Farm Inspection Research Articles

Association between perinatal mortality and the management of cows before and at calving in large dairy herds

Stillbirths account for a relevant proportion of total calf mortality on dairy farms. In addition, the stillbirth rate is an important criterion for assessing animal welfare. The aim of this study was to determine the relationship between the stillbirth rate and aspects concerning the management of cows ante partum and during the calving period in larger dairy herds. In 97 dairy farms with an average herd size of 550 cattle (≥24 months old), data concerning farm management and animal health were collected as part of a farm inspection using a questionnaire. The stillbirth rate of the respective farms was determined based on the farm's own data on the milk yield test and analyzed for possible correlations with farm management using a multivariable factor analysis. A higher stillbirth rate was found on farms with particularly lean and overconditioned transit cows in the herd. A positive association to the stillbirth rate was also found for farms using a special birth monitoring system for heifers and those with more than 20% assisted calvings. There was no association with herd size. Regardless of herd size, factors that are related to the stillbirth rate may be determined by analyzing the farm management. The body condition of the cows is a key parameter. The introduction of special birth monitoring for heifers can probably be interpreted as a reaction to increased stillbirths in the herd. Conditioning cows before calving is an important field of action for herd managers in order to avoid stillbirths. The cows should not approach the calving period in an overly lean or over-conditioned state. When in such state, however, it is advisable for these cows to be monitored more closely in order to allow for professional assistance at the pertinent time period. Integration of body condition scoring of cows at dry-off, calving and the end of the high-yielding period, assessment of calving conditions and training of staff involved in obstetrics into veterinary herd management is warranted.

An efficient zero-labeling segmentation approach for pest monitoring on smartphone-based images

Timely and precise farm inspection, which involves the identification and recognition of harmful insects and diseases, is crucial for safeguarding crop production. Traditional vision-based pest recognition methods typically require extensive annotated data for each pest species and a lengthy training process. This approach is time-consuming, labor-intensive, and prone to human error. Zero-shot learning offers a potential solution by enabling pest segmentation and control without requiring explicit training data. This study supports farmers in automatically identifying ten common pests and their precise locations in real-world outdoor environments. The zero-shot pest segmentation is based on a hybrid approach combining Explainable Contrastive Language-Image Pre-training (ECLIP) and Segment-Anything (SAM). Moreover, an optimized super-resolution model and various data augmentation methods are implemented to improve the quality of the dataset. Lastly, a mask post-processing step is applied to remove highly overlapping segmented masks and noise blobs caused by the complex background. The mean Intersection over Union (mIoU) of 66.5 % on the validation set demonstrates the potential of zero-shot methods for automated pest segmentation during farm inspections. This research lays the foundation for accurate pest monitoring systems capable of adapting to new pests, ultimately improving agricultural productivity.

Optimization of offshore wind farm inspection paths based on K-means-GA.

As global demand for offshore wind energy continues to rise, the imperative to enhance the profitability of wind power projects and reduce their operational costs becomes increasingly urgent. This study proposes an innovative approach to optimize the inspection routes of offshore wind farms, which integrates the K-means clustering algorithm and genetic algorithm (GA). In this paper, the inspection route planning problem is formulated as a multiple traveling salesman problem (mTSP), and the advantages of the K-means clustering algorithm in distance similarity are utilized to effectively group the positions of wind turbines, thereby optimizing the inspection schedule for vessels. Subsequently, by harnessing the powerful optimization capability and robustness of genetic algorithms, further refinement is conducted to search for the optimal inspection routes, aiming to achieve cost reduction objectives. The results of simulation experiments demonstrate the effectiveness of this integrated approach. Compared to traditional genetic algorithms, the inspection route length has been significantly reduced, from 93 kilometers to 79.36 kilometers. Simultaneously, operational costs have also experienced a notable decrease, dropping from 141,500 Chinese Yuan to 125,600 Chinese Yuan.

