Automatic Disease Detection from Strawberry Leaf Based on Improved YOLOv8.

Strawberries are susceptible to various diseases during their growth, and leaves may show signs of diseases as a response. Given that these diseases generate yield loss and compromise the quality of strawberries, timely detection is imperative. To automatically identify diseases in strawberry leaves, a KTD-YOLOv8 model is introduced to enhance both accuracy and speed. The KernelWarehouse convolution is employed to replace the traditional component in the backbone of the YOLOv8 to reduce the computational complexity. In addition, the Triplet Attention mechanism is added to fully extract and fuse multi-scale features. Furthermore, a parameter-sharing diverse branch block (DBB) sharing head is constructed to improve the model's target processing ability at different spatial scales and increase its accuracy without adding too much calculation. The experimental results show that, compared with the original YOLOv8, the proposed KTD-YOLOv8 increases the average accuracy by 2.8% and reduces the floating-point calculation by 38.5%. It provides a new option to guide the intelligent plant monitoring system and precision pesticide spraying system during the growth of strawberry plants.

Wearable knee joint fatigue estimating system based on curvature and pressure sensing.

BACKGROUND: The injury of the knee joint is found to be directly related to the fatigue caused by excessive exercise. Many previous studies used wearable devices to measure the angle of knee joint during activities, but did not pay enough attention to the load of knee joint related to the fatigue degree of it. OBJECTIVE: A wearable embedded system was designed to sense the motion state and load of knee joint and uses the sensoring data to estimate and predict the fatigue degree of knee joint during exercise in real time, so as to prevent it from being injured. METHODS: An economical wearable system is designed to measure the parameters of the knee joint during exercises. Then the warning message and recommended healthy lasting time are able to be sent to users to avoid excessive exercise. 24 healthy volunteers aged 20-25 years were involved in the experiments. Two famous evaluation scales for knee joint from Department of Orthopedics (Lysholm score and IKDC score) were adopted to evaluate the protective effect. RESULTS: After 14 days of the first stage testing, all the participants with wearable devices reported healthy knee joint state to verify the effectiveness of the system. For the second stage, the testing group equipped with wearable warning devices did not receive obvious change in the two scales. However, Lysholm score of control group dropped by at least 7.4 and IKDC score dropped by at least 11.1 which were significantly reduced. CONCLUSION: Only using human perception to prevent knee joint fatigue had a risk of failure while the designed wearable system could protect the knee successfully from injuries during exercises, such as running, badminton, table tennis and basketball. Moreover, female gender and a high BMI value may be two factors that increase the risk of knee injuries during sports.

Comparative Analysis of the MADS-Box Genes Revealed Their Potential Functions for Flower and Fruit Development in Longan (Dimocarpus longan).

Longan (Dimocarpus longan Lour.) is an important economic crop widely planted in tropical and subtropical regions, and flower and fruit development play decisive effects on the longan yield and fruit quality formation. MCM1, AGAMOUS, DEFICIENS, Serum Response Factor (MADS)-box transcription factor family plays important roles for the flowering time, floral organ identity, and fruit development in plants. However, there is no systematic information of MADS-box family in longan. In this study, 114 MADS-box genes were identified from the longan genome, phylogenetic analysis divided them into type I (Mα, Mß, Mγ) and type II (MIKC*, MIKC C ) groups, and MIKC C genes were further clustered into 12 subfamilies. Comparative genomic analysis of 12 representative plant species revealed the conservation of type II in Sapindaceae and analysis of cis-elements revealed that Dof transcription factors might directly regulate the MIKC C genes. An ABCDE model was proposed for longan based on the phylogenetic analysis and expression patterns of MADS-box genes. Transcriptome analysis revealed that MIKC C genes showed wide expression spectrums, particularly in reproductive organs. From 35 days after KClO3 treatment, 11 MIKC genes were up-regulated, suggesting a crucial role in off-season flower induction, while DlFLC, DlSOC1, DlSVP, and DlSVP-LIKE may act as the inhibitors. The gene expression patterns of longan fruit development indicated that DlSTK, DlSEP1/2, and DlMADS53 could be involved in fruit growth and ripening. This paper carried out the whole genome identification and analysis of the longan MADS-box family for the first time, which provides new insights for further understanding its function in flowers and fruit.

Optimization of Artificial Light for Spinach Growth in Plant Factory Based on Orthogonal Test.

Artificial LED source provides the possibility to regulate the lighting environment in plant factorys that use limited space to plant, aiming at high throughput and good quality. However, different parameters of light intensity, quality, and photoperiod will influence the growth and accumulation of bio-compounds in plants. In order to find the optimal setting of LED light for spinach planting, four group experiments were designed using the orthogonal testing method. According to the experimental results, for growth indexes including fresh weight, dry weight, root length and so on, photoperiod is the most influential factor, light intensity is the second, and light quality is the least. The best light mode (R:B = 4:1, photosynthetic photon flux density (PPFD) = 100 µmol∙m-2∙s-1 and 13/11 h) among all eight possible combinations in the range was also determined. Furthermore, for quality indexes, including the soluble sugar content, protein content and so on, a new scoring method was introduced to make a comprehensive score for evaluating. Then, the light combination (R:B = 4:1, PPFD = 150 µmol∙m-2∙s-1 and 9/15 h) in the range was found as the optimal scheme for spinach quality under those parameters. As there is trade-off between the optimal light parameters for growth and quality, it is necessary to achieve a balance between yield and quality of the plant during production. If farmers want to harvest spinach with larger leaf area and higher yield, they need to pay attention to the adjustment of the photoperiod and use a lower light intensity and a longer lighting time. If they do not mind the yield of the vegetable but want to improve the taste and nutrition of spinach products, they should pay more attention to the light intensity and use a higher light intensity and a shorter lighting time.

