Sung-Jung Hsiao

Precision agriculture (PA) is an alternative for increasing agricultural productivity in an efficient and integrated manner. In this study, a comprehensive intelligent monitoring system was developed to capture atmospheric conditions, soil parameters, and crop physiology for mango crops, integrating physical sensing devices, object detection models, WSNs, and IoT. A physical sensing device validation was conducted to evaluate the performance of the humidity, temperature, soil moisture, and soil pH sensors using MAPE analysis, yielding a MAPE of 0.63%. A proposed Improved-YOLOv10 has been developed to capture the mango fruit condition, whether it has the disease or not, consisting of five classes, including alternaria, anthracnose, black_mould_rot, stem_end_rot, and healthy. The Improved-YOLOv10 leverages the advantages of the StarNet architecture, which is embedded into the backbone structure to achieve high-dimensional features in the low-dimensional space, which increases the accuracy of performance without adding computational cost. The Improved-YOLOv10 mAP50 performance has achieved the value of 0.939, while Faster R-CNN, SSD, YOLOv5, YOLOv7, YOLOv8, YOLOv10, and YOLOv11 obtained the mAP ₅₀ of 0.714, 0.862, 0.880, 0.762, 0.893, 0.905, and 0.921, respectively. Meanwhile, the constructed WSNs system implements the Aquila optimizer (AO) method to achieve energy efficiency and optimal time response, which indicates that at the 10 nodes, the energy consumption, delay, and loss rates are 165 mJ, 128 ms, and 7.6%, respectively. The results of the experiment show that the constructed WSNs system has achieved the optimal efficiency compared to the existing system.

Precision agriculture (PA) is an alternative for increasing agricultural productivity in an efficient and integrated manner. In this study, a comprehensive intelligent monitoring system was developed to capture atmospheric conditions, soil parameters and crop physiology for mango crops, integrating physical sensing devices, object detection models, WSNs and IoT. A physical sensing device validation was conducted to evaluate the performance of the humidity, temperature, soil moisture and soil pH sensors using MAPE analysis, yielding a MAPE of 0.63%. A proposed improved YOLOv10 has been developed to capture the mango fruit condition, whether it has a disease or not, consisting of five classes, including alternaria, anthracnose, black_mould_rot, stem_end_rot and healthy. The improved YOLOv10 leverages the advantages of the StarNet architecture, which is embedded into the backbone structure to achieve high‐dimensional features in low‐dimensional space, which increases the accuracy of performance without adding computational cost. The improved YOLOv10 achieved an mAP ₅₀ value of 0.939, whereas faster R‐CNN, SSD, YOLOv5, YOLOv7, YOLOv8, YOLOv10 and YOLOv11 achieved mAP ₅₀ values of 0.714, 0.862, 0.880, 0.762, 0.893, 0.905 and 0.921, respectively. Meanwhile, the constructed WSN system implements the Aquila optimiser (AO) method to achieve energy efficiency and an optimal response time, which indicates that at 10 nodes, the energy consumption, delay and loss rate are 165 mJ, 128 ms and 7.6%, respectively. The results of the experiment show that the constructed WSN system has achieved optimal efficiency compared to the existing system.

Mango is a plant with high economic value in the agricultural industry; thus, it is necessary to maximize the productivity performance of the mango plant, which can be done by implementing artificial intelligence. In this study, a lightweight object detection model will be developed that can detect mango plant conditions based on disease potential, so that it becomes an early detection warning system that has an impact on increasing agricultural productivity. The proposed lightweight model integrates YOLOv7-Tiny and the proposed modules, namely the C2S module. The C2S module consists of three sub-modules such as the convolutional block attention module (CBAM), the coordinate attention (CA) module, and the squeeze-and-excitation (SE) module. The dataset is constructed by eight classes, including seven classes of disease conditions and one class of health conditions. The experimental result shows that the proposed lightweight model has the optimal results, which increase by 13.15% of mAP50 compared to the original model YOLOv7-Tiny. While the mAP50:95 also achieved the highest results compared to other models, including YOLOv3-Tiny, YOLOv4-Tiny, YOLOv5, and YOLOv7-Tiny. The advantage of the proposed lightweight model is the adaptability that supports it in constrained environments, such as edge computing systems. This proposed model can support a robust, precise, and convenient precision agriculture system for the user.

