signal is consistent with the original signal in terms of statistical parameters and power spectral density. Additionally, we also found that an appropriate selection of the wavelet function’s vanishing moment can further reduce the time-domain signal length of the accelerated result by 4.8%. This study can provide beneficial experiential references for load spectrum development in the accelerated durability testing of agricultural machinery. Full article

Artificial-intelligence-based robotics has recently garnered considerable attention, with the prediction of sample attributes becoming essential for artificial-intelligence-based environmental data analysis and decision-making processes in smart equipment and IoT devices. Based on a masked autoencoder (MAE), this study introduces the FedMAE regressor, a federated learning regression framework designed to precisely predict critical nutrients such as nitrogen, phosphorus, and potassium in agricultural and environmental monitoring devices while ensuring data privacy. The proposed adaptive regressor integrates deep learning methodologies within a federated learning architecture. Layer normalization is employed to enhance the model’s stability in distributed environments, and its structure is optimized with residual connections and GELU activation functions. An adaptive normalization method, a multi-layer feature transformation system, and a balanced data allocation technique are introduced to mitigate data distribution biases in edge devices. Furthermore, the AdaBelief optimizer and a dynamic learning rate scheduling approach are implemented to improve the model’s resilience. Experimental results show that the proposed method outperforms baseline and state-of-the-art models in terms of nitrogen prediction and demonstrates notable adaptability in phosphorus and potassium prediction tasks. This research paves the way for the application of federated-learning-based approaches in various ecological and industrial contexts, providing a robust solution for time-series prediction challenges in diverse domains. Full article

Soil organic matter (SOM) content is a key indicator for assessing soil health, carbon cycling, and soil degradation. Traditional SOM detection methods are complex and time-consuming and do not meet the modern agricultural demand for rapid, non-destructive analysis. While significant progress has been made in spectral inversion for SOM prediction, its accuracy still lags behind traditional chemical methods. This study proposes a novel approach to predict SOM content by integrating spectral, texture, and color features using a three-branch convolutional neural network (3B-CNN). Spectral reflectance data (400–1000 nm) were collected using a portable hyperspectral imaging device. The top 15 spectral bands with the highest correlation were selected from 260 spectral bands using the Correlation Coefficient Method (CCM), Boruta algorithm, and Successive Projections Algorithm (SPA). Compared to other methods, CCM demonstrated superior dimensionality reduction performance, retaining bands highly correlated with SOM, which laid a solid foundation for multi-source data fusion. Additionally, six soil texture features were extracted from soil images taken with a smartphone using the gray-level co-occurrence matrix (GLCM), and twelve color features were obtained through the color histogram. These multi-source features were fused via trilinear pooling. The results showed that the 3B-CNN model, integrating multi-source data, performed exceptionally well in SOM prediction, with an R² of 0.87 and an RMSE of 1.68, a 23% improvement in R² compared to the 1D-CNN model using only spectral data. Incorporating multi-source data into traditional machine learning models (SVM, RF, and PLS) also improved prediction accuracy, with R² improvements ranging from 4% to 11%. This study demonstrates the potential of multi-source data fusion in accurately predicting SOM content, enabling rapid assessment at the field scale and providing a scientific basis for precision fertilization and agricultural management. Full article

This study presents a networked cyber-physical architecture that integrates a Reinforcement Learning-based Digital Twin (DT) to enable zero-delay interaction between physical and digital components in smart agriculture. The proposed system allows real-time remote control of a robotic arm inside a hydroponic greenhouse, using a sensor-equipped Wearable Glove (SWG) for hand motion capture. The DT operates in three coordinated modes: Real2Digital, Digital2Real, and Digital2Digital, supporting bidirectional synchronization and predictive simulation. A core innovation lies in the use of a Reinforcement Learning model to anticipate hand motions, thereby compensating for network latency and enhancing the responsiveness of the virtual–physical interaction. The architecture was experimentally validated through a detailed communication delay analysis, covering sensing, data processing, network transmission, and 3D rendering. While results confirm the system’s effectiveness under typical conditions, performance may vary under unstable network scenarios. This work represents a promising step toward real-time adaptive DTs in complex smart greenhouse environments. Full article

