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Information and communication technology (ICT) components, especially actuators in automated irrigation systems, are essential for managing precise irrigation and optimal soil moisture, enhancing orchard growth and yield. However, actuator malfunctions can lead to inefficient irrigation, resulting in water imbalances that impact crop health and reduce productivity. The objective of this study was to develop a signal processing technique to detect potential malfunctions based on the power consumption level and operating status of actuators for an automated orchard irrigation system. A demonstration orchard with four apple trees was set up in a 3 m × 3 m soil test bench inside a greenhouse, divided into two sections to enable independent irrigation schedules and management. The irrigation system consisted of a single pump and two solenoid valves controlled by a Python-programmed microcontroller. The microcontroller managed the pump cycling ‘On’ and ‘Off’ states every 60 s and solenoid valves while storing and transmitting sensor data to a smartphone application for remote monitoring. Commercial current sensors measured actuator power consumption, enabling the identification of normal and abnormal operations by applying threshold values to distinguish activation and deactivation states. Analysis of power consumption, control commands, and operating states effectively detected actuator operations, confirming reliability in identifying pump and solenoid valve failures. For the second solenoid valve in channel 2, with 333 actual instances of normal operation and 60 actual instances of abnormal operation, the model accurately detected 316 normal and 58 abnormal instances. The proposed method achieved a mean average precision of 99.9% for detecting abnormal control operation of the pump and solenoid valve of channel 1 and a precision of 99.7% for the solenoid valve of channel 2. The proposed approach effectively detects actuator malfunctions, demonstrating the potential to enhance irrigation management and crop productivity. Future research will integrate advanced machine learning with signal processing to improve fault detection accuracy and evaluate the scalability and adaptability of the system for larger orchards and diverse agricultural applications.

Efficient management of soil nutrients is essential for optimizing crop production, ensuring sustainable agricultural practices, and addressing the challenges posed by population growth and environmental degradation. Smart agriculture, using advanced technologies, plays an important role in achieving these goals by enabling real-time monitoring and precision management of nutrients. In open-field soil cultivation, spatial variability in soil properties demands site-specific nutrient management and integration with variable-rate technology (VRT) to optimize fertilizer application, reduce nutrient losses, and enhance crop yields. Hydroponic solution cultivation, on the other hand, requires precise monitoring and control of nutrient solutions to maintain optimal conditions for plant growth, ensuring efficient use of water and fertilizers. This review aims to explore recent trends in soil and solution nutrient sensing technologies for open-field soil and facilitated hydroponic cultivation, highlighting advancements that promote efficiency and sustainability. Key technologies include electrochemical and optical sensors, Internet of Things (IoT)-enabled monitoring, and the integration of machine learning (ML) and artificial intelligence (AI) for predictive modeling. Blockchain technology is also emerging as a tool to enhance transparency and traceability in nutrient management, promoting compliance with environmental standards and sustainable practices. In open-field soil cultivation, real-time sensing technologies support targeted nutrient application by accounting for spatial variability, minimizing environmental risks such as runoff and eutrophication. In hydroponic solution cultivation, precise solution sensing ensures nutrient balance, optimizing plant health and productivity. By advancing these technologies, smart agriculture can achieve sustainable crop production, improved resource efficiency, and environmental protection, fostering a resilient food system.