Unmanned aerial vehicle–human collaboration route planning for intelligent infrastructure inspection

AbstractMotivated by the strengths of unmanned aerial vehicle (UAV), the UAV–human collaboration route planning (UHCRP) for intelligent infrastructure inspection is a problem worthy of discussion to help reduce human costs and minimize the risk of noninspected infrastructures under limited resources. To facilitate UHCRP, this paper proposes a novel deep reinforcement learning (DRL)‐based approach to well handle multi‐source uncertain features and constraints at a fast speed. To begin with, UHCRP is mathematically described and reformulated as a dual interdependent deep reinforcement learning (diDRL) framework to reflect real‐world scenarios. Afterward, a novel policy network named the attention‐based deep neural network (A‐DNN) is introduced to learn the route planning decisions for the combinatorial optimization problem. In particular, A‐DNN is made up of an encoder and a dual decoder for UAV and human inspection, where the multi‐head attention mechanism is incorporated to generate richer representations for model performance improvement. Performance of the proposed dual multi‐head attention model (DAM) has been tested in simulations and a real‐world case study regarding wind farm inspection. Results indicate that DAM under the sampling decoding strategy can deliver a high‐quality path plan and show better generalizability for larger scale problem sizes compared to single‐head attention model (SAM), multi‐head attention model (AM), and two baseline models, namely OR‐Tools and genetic algorithm. Moreover, DAM trained by randomly generated data can be directly employed to solve the practical problem with standardization of inputs. Overall, DRL integrates decision‐making for inspection method selection and inspected infrastructure selection, providing adaptive and intelligent inspection path planning for UAV and human in complex and dynamic engineering environments.

"IMPACT OF INFORMATION TECHNOLOGY ON BANKING BUSINESS, AN ANALYTICAL STUDY"

Due to the Information Technology known in Banking business, mainly using internet, extranet and intranet etc. in Banking, through electronics, Banking and their customers complete all their transactions using computer and do all the records of paperless transaction in computer.
 All the computers in the country or the world are connected by internet so any person in the country can contact the bank from any location, pay, withdraw, transfer money, tasks are completed on time, there is more transparency in the transaction, valuable time of customers is not wasted, employee on Work stress is reduced, search farm inspection is faster, less staff and more profit etc.
 Information technology has made the banking process faster and more reliable than ever before and the maintenance and retrieval of documents and records has become faster and easier. Technology creates competition in competitive industries and provides better customer service. That is, information technology has had a positive effect on the lucrative business.
 Overall, information technology has made the financial services more convenient, efficient and personalized and has led to significant growth and progress in the banking industry.

Real-Time Object Detection for Autonomous Solar Farm Inspection via UAVs

Robotic missions for solar farm inspection demand agile and precise object detection strategies. This paper introduces an innovative keypoint-based object detection framework specifically designed for real-time solar farm inspections with UAVs. Moving away from conventional bounding box or segmentation methods, our technique focuses on detecting the vertices of solar panels, which provides a richer granularity than traditional approaches. Drawing inspiration from CenterNet, our architecture is optimized for embedded platforms like the NVIDIA AGX Jetson Orin, achieving close to 60 FPS at a resolution of 1024 ×1376 pixels, thus outperforming the camera’s operational frequency. Such a real-time capability is essential for efficient robotic operations in time-critical industrial asset inspection environments. The design of our model emphasizes reduced computational demand, positioning it as a practical solution for real-world deployment. Additionally, the integration of active learning strategies promises a considerable reduction in annotation efforts and strengthens the model’s operational feasibility. In summary, our research emphasizes the advantages of keypoint-based object detection, offering a practical and effective approach for real-time solar farm inspections with UAVs.

A Chaotic Simulated Annealing Genetic Algorithm with Asymmetric Time for Offshore Wind Farm Inspection Path Planning

A Chaotic Simulated Annealing Genetic Algorithm with Asymmetric Time for Offshore Wind Farm Inspection Path Planning

An Assessment of The Risk Factors Associated with Subclinical Mastitis in Small Holder Dairy Cows and Large Mechanized Dairy Cows in Parts of Kaduna State, Nigeria