With the acceleration of agricultural intelligent transformation, deep reinforcement learning (DRL), leveraging its adaptive perception and decision-making capabilities in complex environments, has emerged as a pivotal technology in advancing the intelligent upgrade of agricultural machinery and equipment. For example, in UAV path optimization, DRL can help UAVs plan more efficient flight paths to cover more areas in less time. To enhance the systematicity and credibility of this review, this paper systematically examines the application status, key issues, and development trends of DRL in agricultural scenarios, based on the research literature from mainstream Chinese and English databases spanning from 2018 to 2024. From the perspective of algorithm–hardware synergy, the article provides an in-depth analysis of DRL’s specific applications in agricultural ground platform navigation, path planning for intelligent agricultural end-effectors, and autonomous operations of low-altitude unmanned aerial vehicles. It highlights the technical advantages of DRL by integrating typical experimental outcomes, such as improved path-tracking accuracy and optimized spraying coverage. Meanwhile, this paper identifies three major challenges facing DRL in agricultural contexts: the difficulty of dynamic path planning in unstructured environments, constraints imposed by edge computing resources on algorithmic real-time performance, and risks to policy reliability and safety under human–machine collaboration conditions. Looking forward, the DRL-driven smart transformation of agricultural machinery will focus on three key aspects: (1) The first aspect is developing a hybrid decision-making architecture based on model predictive control (MPC). This aims to enhance the strategic stability and decision-making interpretability of agricultural machinery (like unmanned tractors, harvesters, and drones) in complex and dynamic field environments. This is essential for ensuring the safe and reliable autonomous operation of machinery. (2) The second aspect is designing lightweight models that support edge-cloud collaborative deployment. This can meet the requirements of low-latency responses and low-power operation in edge computing scenarios during field operations, providing computational power for the real-time intelligent decision-making of machinery. (3) The third aspect is integrating meta-learning with self-supervised mechanisms. This helps improve the algorithm’s fast generalization ability across different crop types, climates, and geographical regions, ensuring the smart agricultural machinery system has broad adaptability and robustness and accelerating its application in various agricultural settings. This paper proposes research directions from three key dimensions-“algorithm capability enhancement, deployment architecture optimization, and generalization ability improvement”-offering theoretical references and practical pathways for the continuous evolution of intelligent agricultural equipment. Full article

In the context of the booming development of precision agriculture and plant phenotyping, plant 3D reconstruction technology has become a research hotspot, with widespread applications in plant growth monitoring, pest and disease detection, and smart agricultural equipment. Given the complex geometric and textural characteristics of plants, traditional 2D image analysis methods are difficult to meet the modeling requirements, highlighting the growing importance of 3D reconstruction technology. This paper reviews active vision techniques (such as structured light, time-of-flight, and laser scanning methods), passive vision techniques (such as stereo vision and structure from motion), and deep learning-based 3D reconstruction methods (such as NeRF, CNN, and 3DGS). These technologies enhance crop analysis accuracy from multiple perspectives, provide strong support for agricultural production, and significantly promote the development of the field of plant research. Full article

Recently, Internet of Drones (IoD) applications have grown in various fields, including the military, healthcare, smart agriculture, and traffic monitoring. Drones are equipped with computation resources, communication units, and embedded systems that allow them to sense, collect, and deliver data in real-time through public communication channels. However, this fact introduces the risk of attack on data transmitted over unsecured public channels. Addressing several security threats is crucial to ensuring the secure operation of IoD networks. Robust authentication protocols play a vital role in establishing secure processes in the IoD environment. However, designing efficient and lightweight authentication solutions is a complex task due to the unique characteristics of the IoD and the limitations of drones in terms of their communication and computational capabilities. There is a need to review the role of authentication processes in controlling security threats in the IoD due to the increasing complexity and frequency of security breaches. This review will present the primary issues and future path directions for authentication schemes in the IoD and provide a framework for relevant existing schemes to facilitate future research into the IoD. Consequently, in this paper, we review the literature to highlight the research conducted in this area of the IoD. This study reviews several existing methods for authenticating entities in the IoD environment. Moreover, this study discusses security requirements and highlights several challenges encountered with the authentication schemes used in the IoD. The findings of this paper suggest future directions for research to consider in order for this domain to continue to evolve. Full article

The integration of new technologies in Industry 4.0 has modernised agriculture, fostering the concept of smart agriculture (Agriculture 4.0). Higher education institutions are incorporating digital technologies into agricultural curricula, equipping students in agriculture, agronomy, and engineering with essential skills. The implementation of targeted STEM activities has the potential to enhance the teaching of Agriculture 4.0 through the utilisation of practical applications that stimulate student interest, thereby facilitating more accessible and effective teaching. In this context, this study presents a system comprising retrofitted real-scale components that facilitate the understanding of digital technologies and automations in agriculture. The specific system utilises a typical centrifugal electric pump and a water tank and adds logic to it, so that its flow follows various user-defined setpoints, given and monitored via a smartphone application, despite the in-purpose disturbances invoked via intermediating valves. This setup aims for students to gain familiarity with concepts such as closed-loop systems and PID controllers. Going further, fertile ground is provided for experimentation on the efficiency of the PID controller via testing different algorithmic variants incorporating non-linear methods as well. Feedback collected from the participating students via a corresponding survey highlights the importance of integrating similar hands-on interdisciplinary activities into university curricula to foster engineering education. Full article