Fire disasters have a serious impact on human life and property. Fire detection methods have been developed as early detection in preventing fire disasters, including implementing object detection models. However, the challenges in fire detection are random object shapes and complex backgrounds, which cause the model to be unable to detect fire optimally. In addition, to support practical application, the model must be efficient in memory-consuming. To address these issues, this study proposed FSP-YOLO as a fire detection algorithm that integrates the YOLOv8 model with the attention module and the improved GhostNet (iGhostNet) that can detect three classes, including fire, smoke, and people. The improvement of iGhostNet is implementing coordinate attention module and trained integrated learning, which has an impact on increasing accuracy and reducing computational costs. With this improvement, the proposed fire detection model could gain outstanding performance, reducing computational costs, and a lightweight architecture. Furthermore, to validate the performance of FSP-YOLO, several comparative experiments have been conducted, including single and multiple module experiments, evaluation under different YOLOv8, experiments with other object detection algorithms, and experiments in practical application. From these experiments, FSP-YOLO represents the optimal performance, which results in mAP0.5 of 0.977, precision of 0.952, and recall of 0.973. Based on weight or model size, compared to the other fire detection algorithms such as YOLOFM, FireRPG, Fine Tuned YOLOv8, Improved YOLOv8, YOLOv7-Fire, and Improved-YOLOv7, FSP-YOLO has a lighter size of 53.18 MB. Therefore, FSP-YOLO is reliable in accuracy but reduces the computational cost.

Mango fruit is one of the main fruit commodities that contributes to Taiwan’s income. The implementation of technology is an alternative to increasing the quality and quantity of mango plantation product productivity. In this study, a Wireless Sensor Networks (“WSNs”)-based intelligent mango plantation monitoring system will be developed that implements deep reinforcement learning (DRL) technology in carrying out prediction tasks based on three classifications: “optimal,” “sub-optimal,” or “not-optimal” conditions based on three parameters including humidity, temperature, and soil moisture. The key idea is how to provide a precise decision-making mechanism in the real-time monitoring system. A value function-based will be employed to perform DRL model called deep Q-network (DQN) which contributes in optimizing the future reward and performing the precise decision recommendation to the agent and system behavior. The WSNs experiment result indicates the system’s accuracy by capturing the real-time environment parameters is 98.39%. Meanwhile, the results of comparative accuracy model experiments of the proposed DQN, individual Q-learning, uniform coverage (UC), and Naïve Bayes classifier (NBC) are 97.60%, 95.30%, 96.50%, and 92.30%, respectively. From the results of the comparative experiment, it can be seen that the proposed DQN used in the study has the most optimal accuracy. Testing with 22 test scenarios for “optimal,” “sub-optimal,” and “not-optimal” conditions was carried out to ensure the system runs well in the real-world data. The accuracy percentage which is generated from the real-world data reaches 95.45%. From the results of the cost analysis, the system can provide a low-cost system compared to the conventional system.

The aquaculture production sector is one of the suppliers of global food consumption needs. Countries that have a large amount of water contribute to the needs of aquaculture production, especially the freshwater fisheries sector. Indonesia is a country that has a large number of large bodies of water and is the top-five producer of aquaculture production. Technology and engineering continue to be developed to improve the quality and quantity of aquaculture production. One aspect that can be observed is how the condition of fish pond water is healthy and supports fish growth. Various studies have been conducted related to the aquaculture monitoring system, but the problem is how effective it is in terms of accuracy of the resulting output, implementation, and costs. In this research, data fusion (DF) and deep reinforcement learning (DRL) were implemented in an aquaculture monitoring system with temperature, turbidity, and pH parameters to produce valid and accurate output. The stage begins with testing sensor accuracy as part of sensor quality validation, then integrating sensors with wireless sensor networks (WSNs) so they can be accessed in real time. The implemented DF is divided into three layers: first, the signal layer consists of WSNs and their components. Second, the feature layer consists of DRL combined with deep learning (DL). Third, the decision layer determines the output of the condition of the fish pond in “normal” or “not normal” conditions. The analysis and testing of this system look at several factors, i.e., (1) the accuracy of the performance of the sensors used; (2) the performance of the models implemented; (3) the comparison of DF-DRL-based systems with rule-based algorithm systems; and (4) the cost effectiveness compared to labor costs. Of these four factors, the DF-DRL-based aquaculture monitoring system has a higher percentage value and is a low-cost alternative for an accurate aquaculture monitoring system.