It appears that small holder dairy farmers as well as large mechanized dairy farmers in Kaduna State do not seem to be aware of the subclinical mastitis situation in their cows. A cross-sectional study was carried out with the objective of establishing the prevalence of subclinical mastitis and related risk indicators and to assess their contribution to the occurrence of subclinical mastitis. Three field procedures based on the principles of herd health and production management were followed: clinical inspection, farm management inspection and farm records inspection. The California mastitis test (CMT) was carried out on quarter milk samples to determine the prevalence of subclinical mastitis. A total of 150 lactating cows from 30 herds were investigated. Clinical mastitis was detected in about 5.0% of the lactating cows, subclinical mastitis was found in 24.5% (about 25%) of the cows screened. Farm inspection revealed that, water scarcity, poor housing, hand milking, single udder towel and dairy labourers as most substantial (P < 0.05) risk indicators. Overall, there was the epidemiological need to address such risk indicators.

Smart insect monitoring based on YOLOV5 case study: Mediterranean fruit fly Ceratitis capitata and Peach fruit fly Bactrocera zonata

The agricultural sector in Egypt is adversely affected by factors such as inadequate soil fertility and environmental hazards such as pestilence and diseases. The implementation of early pest prediction techniques has the potential to enhance agricultural yield. Bactrocera zonata and Ceratitis capitata, known as peach fruit fly and Mediterranean fruit fly, respectively, are the predominant pests that cause significant damage to fruits on a global scale. The present study proposes a deep learning-based approach for the detection and quantification of pests. The proposed approach entails the retrieval of data pertaining to the adhesive trap condition, followed by its examination and presentation through a mobile application. The YOLOV5 model has been implemented for the purpose of pest classification, localization, and quantification. In order to address the issue of a restricted dataset, a hybrid technique of transfer learning and data augmentation (copy and paste) was employed. The proposed approach offers an intelligent real time pest detection, thereby facilitating the prediction of treatment options. An application for smartphones has been developed to aid farmers and agricultural professionals in the management and treatment of pests. The proposed approach has the potential to aid farmers in identifying the existence of pests, thereby diminishing the duration and resources needed for farm inspection. As per the results of the conducted experiments, the proposed approach demonstrates a noteworthy increase in performance. The weighted average accuracy reaches 84%, while precision (P), mean average precision (mAP), and F1-score show enhancements of up to 15%, 18%, and 7% respectively.

Study on a Boat-Assisted Drone Inspection Scheme for the Modern Large-Scale Offshore Wind Farm

This study proposes a novel and comprehensive boat-assisted drone inspection scheme for offshore wind farms. Specifically, we develop a unique wireless communication model, a detailed wind turbine blade detection model, and a route planning scheme for the drone and boat to formulate the minimum energy consumption problem for drone inspection. Then, we demonstrate that this problem is a combinatorial optimization mixed-integer nonlinear programming problem, which we solve by decomposing the optimization problem into four subproblems. Moreover, an improved heuristic algorithm is constructed to solve the optimum energy consumption problem, exploiting these subproblems. The simulation results indicate that the proposed scheme achieves optimal energy consumption for boat-assisted drone inspection. Furthermore, compared with the existing methods, the proposed algorithm obtains the lowest total energy consumption and provides a higher route planning performance for the unmanned aerial vehicle and boat when utilized for offshore wind farm inspection.

A System for Autonomous Seaweed Farm Inspection with an Underwater Robot.

This paper outlines challenges and opportunities in operating underwater robots (so-called AUVs) on a seaweed farm. The need is driven by an emerging aquaculture industry on the Swedish west coast where large-scale seaweed farms are being developed. In this paper, the operational challenges are described and key technologies in using autonomous systems as a core part of the operation are developed and demonstrated. The paper presents a system and methods for operating an AUV in the seaweed farm, including initial localization of the farm based on a prior estimate and dead-reckoning navigation, and the subsequent scanning of the entire farm. Critical data from sidescan sonars for algorithm development are collected from real environments at a test site in the ocean, and the results are demonstrated in a simulated seaweed farm setup.

Dairy Sheep Played a Minor Role in the 2005-2010 Human Q Fever Outbreak in The Netherlands Compared to Dairy Goats.

