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This study proposes an enhanced multi-agent swarm control algorithm (EN-MASCA) to solve the problem of efficient patrolling of drone swarms in complex durian orchard environments. It introduces a virtual navigator model to dynamically adjust the patrol path of the drone swarm and perform obstacle avoidance and path optimization in real time according to environmental changes. Different from traditional algorithms that only rely on fixed path planning, the virtual navigator model significantly improves the flexibility and stability of the drone swarm in complex environments. It also applies deep reinforcement learning algorithms to path planning and obstacle avoidance of drone swarms for the first time, improving the algorithm’s adaptability and optimization capabilities by learning dynamic information in complex environments. This innovation significantly improves the applicability of existing methods in complex terrain and dynamic obstacle environments. Finally, it incorporates the simulation characteristics of biological swarm behavior, and on this basis, comprehensively optimizes the flight path, obstacle avoidance and swarm stability of the drone swarm. By improving control strategies and parameter design, it improves the trajectory consistency and mission completion efficiency of the UAV swarm during flight. In the experimental part, this study verified in detail the advantages of the EN-MASCA algorithm in terms of flight trajectory, flight stability, cluster consistency and task completion efficiency by constructing a six-degree-of-freedom UAV motion simulation model and real environment simulation. It provides an efficient and intelligent solution for collaborative patrol operations of drones in durian orchards, which has important practical application value and promotion prospects.

The agricultural WSN (wireless sensor network) has the characteristics of long operation cycle and wide coverage area. In order to cover as much area as possible, farms usually deploy multiple monitoring devices in different locations of the same area. Due to different types of equipment, monitoring data will vary greatly, and too many monitoring nodes also reduce the efficiency of the network. Although there have been some studies on data fusion algorithms, they have problems such as ignoring the dynamic changes of time series, weak anti-interference ability, and poor processing of data fluctuations. So in this study, a data fusion algorithm for optimal node tracking in agricultural wireless sensor networks is designed. By introducing the dynamic bending distance in the dynamic time warping algorithm to replace the absolute distance in the fuzzy association algorithm and combine the sensor’s own reliability and association degree as the weighted fusion weight, which improved the fuzzy association algorithm. Finally, another three algorithm were tested for multi-temperature sensor data fusion. Compare with the kalman filter, arithmetic mean and fuzzy association algorithm, the average value of the improved data fusion algorithm is 29.5703, which is close to the average value of the other three algorithms, indicating that the data distribution is more even. Its extremely bad value is 8.9767, which is 10.04%, 1.14% and 9.85% smaller than the other three algorithms, indicating that it is more robust when dealing with outliers. Its variance is 2.6438, which is 2.82%, 0.65% and 0.27% smaller than the other three algorithms, indicating that it is more stable and has less data volatility. The results show that the algorithm proposed in this study has higher fusion accuracy and better robustness, which can obtain the fusion value that truly feedbacks the agricultural environment conditions. It reduces production costs by reducing redundant monitoring devices, the energy consumption and improves the data collection efficiency in wireless sensor networks.

Greenhouse vegetable production was a complex agricultural system influenced by multiple interrelated environmental and management factors. Its irrigation control was a critical but not singularly decisive component. Traditional irrigation methods often caused the water wastage, uneven resource utilization and limited adaptability to dynamic environmental conditions, thereby hindering the sustainable production efficiency. To address these challenges comprehensively, this study proposed an advanced irrigation control method by utilizing the enhanced reinforcement learning approach. The Enhanced Negative-incentive Proximal Policy Optimization (ENPPO) algorithm is introduced, which integrates the dynamic clipping functions and negative incentives to manage the intricacies of continuous action spaces and high-dimensional environmental states. By incorporating real-time sensor data and historical irrigation records, the ENPPO algorithm accurately predicts the optimal irrigation volumes aligned with various vegetable growth stages. Experimental results showed that ENPPO algorithm outperforms conventional methods such as PPO and TRPO in prediction accuracy, convergence efficiency and water resource utilization. It minimized both excessive and insufficient irrigation scenarios, thus promoting enhanced vegetable yield and quality while simultaneously reducing agricultural production costs. Overall, this study presented the versatile technical solution for intelligent irrigation management within greenhouse systems, highlighting its substantial potential to advance sustainable agricultural practices.

