Multi-scenario pear tree inflorescence detection based on improved YOLOv7 object detection algorithm

Efficient and precise thinning during the orchard blossom period is a crucial factor in enhancing both fruit yield and quality. The accurate recognition of inflorescence is the cornerstone of intelligent blossom equipment. To advance the process of intelligent blossom thinning, this paper addresses the issue of suboptimal performance of current inflorescence recognition algorithms in detecting dense inflorescence at a long distance. It introduces an inflorescence recognition algorithm, YOLOv7-E, based on the YOLOv7 neural network model. YOLOv7 incorporates an efficient multi-scale attention mechanism (EMA) to enable cross-channel feature interaction through parallel processing strategies, thereby maximizing the retention of pixel-level features and positional information on the feature maps. Additionally, the SPPCSPC module is optimized to preserve target area features as much as possible under different receptive fields, and the Soft-NMS algorithm is employed to reduce the likelihood of missing detections in overlapping regions. The model is trained on a diverse dataset collected from real-world field settings. Upon validation, the improved YOLOv7-E object detection algorithm achieves an average precision and recall of 91.4% and 89.8%, respectively, in inflorescence detection under various time periods, distances, and weather conditions. The detection time for a single image is 80.9 ms, and the model size is 37.6 Mb. In comparison to the original YOLOv7 algorithm, it boasts a 4.9% increase in detection accuracy and a 5.3% improvement in recall rate, with a mere 1.8% increase in model parameters. The YOLOv7-E object detection algorithm presented in this study enables precise inflorescence detection and localization across an entire tree at varying distances, offering robust technical support for differentiated and precise blossom thinning operations by thinning machinery in the future

Research Progress Regarding the Precision of Dosing and Distribution Devices for Fertilizers

As a key component of fertilization equipment, the fertilizer discharger has an important impact on the accuracy of the amount of fertilizer applied during the fertilization process. Countries around the world have been advocating for reducing the use of chemical fertilizers and improving fertilizer utilization, and researchers have also conducted in-depth research on precision fertilizer devices. In order to further improve the precision of dosing and distribution devices for fertilizers, in this study, four types of fertilizer dischargers (spiral fertilizer dischargers, groove wheel fertilizer dischargers, disc fertilizer dischargers, and air-feed fertilizer dischargers) which are currently commonly used are thoroughly analyzed. The operating principle and performance characteristics of the fertilizer dischargers are elaborated upon, and the current research status of their structure and working parameter optimization are discussed and summarized. Overall, the research of Chinese researchers is mainly described. The problems existing in research on the precise fertilizer discharge of fertilizer dischargers are pointed out, and the future development trend is predicted, aiming to provide a beneficial reference for improving the technical level of precise fertilizer discharge

Research Progress in Intelligent Diagnosis Key Technology for Orchard Nutrients

The intelligent diagnosis key technology of orchard nutrients provides a decision-making basis for precision fertilization, which has important research significance. This article reviewed the recent research literature, compared and analyzed existing technologies, and summarized solved and unresolved problems. It aimed to find breakthroughs to further improve the level of intelligent diagnosis key technology for orchard nutrients, and promote the implementation and application of the technology. Research had found that the current rapid nutrient detection technologies were mostly based on spectral data, with a focus on preprocessing algorithms and regression models. Hyperspectral technology shows good performance in predicting tree and soil nutrients due to its large number of characteristic variables. Meanwhile, preprocessing algorithms such as filtering, transformation, and feature band selection had also solved the problem of data redundancy. However, there were few studies for small and trace elements, and field applications. Laser breakdown-induced spectroscopy has good prospects for soil nutrient detection, as it can simultaneously detect multiple nutrients. There had been some studies on the technology for generating suitable nutrient standards for orchards in terms of soil and tree nutrients, but it requires a long and extensive experiment, which is time-consuming and laborious. A universal and rapid method needs to be studied to meet the construction needs of suitable nutrient standards for different varieties of fruit trees

State of Charge Estimation for Lithium-Ion Battery via MILS Algorithm Based on Ensemble Kalman Filter

Accurate state of charge (SOC) is great significant for lithium-ion battery to maximize its performance and prevent it from overcharging or overdischarging. This paper presents an ensemble Kalman filter- (EnKF-) based SOC estimation algorithm for lithium-ion battery. Firstly, the lithium-ion battery is modeled by the first-order RC equivalent circuit, and the multi-innovation least square (MILS) algorithm is used to perform online parameter identification of the model parameters. Then, the ensemble Kalman filter (EnKF) is introduced to estimate the state of charge. Finally, two typical experiments including constant current discharge experiment and cycling dynamic stress test are applied to evaluate the performance of the joint algorithm of MILS and EnKF. The experimental results show that the joint algorithm-based ensemble Kalman filter can achieve fast tracking and higher estimation accuracy for lithium-ion battery SOC