Q fever is an almost ubiquitous zoonosis caused by Coxiella burnetii. This organism infects several animal species, as well as humans, and domestic ruminants like cattle, sheep and goats are an important animal reservoir of C. burnetii. In 2007, a sudden rise in notified human Q fever cases occurred in The Netherlands, and by the end of 2009, more than 3500 human Q fever patients had been notified. Dairy sheep and dairy goats were suspected to play a causal role in this human Q fever outbreak, and several measures were taken, aiming at a reduction of C. burnetii shedding by infected small ruminants, in order to reduce environmental contamination and thus human exposure. One of the first measures was compulsory notification of more than five percent abortion within thirty days for dairy sheep and dairy goat farms, starting 12 June 2008. After notification, an official farm inspection took place, and laboratory investigations were performed aiming at ruling out or demonstrating a causal role of C. burnetii. These measures were effective, and the number of human Q fever cases decreased; levels are currently the same as they were prior to 2007. The effect of these measures was monitored using a bulk tank milk (BTM) PCR and an antibody ELISA. The percentage PCR positive dairy herds and flocks decreased over time, and dairy sheep flocks tested PCR positive significantly less often and became PCR negative earlier compared to dairy goat herds. Although there was no difference in the percentage of dairy goat and dairy sheep farms with a C. burnetii abortion outbreak, the total number of shedding dairy sheep was much lower than the number of shedding dairy goats. Combined with the fact that Q fever patients lived mainly in the proximity of infected dairy goat farms and that no Q fever patients could be linked directly to dairy sheep farms, although this may have happened in individual cases, we conclude that dairy sheep did not play a major role in the Dutch Q fever outbreak. BTM monitoring using both a PCR and an ELISA is essential to determine a potential C. burnetii risk, not only for The Netherlands but for other countries with small ruminant dairy industries.

Small-Scale Farmers' Preference in Adopting Geographical Indications’ Code of Practice to Produce Coffee in Indonesia: A Choice Experiment Study

Geographical Indication is a type of collective intellectual property that producers may use to capture the place-related value expressed in their product which involve the physical and anthropic characteristics of the production area. There is GI’s Code of Practice (CoP) that should be adopted by farmers as reference standard to maintain GIs product consistency and definition requirements. Understanding farmer preferences and incorporating them into the development of improvement of GIs’ certification schemes is thought to result in more internalized, and thus more efficient standards. This study aims to 1) investigate the small-scale farmers’ preferences and the influence of socio-economics characteristics rejecting GIs’ scheme; and 2) develop strategies to increase farmers participation on GIs’ scheme. Choice Experiment method and Conditional Logit Model (CLM) were used to estimate preferences of 157 small-scale coffee farmers for accepting GIs’ scheme. The findings suggest that premium price, coffee processing, technical assistance, and selling agreement are all GIs’ attributes that could increase small-scale farmers’ utility. More incentives are required to compensate farmers due to the loss of utility caused by pesticide bans and farm inspection. Based on CLM, the study found that farmers have clear preferences for and against certain aspects of GIs’ CoP scheme.

Energy Saving Quadrotor Control for Field Inspections

This article presents a control strategy to improve the energy efficiency of a quadrotor for field inspections. A power consumption analytical model for a quadrotor is constructed, which indicates that the power consumption of a quadrotor is correlated with the roll and pitch angles. Based on that, a cascaded bounded control framework is developed to regulate the roll and pitch angles within the predefined energy-efficient ranges, while a successful inspection trajectory tracking can be achieved simultaneously. Experimental studies are carried out to prove the effectiveness of the proposed method through a mock-up scenario of wind farm inspection. Compared to the conventional cascaded control framework, the proposed energy saving control strategy is capable of saving 5% energy for the same inspection assignment under wind disturbance.

Fully digital wind farm monitoring and inspection system based on AR augmented reality technology

The wind power is a vigorously developed clean energy in China recent years. It’s installed capacity of our country is increasing year by year. Since 2012, the new wind power installed capacity in China has became the first place all around the world. By the end of 2016, our wind power installed capacity has added a total of 87 million 580 thousand kilowatts. Besides the rapid growth of wind power, there are still many problems in China’s wind power business. In order to solve the following problems: the inspection environment of the wind farm equipment is quite complex, the efficiency is not high enough, and the statistical data of the inspection is not good enough, this paper puts forward the “full digital monitoring and inspection platform of wind farm based on AR augmented reality” as the solution of the problems. The project implements fan positioning, model-assisted inspection, process recording, and expert remote assistant APP functions. It promotes the development of wind farm detection and inspection, which can greatly improve the inspection efficiency of wind farms and reduce the workload of inspectors.