Durian is a valuable tropical fruit whose pollination heavily relies on bats and nocturnal insects. However, environmental degradation and pesticide usage have reduced insect populations, leading to inefficient natural pollination. This study proposes an AI-powered drone-based pollination method for complex durian orchards, integrating improved object detection and optimized path planning. Specifically, we enhance the YOLO-v8 algorithm using the GhostNet lightweight network to reduce computational complexity while boosting detection precision. For path planning, we develop an Enhanced TSP (EN-TSP) algorithm based on a branch and bound strategy with least-cost optimization. Experimental results demonstrate that the proposed method improves detection accuracy by 5.85% and path efficiency by 26.89% compared to baseline algorithms. The novel use of GhostNet with YOLO-v8 enables superior detection of durian flowers under low-light and occluded conditions, while EN-TSP ensures globally optimal drone routes, reducing travel distance and improving operational reliability. This integrated solution advances smart agriculture by enabling scalable, efficient, and precise pollination, reducing labor costs and increasing durian yield and quality.

WSN (wireless sensor network) plays a very important role in the agricultural environment monitoring. Although solar energy and other power supply methods are used to solve the node energy problem, the monitoring equipment works outdoors for a long time, which is easily affected by the environment. The supply is unstable to cause abnormalities in some nodes. So this study proposes a ZIRRA algorithm (ZigBee immune routing repair algorithm) for the rechargeable agricultural WSN. It simulates the working mechanism of the immune system and designs modules such as identification, processing, cloning and storage, which can provide a better repair strategy for abnormal nodes. Then it compares the quality of the backup nodes and replaces the backup nodes with poor quality, so that the optimal paths are maintained between source nodes and middle relay nodes, which increases the optimization ability of the algorithm. The experimental results show that the ZIRRA algorithm shows significant advantages in routing node repair mechanism. Compared with the LFRA, AR-TORA and ICCO algorithms, the average routing energy consumption of the ZIRRA algorithm reduced 35.33%, 58.37% and 45.15% , the data transmission delay reduced by 23.72%, 36.74% and 16.28%, and the average node survival time extended 25.08%, 33.55% and 13.88%. In addition, the maximum communication time and network throughput of the ZIRRA algorithm increased 44.49% and 13.03% at the scale of 1000 to 2000 nodes. These quantitative results show that the ZIRRA algorithm can improve the energy efficiency, transmission reliability and stability. The ZIRRA algorithm draws on the working principle of the immune system and repairs abnormal nodes through identification, processing, cloning and storage modules. Unlike the traditional node repair algorithms, the ZIRRA algorithm has higher efficiency and accuracy in identifying and processing abnormal nodes through the improved clone tracking algorithm. It uses an improved clone tracking algorithm in the learning module, improves the cloning and mutation mechanisms, and generates the optimal antibodies for repairing abnormal nodes. It also integrates an adaptive energy management strategy to cope with fluctuations in energy levels by prioritizing the transmission of critical data and reducing the frequency of non-essential communications, which improves the network stability and data transmission reliability.

Durian is a major tropical crop in Southeast Asia, but its yield and quality are severely impacted by a range of pests and diseases. Manual inspection remains the dominant detection method but suffers from high labor intensity, low accuracy, and difficulty in scaling. To address these challenges, this paper proposes EN-YOLO, a novel enhanced YOLO-based deep learning model that integrates the EfficientNet backbone and multimodal attention mechanisms for precise detection of durian pests and diseases. The model removes redundant feature layers and introduces a large-span residual edge to preserve key spatial information. Furthermore, a multimodal input strategy—incorporating RGB, near-infrared and thermal imaging—is used to enhance robustness under variable lighting and occlusion. Experimental results on real orchard datasets demonstrate that EN-YOLO outperforms YOLOv8 (You Only Look Once version 8), YOLOv5-EB (You Only Look Once version 5—Efficient Backbone), and Fieldsentinel-YOLO in detection accuracy, generalization, and small-object recognition. It achieves a 95.3% counting accuracy and shows superior performance in ablation and cross-scene tests. The proposed system also supports real-time drone deployment and integrates an expert knowledge base for intelligent decision support. This work provides an efficient, interpretable, and scalable solution for automated pest and disease management in smart agriculture.