The geometric feature characterization of fruit trees plays a role in effective management in orchards. LiDAR (light detection and ranging) technology for object detection enables the rapid and precise evaluation of geometric features. This study aimed to quantify the height, canopy volume, tree spacing, and row spacing in an apple orchard using a three-dimensional (3D) LiDAR sensor. A LiDAR sensor was used to collect 3D point cloud data from the apple orchard. Six samples of apple trees, representing a variety of shapes and sizes, were selected for data collection and validation. Commercial software and the python programming language were utilized to process the collected data. The data processing steps involved data conversion, radius outlier removal, voxel grid downsampling, denoising through filtering and erroneous points, segmentation of the region of interest (ROI), clustering using the density-based spatial clustering (DBSCAN) algorithm, data transformation, and the removal of ground points. Accuracy was assessed by comparing the estimated outputs from the point cloud with the corresponding measured values. The sensor-estimated and measured tree heights were 3.05 ± 0.34 m and 3.13 ± 0.33 m, respectively, with a mean absolute error (MAE) of 0.08 m, a root mean squared error (RMSE) of 0.09 m, a linear coefficient of determination (r2) of 0.98, a confidence interval (CI) of −0.14 to −0.02 m, and a high concordance correlation coefficient (CCC) of 0.96, indicating strong agreement and high accuracy. The sensor-estimated and measured canopy volumes were 13.76 ± 2.46 m3 and 14.09 ± 2.10 m3, respectively, with an MAE of 0.57 m3, an RMSE of 0.61 m3, an r2 value of 0.97, and a CI of −0.92 to 0.26, demonstrating high precision. For tree and row spacing, the sensor-estimated distances and measured distances were 3.04 ± 0.17 and 3.18 ± 0.24 m, and 3.35 ± 0.08 and 3.40 ± 0.05 m, respectively, with RMSE and r2 values of 0.12 m and 0.92 for tree spacing, and 0.07 m and 0.94 for row spacing, respectively. The MAE and CI values were 0.09 m, 0.05 m, and −0.18 for tree spacing and 0.01, −0.1, and 0.002 for row spacing, respectively. Although minor differences were observed, the sensor estimates were efficient, though specific measurements require further refinement. The results are based on a limited dataset of six measured values, providing initial insights into geometric feature characterization performance. However, a larger dataset would offer a more reliable accuracy assessment. The small sample size (six apple trees) limits the generalizability of the findings and necessitates caution in interpreting the results. Future studies should incorporate a broader and more diverse dataset to validate and refine the characterization, enhancing management practices in apple orchards.

LiDAR sensors have great potential for enabling crop recognition (e.g., plant height, canopy area, plant spacing, and intra-row spacing measurements) and the recognition of agricultural working environments (e.g., field boundaries, ridges, and obstacles) using agricultural field machinery. The objective of this study was to review the use of LiDAR sensors in the agricultural field for the recognition of crops and agricultural working environments. This study also highlights LiDAR sensor testing procedures, focusing on critical parameters, industry standards, and accuracy benchmarks; it evaluates the specifications of various commercially available LiDAR sensors with applications for plant feature characterization and highlights the importance of mounting LiDAR technology on agricultural machinery for effective recognition of crops and working environments. Different studies have shown promising results of crop feature characterization using an airborne LiDAR, such as coefficient of determination (R2) and root-mean-square error (RMSE) values of 0.97 and 0.05 m for wheat, 0.88 and 5.2 cm for sugar beet, and 0.50 and 12 cm for potato plant height estimation, respectively. A relative error of 11.83% was observed between sensor and manual measurements, with the highest distribution correlation at 0.675 and an average relative error of 5.14% during soybean canopy estimation using LiDAR. An object detection accuracy of 100% was found for plant identification using three LiDAR scanning methods: center of the cluster, lowest point, and stem–ground intersection. LiDAR was also shown to effectively detect ridges, field boundaries, and obstacles, which is necessary for precision agriculture and autonomous agricultural machinery navigation. Future directions for LiDAR applications in agriculture emphasize the need for continuous advancements in sensor technology, along with the integration of complementary systems and algorithms, such as machine learning, to improve performance and accuracy in agricultural field applications. A strategic framework for implementing LiDAR technology in agriculture includes recommendations for precise testing, solutions for current limitations, and guidance on integrating LiDAR with other technologies to enhance digital agriculture.

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.

Introduction: COVID 19 is an unknown virus affecting mankind creating a deadly experience to all. It is true for Bangladesh also. So the objectives of the present study are to find the clinicopathological features and outcome of COVID patients admitted to three COVID dedicated hospitals of Chittagong, Bangladesh. Methods: This was an observational study where a total of 209 patients admitted to three COVID dedicated hospital were recruited. Clinicopathological data were recorded and patients were under observation till discharge and thus outcome were recorded. Prior consent was taken from the patients and ethical clearance was also taken. Data were compiled and analyzed by Statistical Package for Social Sciences-20. Results: Among 209 patients most of them were male 139 (66.5%) and male to female ratio was 1.98:1. Age group distribution revealed more were aggregated in the age group of 41–50 years 36 (17.2%), 51–60 years 54 (25.8%), and 61–70 years 57 (27.3%). Among all 92 (44%) patients were reverse transcription-polymerase chain reaction (RT-PCR) positive and 117 (56%) were probable cases. Fever was present in 195 (93.3%) cases, cough in 180 (86.1%), respiratory distress in 105 (50.2%) anosmia in 123 (58.8%), aguesea in 112 (53.58%) and lethargy was present in 143 (68.42%). Chest X-ray findings revealed 73 (34.9%) had bilateral patchy opacities, 20 (9.6%) had unilateral opacities 65 (31.1%) had consolidations, 6 (2.9%) had ground glass opacities, and 2 (1.0%) had pleural effusion. Supplemental O2 was given in 173 (82.8%) patients, Favipiravir in 59 (28.2%), Remdesivir in 111 (53.1%), Methylprednisolone in 87 (41.6%), Dexamethasone in 93 (44.5%), Antibiotics in 204 (97.60%), Toccilizumab in 34 (16.3%), plasma in 18 (8.6%), and low molecular weight heparin (LMWH) in 200 (95.7%) patients. Regarding outcome of the COVID patients admitted, 85 (92.4%) patients improved, 6 (6.5%) died who were RT-PCR positive and 107 (91.15%) improved, 9 (7.7%) died who were probable cases. Total death rate was 7.1%. Conclusion: The present study findings were some early activities among COVID patients in the years 2020. Male were more affected and middle age group people were the most victims.