Robots to Carry Out Wind Farm Repair and Inspection

New wind turbine blade project could save an average farm £26m in its life

Study on wind turbine blade defect detection system based on imaging array

Currently in the process of wind farm inspection, wind turbine blade appearance inspection mainly adopts the telescope or high-definition cameras, low detection efficiency, labor intensity and the precision is limited, in order to solve this problem, a kind of wind turbine blades defect recognition system based on image array is proposed. Through the joint of array camera and image processing server, the functions of the image acquisition, processing, and defect recognition and detection results output are implemented. The software of artificial intelligence deep learning based on neural network algorithm is used to identify the defects of blade image, and output quality analysis report, to realize automatic detection of wind turbine blade surface defect. The field measurement results show that the system greatly improves the efficiency and accuracy of wind turbine blade defect detection.

Detection of Treponemapedis associated with digital dermatitis in Mediterranean buffalo (Bubalus bubalis)

Clinical outcomes compatible with digital dermatitis (DD) were observed in a second lactation, milking Mediterranean buffalo (MB). The animal was sharing the free stall with other 99 milking MBs separated by a fence from a pen harbouring 40 milking crossbreed dairy cows. The macroscopic lesion observed on the right rear limb was categorised as M4.1 stage and associated with a severe lameness (locomotion score 5). In the same farm, DD was also observed in 14/40 cows. Nested PCR analysis of genomic DNA isolated from the centre lesion’s biopsy showed the presence of Treponema pedis. Sequence analysis of 494 bp of the PCR fragment revealed 100 per cent sequence identity to the T pedis‐type strain, confirming the PCR result’s specificity and for the first time the presence of the bacterium in MB. In order to establish the spread of the DD, a farm inspection was performed to identify risk factors and preventive strategies.

Use of welfare outcome information in three types of dairy farm inspection reports.

ObjectiveThe aim of this study was to examine the use of outcome-based observations within Assured Dairy Farm scheme (ADF), Soil Association Organic Standards (SA), and cross compliance (CC) farm assessment reports.MethodsA total of 449 ADF reports, 37 SA reports and 26 CC reports were analyzed and their objective comments categorized as either resource-based or outcome-based.ResultsA mean of 61.0% of ADF questions were responded to with comments, in comparison to 25.0% of SA and, 21.0% of CC report questions. The SA and CC reports had significantly more outcome-based comments than the ADF (p<0.001). The assessors’ tendency of choosing resource-based approach was revealed in the questionnaire results.ConclusionGenerally, the comments were comprehensive and contained professional judgements. Large numbers of comments provided in the ADF reports were mostly compliant and resource-based evidence, which serves as proof of assessment rather than aiding the certifying process. The inclusion of specific welfare outcome measures in the SA inspection likely increased the use of outcome-based comments in the reports, irrespective of whether the farm achieved compliance with a given standards. The CC scheme, on the other hand, focused on providing outcome-based evidence to justify noncompliant decisions.

Comparison of National and International Standards of Good Egg Production Practices

Egg production is an important economic activity in Brazil, with about 697 million eggs produced annually. The conventional cage system is commonly used for egg production. However, there has been a growing concern for the welfare of laying hens around the world. In this context, many countries have issued laws, protocols, and other normative technical specifications to ensure the welfare of layers. This study aims at identifying similarities and differences between international standards and Brazilian protocols using the Comparative Law perspective. This article reports an analytical study of selected protocols, performing three analyses using the Comparative Law method. The research concludes that some items of the Brazilian protocols of good egg production practices, such as farm inspection, treatment of diseases, temperature, ventilation, beak trimming, feed and water supply, correspond to international specifications, whereas others, such as housing, freedom movement, use of equipment, and transport, are less strict.