A Real-Time Smooth Weighted Data Fusion Algorithm for Greenhouse Sensing Based on Wireless Sensor Networks.

Wireless sensor networks are widely used to acquire environmental parameters to support agricultural production. However, data variation and noise caused by actuators often produce complex measurement conditions. These factors can lead to nonconformity in reporting samples from different nodes and cause errors when making a final decision. Data fusion is well suited to reduce the influence of actuator-based noise and improve automation accuracy. A key step is to identify the sensor nodes disturbed by actuator noise and reduce their degree of participation in the data fusion results. A smoothing value is introduced and a searching method based on Prim's algorithm is designed to help obtain stable sensing data. A voting mechanism with dynamic weights is then proposed to obtain the data fusion result. The dynamic weighting process can sharply reduce the influence of actuator noise in data fusion and gradually condition the data to normal levels over time. To shorten the data fusion time in large networks, an acceleration method with prediction is also presented to reduce the data collection time. A real-time system is implemented on STMicroelectronics STM32F103 and NORDIC nRF24L01 platforms and the experimental results verify the improvement provided by these new algorithms.

Adaptive Energy-Efficient Target Detection Based on Mobile Wireless Sensor Networks.

Target detection is a widely used application for area surveillance, elder care, and fire alarms; its purpose is to find a particular object or event in a region of interest. Usually, fixed observing stations or static sensor nodes are arranged uniformly in the field. However, each part of the field has a different probability of being intruded upon; if an object suddenly enters an area with few guardian devices, a loss of detection will occur, and the stations in the safe areas will waste their energy for a long time without any discovery. Thus, mobile wireless sensor networks may benefit from adaptation and pertinence in detection. Sensor nodes equipped with wheels are able to move towards the risk area via an adaptive learning procedure based on Bayesian networks. Furthermore, a clustering algorithm based on k-means++ and an energy control mechanism is used to reduce the energy consumption of nodes. The extended Kalman filter and a voting data fusion method are employed to raise the localization accuracy of the target. The simulation and experimental results indicate that this new system with adaptive energy-efficient methods is able to achieve better performance than the traditional ones.

Blind RSSD-Based Indoor Localization with Confidence Calibration and Energy Control.

Indoor localization based on wireless sensor networks (WSNs) is an important field of research with numerous applications, such as elderly care, miner security, and smart buildings. In this paper, we present a localization method based on the received signal strength difference (RSSD) to determine a target on a map with unknown transmission information. To increase the accuracy of localization, we propose a confidence value for each anchor node to indicate its credibility for participating in the estimation. An automatic calibration device is designed to help acquire the values. The acceleration sensor and unscented Kalman filter (UKF) are also introduced to reduce the influence of measuring noise in the application. Energy control is another key point in WSN systems and may prolong the lifetime of the system. Thus, a quadtree structure is constructed to describe the region correlation between neighboring areas, and the unnecessary anchor nodes can be detected and set to sleep to save energy. The localization system is implemented on real-time Texas Instruments CC2430 and CC2431 embedded platforms, and the experimental results indicate that these mechanisms achieve a high accuracy and low energy cost.

Energy-Efficient Control with Harvesting Predictions for Solar-Powered Wireless Sensor Networks.

Wireless sensor networks equipped with rechargeable batteries are useful for outdoor environmental monitoring. However, the severe energy constraints of the sensor nodes present major challenges for long-term applications. To achieve sustainability, solar cells can be used to acquire energy from the environment. Unfortunately, the energy supplied by the harvesting system is generally intermittent and considerably influenced by the weather. To improve the energy efficiency and extend the lifetime of the networks, we propose algorithms for harvested energy prediction using environmental shadow detection. Thus, the sensor nodes can adjust their scheduling plans accordingly to best suit their energy production and residual battery levels. Furthermore, we introduce clustering and routing selection methods to optimize the data transmission, and a Bayesian network is used for warning notifications of bottlenecks along the path. The entire system is implemented on a real-time Texas Instruments CC2530 embedded platform, and the experimental results indicate that these mechanisms sustain the networks' activities in an uninterrupted and efficient manner.

Robust feedback zoom tracking for digital video surveillance.

Zoom tracking is an important function in video surveillance, particularly in traffic management and security monitoring. It involves keeping an object of interest in focus during the zoom operation. Zoom tracking is typically achieved by moving the zoom and focus motors in lenses following the so-called "trace curve", which shows the in-focus motor positions versus the zoom motor positions for a specific object distance. The main task of a zoom tracking approach is to accurately estimate the trace curve for the specified object. Because a proportional integral derivative (PID) controller has historically been considered to be the best controller in the absence of knowledge of the underlying process and its high-quality performance in motor control, in this paper, we propose a novel feedback zoom tracking (FZT) approach based on the geometric trace curve estimation and PID feedback controller. The performance of this approach is compared with existing zoom tracking methods in digital video surveillance. The real-time implementation results obtained on an actual digital video platform indicate that the developed FZT approach not only solves the traditional one-to-many mapping problem without pre-training but also improves the robustness for tracking moving or switching objects which is the key challenge in video surveillance.