Air pollutants, such as particulate matter (PM) and ammonia (NH₃), generated by intensive animal farming pose considerable threats to human health, animal welfare, and ecological balance. Conventional materials are often ineffective at simultaneously removing multiple pollutants, maintaining a low pressure drop, and ensuring durability in heavily polluted environments. Inspired by the dust-retention properties of Pinus sylvestris var. mongolica (PS) needles, this study developed a biomimetic grooved ribbon fiber using electrospinning technology. These fibers were further assembled into a three-dimensional bioinspired aerogel structure through freeze-forming technology to achieve efficient dust capture. Additionally, the introduction of UiO-66-NH₂ nanoparticles significantly enhanced the properties of the aerogels for NH₃ adsorption. Among the various prepared aerogels (PG, UPG-5, UPG-10, UPG-15, and UPG-20), UPG-10 demonstrated the best performance, achieving a filtration efficiency of 99.24% with a pressure drop of 95 Pa. Notably, it exhibited a remarkable dust-holding capacity of 147 g/m², and its NH₃ adsorption capacity reached 99.89 cm³/g, surpassing PG aerogel by 31.46 cm³/g. Additionally, UPG-10 exhibited outstanding elasticity, maintaining over 80% of its original shape after 30 compression cycles. This biomimetic aerogel presents a promising solution for air purification, contributing to improved agricultural efficiency and environmental sustainability. Full article

Robotic tomato picking is a crucial step toward mechanized and precision farming. Effective tomato recognition and localization algorithms for these robots require high accuracy and real-time performance in complex field environments. This study modifies the SSD model to develop a fast and high-precision tomato detection method. The classical SSD model is optimized by discarding certain feature maps for larger objects and incorporating a self-attention mechanism. Experiments utilized images from an organic tomato farm. The model was trained and evaluated based on detection accuracy, recall rate, time consumption, and model size. Results indicate that the modified SSD model has a 95% detection accuracy and 96.1% recall rate, outperforming the classical and self-attention SSD models in accuracy, time consumption, and model size. Field experiments also demonstrate its robustness under different illumination conditions. In conclusion, this study promotes the development of tomato-picking robots by presenting an optimized detection method that effectively balances accuracy and efficiency. This method improves detection accuracy remarkably. It also reduces complexity, making it very suitable for real-world use. It plays a crucial role in facilitating the adoption of robotic harvesting systems in modern agriculture. Technologically, it remarkably boosts the picking efficiency, lessens the reliance on human labor, and cuts down fruit losses through precise picking. As a result, it effectively enhances resource utilization efficiency, providing a practical solution for the development of sustainable agriculture. Full article

Achieving global sustainable agriculture requires farmers worldwide to adopt smart agricultural technologies, such as autonomous ground robots. However, most ground robots are either task- or crop-specific and expensive for small-scale farmers and smallholders. Therefore, there is a need for cost-effective robotic platforms that are modular by design and can be easily adapted to varying tasks and crops. This paper describes the hardware design of a unique, low-cost multiaxial modular agricultural robot (ModagRobot), and its field evaluation for soybean phenotyping. The ModagRobot’s chassis was designed without any welded components, making it easy to adjust trackwidth, height, ground clearance, and length. For this experiment, the ModagRobot was equipped with an RGB-Depth (RGB-D) sensor and adapted to safely navigate over soybean rows to collect RGB-D images for estimating soybean phenotypic traits. RGB images were processed using the Excess Green Index to estimate the percent canopy ground coverage area. 3D point clouds generated from RGB-D images were used to estimate canopy height (CH) and the 3D Profile Index of sample plots using linear regression. Aboveground biomass (AGB) was estimated using extracted phenotypic traits. Results showed an R², RMSE, and RRMSE of 0.786, 0.0181 m, and 2.47%, respectively, between estimated CH and measured CH. AGB estimated using all extracted traits showed an R², RMSE, and RRMSE of 0.59, 0.0742 kg/m², and 8.05%, respectively, compared to the measured AGB. The results demonstrate the effectiveness of the ModagRobot for in-row crop phenotyping. Full article

This work proposes a vision system mounted on the head of an omnidirectional robot to track pineapples and maintain them at the center of its field of view. The robot head is equipped with a pan–tilt unit that facilitates dynamic adjustments. The system architecture, implemented in Robot Operating System 2 (ROS2), performs the following tasks: it captures images from a webcam embedded in the robot head, segments the object of interest based on color, and computes its centroid. If the centroid deviates from the center of the image plane, a proportional–integral–derivative (PID) controller adjusts the pan–tilt unit to reposition the object at the center, enabling continuous tracking. A multivariate Gaussian function is employed to segment objects with complex color patterns, such as the body of a pineapple. The parameters of both the PID controller and the multivariate Gaussian filter are optimized using a genetic algorithm. The PID controller receives as input the (x, y) positions of the pan–tilt unit, obtained via an embedded board and MicroROS, and generates control signals for the servomotors that drive the pan–tilt mechanism. The experimental results demonstrate that the robot successfully tracks a moving pineapple. Additionally, the color segmentation filter can be further optimized to detect other textured fruits, such as soursop and melon. This research contributes to the advancement of smart agriculture, particularly for fruit crops with rough textures and complex color patterns. Full article