Technologies and Equipment of Mechanized Blossom Thinning in Orchards: A Review

Orchard thinning can avoid biennial bearing and improve fruit quality, which is a necessary agronomic section in orchard management. The existing methods of artificial fruit thinning and chemical spraying are no longer suitable for the development of modern agriculture. With the continuous acceleration of the construction process of modern orchards, blossom thinning mechanization has become an inevitable trend in the development of the orchard flower and fruit management. Based on relevant reports in the past 20 years, the paper discusses the current level of development of mechanized blossom thinning technologies and equipment in orchards from three aspects: mechanism research, machine development, and intelligent upgrading. Firstly, for thinning mechanism research, three directions were investigated: the rope flexible hitting force, thinning agronomic requirements, and the fruit tree growth model between thinning and fruit yields. Secondly, for marketable machine developments, two types of machines were investigated: the hand-held thinner and tractor-mounted thinner. The hand-held thinner is mainly suitable for traditional old orchards with a messy canopy structure, especially in the interior and top of the canopy. The tractor-mounted thinner is mainly suitable for orchards with the same crown structure, such as the hedge type, trunk type, and V-type. Thirdly, for equipment intelligent upgrading, the research of the intelligent detection algorithm for inflorescence on the fruit tree was investigated, for species including the apple, pear, citrus, grape, litchi, mango, and apricot. Finally, combining the advantages and disadvantages of the research, the authors propose thoughts and prospects, which can provide a reference for the design and applications of orchard mechanized blossom thinning

Dammarane Sapogenins Ameliorates Neurocognitive Functional Impairment Induced by Simulated Long-Duration Spaceflight

Increasing evidence indicates the occurrence of cognitive impairment in astronauts under spaceflight compound conditions, but the underlying mechanisms and countermeasures need to be explored. In this study, we found that learning and memory abilities were significantly reduced in rats under a simulated long-duration spaceflight environment (SLSE), which includes microgravity, isolation confinement, noises, and altered circadian rhythms. Dammarane sapogenins (DS), alkaline hydrolyzed products of ginsenosides, can enhance cognition function by regulating brain neurotransmitter levels and inhibiting SLSE-induced neuronal injury. Bioinformatics combined with experimental verification identified that the PI3K-Akt-mTOR pathway was inhibited and the MAPK pathway was activated during SLSE-induced cognition dysfunction, whereas DS substantially ameliorated the changes in brain. These findings defined the characteristics of SLSE-induced cognitive decline and the mechanisms by which DS improves it. The results provide an effective candidate for improving cognitive function in spaceflight missions

Mobile Health Technology to Improve Care for Patients With Atrial Fibrillation

Background Current management of patients with atrial fibrillation (AF) is limited by low detection of AF, non-adherence to guidelines, and lack of consideration of patients’ preferences, thus highlighting the need for a more holistic and integrated approach to AF management. Objective The objective of this study was to determine whether a mobile health (mHealth) technology-supported AF integrated management strategy would reduce AF-related adverse events, compared with usual care. Methods This is a cluster randomized trial of patients with AF older than 18 years of age who were enrolled in 40 cities in China. Recruitment began on June 1, 2018 and follow-up ended on August 16, 2019. Patients with AF were randomized to receive usual care, or integrated care based on a mobile AF Application (mAFA) incorporating the ABC (Atrial Fibrillation Better Care) Pathway: A, Avoid stroke; B, Better symptom management; and C, Cardiovascular and other comorbidity risk reduction. The primary composite outcome was a composite of stroke/thromboembolism, all-cause death, and rehospitalization. Rehospitalization alone was a secondary outcome. Cardiovascular events were assessed using Cox proportional hazard modeling after adjusting for baseline risk. Results There were 1,646 patients allocated to mAFA intervention (mean age, 67.0 years; 38.0% female) with mean follow-up of 262 days, whereas 1,678 patients were allocated to usual care (mean age, 70.0 years; 38.0% female) with mean follow-up of 291 days. Rates of the composite outcome of ‘ischemic stroke/systemic thromboembolism, death, and rehospitalization’ were lower with the mAFA intervention compared with usual care (1.9% vs. 6.0%; hazard ratio [HR]: 0.39; 95% confidence interval [CI]: 0.22 to 0.67; p < 0.001). Rates of rehospitalization were lower with the mAFA intervention (1.2% vs. 4.5%; HR: 0.32; 95% CI: 0.17 to 0.60; p < 0.001). Subgroup analyses by sex, age, AF type, risk score, and comorbidities demonstrated consistently lower HRs for the composite outcome for patients receiving the mAFA intervention compared with usual care (all p < 0.05). Conclusions An integrated care approach to holistic AF care, supported by mHealth technology, reduces the risks of rehospitalization and clinical adverse events. (Mobile Health [mHealth] technology integrating atrial fibrillation screening and ABC management approach trial; ChiCTR-OOC-17014138)