Cloud computing is currently emerging as an ever-changing, growing paradigm that models “everything-as-a-service.” Virtualised physical resources, infrastructure, and applications are supplied by service provisioning in the cloud. The evolution in the adoption of cloud computing is driven by clear and distinct promising features for both cloud users and cloud providers. However, the increasing number of cloud providers and the variety of service offerings have made it difficult for the customers to choose the best services. By employing successful service provisioning, the essential services required by customers, such as agility and availability, pricing, security and trust, and user metrics can be guaranteed by service provisioning. Hence, continuous service provisioning that satisfies the user requirements is a mandatory feature for the cloud user and vitally important in cloud computing service offerings. Therefore, we aim to review the state-of-the-art service provisioning objectives, essential services, topologies, user requirements, necessary metrics, and pricing mechanisms. We synthesize and summarize different provision techniques, approaches, and models through a comprehensive literature review. A thematic taxonomy of cloud service provisioning is presented after the systematic review. Finally, future research directions and open research issues are identified.

Six years after ratifying the UN Convention on the Rights of Persons with Disabilities, Bangladesh enacted the Rights and Protection of Persons with Disabilities Act, 2013, which recognized enforceable rights for Bangladeshis with disabilities for the first time and created a novel complaints procedure for seeking remedies outside of court. Yet little had been done to raise awareness of the Rights and Protection of Persons with Disabilities Act's rights or its novel procedure, until a collaboration between BlueLaw International, an inclusive development firm, and three Bangladeshi organizations with national scope to empower local disabled peoples' organizations to advocate for implementation of the law in 7 of Bangladesh's 64 districts. After three years, this team had filed the first 22 complaints under the novel Section 36 procedure before district-level committees created by the Rights and Protection of Persons with Disabilities Act. Although to date the outcomes of these complaints have been mixed, disabled peoples' organization efforts to activate this mechanism have yielded valuable lessons regarding effective advocacy strategies that can inform future disabled peoples' organization-led efforts. Although prospects for tangible governmental buy-in for effective, countrywide enforcement of the Rights and Protection of Persons with Disabilities Act may be dim for now, disabled peoples' organizations have demonstrated their capacity to contribute meaningfully to operationalizing this important vehicle for domestic implementation of the UN Convention on the Rights of Persons with Disabilities. [ABSTRACT FROM AUTHOR]

Cloud computing is currently emerging as an ever-changing, growing paradigm that models "everything-as-a-service." Virtualised physical resources, infrastructure, and applications are supplied by service provisioning in the cloud. The evolution in the adoption of cloud computing is driven by clear and distinct promising features for both cloud users and cloud providers. However, the increasing number of cloud providers and the variety of service offerings have made it difficult for the customers to choose the best services. By employing successful service provisioning, the essential services required by customers, such as agility and availability, pricing, security and trust, and user metrics can be guaranteed by service provisioning.Hence, continuous service provisioning that satisfies the user requirements is amandatory feature for the cloud user and vitally important in cloud computing service offerings. Therefore, we aim to review the state-of-the-art service provisioning objectives, essential services, topologies, user requirements, necessary metrics, and pricing mechanisms.We synthesize and summarize different provision techniques, approaches, and models through a comprehensive literature review. A thematic taxonomy of cloud service provisioning is presented after the systematic review. Finally, future research directions and open research issues are identified. [ABSTRACT FROM AUTHOR]