With the rapid growth of the agricultural information and the need for data analysis, how to accurately extract useful information from massive data has become an urgent first step in agricultural data mining and application. In this study, an agricultural question-answering information extraction method based on the BE-BILSTM (Improved Bidirectional Long Short-Term Memory) algorithm is designed. Firstly, it uses Python’s Scrapy crawler framework to obtain the information of soil types, crop diseases and pests, and agricultural trade information, and remove abnormal values. Secondly, the information extraction converts the semi-structured data by using entity extraction methods. Thirdly, the BERT (Bidirectional Encoder Representations from Transformers) algorithm is introduced to improve the performance of the BILSTM algorithm. After comparing with the BERT-CRF (Conditional Random Field) and BILSTM algorithm, the result shows that the BE-BILSTM algorithm has better information extraction performance than the other two algorithms. This study improves the accuracy of the agricultural information recommendation system from the perspective of information extraction. Compared with other work that is done from the perspective of recommendation algorithm optimization, it is more innovative; it helps to understand the semantics and contextual relationships in agricultural question and answer, which improves the accuracy of agricultural information recommendation systems. By gaining a deeper understanding of farmers’ needs and interests, the system can better recommend relevant and practical information.

Greenhouse cultivation technology has greatly contributed to the development of agriculture in Malaysia. Understanding how to monitor the greenhouse environment with high efficiency and low power consumption is particularly important. In this research, a wireless sensor network for agricultural greenhouses based on the improved Zigbee protocol is designed. Its hardware consists of various sensors and Zigbee nodes commonly used in agricultural greenhouses. On the basis of this hardware, this research designed the network topology of WMN (Wireless mesh network) by comparing the advantages and disadvantages of various topologies, and combined with this structure, proposed an improved ZigBee routing protocol EMP-ZBR to solve the question regarding energy loss and the network congestion of wireless networks. After testing EMP-ZBR and traditional Zigbee routing protocols, the improved EMP-ZBR protocol is superior to traditional Zigbee routing in terms of the end-to-end average delay, packet delivery rate, routing control overhead and routing discovery frequency, which were optimized about 1.1%, 15.2%, 15.2%, 8.1 ms in different mobile pause times, and 9.8%, 19.3%, 15.7% and 121 ms in different packet sending rates. The agreement proves that EMP-ZBR can more effectively alleviate the impact of congestion and improve the overall performance of the data monitoring system for agricultural greenhouses.

Durian is one of the tropical fruits that requires soil nutrients in its cultivation. It is important to understand the relationship between the content of critical nutrients, such as nitrogen (N), phosphorus (P), and potassium (K) in the soil and durian yield. How to optimize the fertilization plan is also important to the durian planting. Thus, this study proposes an Improved Radial Basis Neural Network Algorithm (IM-RBNNA) in the durian precision fertilization. It uses the gray wolf algorithm to optimize the weights and thresholds of the RBNNA algorithm, which can improve the prediction accuracy of the RBNNA algorithm for the soil nutrient content and its relationship with the durian yield. It also collects the soil nutrients and historical yield data to build the IM-RBNNA model and compare with other similar algorithms. The results show that the IM-RBNNA algorithm is better than the other three algorithms in the average relative error, average absolute error, and coefficient of determination between the predicted and true values of soil N, K, and P fertilizer contents. It also predicts the relationship between soil nutrients and yield, which is closer to the true value. It shows that the IM-RBNNA algorithm can accurately predict the durian soil nutrient content and yield, which is benefited for farmers to make agronomic plans and management strategies. It uses soil nutrient resources efficiently, which reduces the environmental negative impacts. It also ensures that the durian tree can obtain the appropriate amount of nutrients, maximize its growth potential, reduce production costs, and increase yields.

Durian is an important economic crop in Southeast Asia; it is susceptible to various pests and diseases, which affects its fruit quality and yield. Traditional manual and mechanical spraying methods have many shortcomings in spraying durian blind spots (such as tall and dense durian canopies). Drones can fly over these blind spots and carry out all-around pesticide spraying. How to combine artificial intelligence and automatic control to realize the spraying of pesticides by drones in the blind spots of durian trees has become a key research issue. Therefore, this study proposes an IM-PID (Improved Proportional-Integral-Derivative) control algorithm to improve the accuracy of drone pesticide spraying in the blind areas of durian tree pests and diseases. It introduces the RBF (Radial Basis Function) neural network to adjust the proportion, integral, and differential of incremental PID controller, which adjusts spraying parameters in real time to improve the accuracy of pesticide spraying. The experimental results show that the IM-PID control algorithm is superior to the traditional PID, fuzzy logic and sliding mode control algorithms regarding the spray flow accuracy, droplet distribution uniformity, and dynamic adjustment capabilities. It can significantly improve the efficiency of pesticide spraying in durian orchards and solve the problem of traditional spraying methods in blind areas, which controls pests and diseases and ensures the high quality and yield of durian fruits. It also reduces the environmental impact of excessive pesticide spraying and improves the economic benefits of durian cultivation.