Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production

Mining Safety and Sustainability I

Safety and sustainability are becoming ever bigger challenges for the mining industry with the increasing depth of mining. It is of great significance to reduce the disaster risk of mining accidents, enhance the safety of mining operations, and improve the efficiency and sustainability of development of mineral resource. This book provides a platform to present new research and recent advances in the safety and sustainability of mining. More specifically, Mining Safety and Sustainability presents recent theoretical and experimental studies with a focus on safety mining, green mining, intelligent mining and mines, sustainable development, risk management of mines, ecological restoration of mines, mining methods and technologies, and damage monitoring and prediction. It will be further helpful to provide theoretical support and technical support for guiding the normative, green, safe, and sustainable development of the mining industry

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production

Text detection and recognition in images and video sequences

Text characters embedded in images and video sequences represents a rich source of information for content-based indexing and retrieval applications. However, these text characters are difficult to be detected and recognized due to their various sizes, grayscale values and complex backgrounds. This thesis investigates methods for building an efficient application system for detecting and recognizing text of any grayscale values embedded in images and video sequences. Both empirical image processing methods and statistical machine learning and modeling approaches are studied in two sub-problems: text detection and text recognition. Applying machine learning methods for text detection encounters difficulties due to character size, grayscale variations and heavy computation cost. To overcome these problems, we propose a two-step localization/verification approach. The first step aims at quickly localizing candidate text lines, enabling the normalization of characters into a unique size. In the verification step, a trained support vector machine or multi-layer perceptrons is applied on background independent features to remove the false alarms. Text recognition, even from the detected text lines, remains a challenging problem due to the variety of fonts, colors, the presence of complex backgrounds and the short length of the text strings. Two schemes are investigated addressing the text recognition problem: bi-modal enhancement scheme and multi-modal segmentation scheme. In the bi-modal scheme, we propose a set of filters to enhance the contrast of black and white characters and produce a better binarization before recognition. For more general cases, the text recognition is addressed by a text segmentation step followed by a traditional optical character recognition (OCR) algorithm within a multi-hypotheses framework. In the segmentation step, we model the distribution of grayscale values of pixels using a Gaussian mixture model or a Markov Random Field. The resulting multiple segmentation hypotheses are post-processed by a connected component analysis and a grayscale consistency constraint algorithm. Finally, they are processed by an OCR software. A selection algorithm based on language modeling and OCR statistics chooses the text result from all the produced text strings. Additionally, methods for using temporal information of video text are investigated. A Monte Carlo video text segmentation method is proposed for adapting the segmentation parameters along temporal text frames. Furthermore, a ROVER (Recognizer Output Voting Error Reduction) algorithm is studied for improving the final recognition text string by voting the characters through temporal frames

Volume II: Mining Innovation

Contemporary exploitation of natural raw materials by borehole, opencast, underground, seabed, and anthropogenic deposits is closely related to, among others, geomechanics, automation, computer science, and numerical methods. More and more often, individual fields of science coexist and complement each other, contributing to lowering exploitation costs, increasing production, and reduction of the time needed to prepare and exploit the deposit. The continuous development of national economies is related to the increasing demand for energy, metal, rock, and chemical resources. Very often, exploitation is carried out in complex geological and mining conditions, which are accompanied by natural hazards such as rock bursts, methane, coal dust explosion, spontaneous combustion, water, gas, and temperature. In order to conduct a safe and economically justified operation, modern construction materials are being used more and more often in mining to support excavations, both under static and dynamic loads. The individual production stages are supported by specialized computer programs for cutting the deposit as well as for modeling the behavior of the rock mass after excavation in it. Currently, the automation and monitoring of the mining works play a very important role, which will significantly contribute to the improvement of safety conditions. In this Special Issue of Energies, we focus on innovative laboratory, numerical, and industrial research that has a positive impact on the development of safety and exploitation in mining

Investigating the dynamic response of rock mass to reservoir drainage at Grimsel test site, Switzerland, as an analogue for glacial retreat

An effective solution for the geologic disposal of nuclear waste, with no environmental risk (i.e. avoidance of harmful release of radioactive material), is a fundamental issue for the environment protection, and for the future continued reliance on nuclear power. Although geological disposal is considered as the best option, there are still elements of risk to be addressed, such as glacial retreat, which could impact the safety performance of a geological disposal facility. In this project two consecutive annual cycles of a reservoir in the Swiss Alps are used as a small scale analogue of the glacial retreat cycles, in order to investigate the response of granitic rock (as a host rock to a geologic disposal facility) to significant load changes. Assuming that the reservoir’s stress changes cause the fractured and weakened rock slopes to slip, I chose to use microseismic monitoring as a tool to monitor the reservoir induced seismicity. A seismic network was deployed in the tunnels adjacent to the reservoir and recorded continuously ground movement over a 3.5-year period (Nov 2014 – Aug 2018). In order to be able to detect microseismic slips in the acquired real field dataset I explore various algorithms from the literature and develop my own methodology. The two main problems my research focuses on are the length of the dataset (big data issues) and the signal to noise ratio of the events I want to detect (small magnitude events in a varying noisy background). My results show, albeit not all of the seismic signals were possible to locate or characterise, that the reservoir unloading increases the frequency of occurrence of microseismic events for a short time period in the region surrounding the reservoir. It is possible therefore that the construction of a geologic disposal facility will have a similar effect. However, the magnitudes of the induced events are very small and hence unlikely to have a significant effect as part of a safety case for a geologic disposal facility. The contributions of this thesis can be summarised to: (i) using a reservoir as a small-scale test site analogue for exploring the seismic hazard in radioactive deep geologic disposal facilities due to glacial retreat; (ii) sensor deployment design and sensor data cleaning with noise characterisation for microseismic monitoring over several years; (iii) proposal of a new algorithm (NpD) for detecting potential seismic signals under not well-constrained conditions and without requirement of a priori knowledge about the expected signal frequencies and amplitudes; (iv) the NpD detection algorithm and acquired 3.5 years dataset are made freely available; (v) detailed discussion of onset time picking and hypocentre localisation methodologies, where again novelty lies in using, comparing suitability and adjusting a number of well-known approaches for the purposes of my project; (vi) compilation of a seismic catalogue related to the dynamic response of the rock mass to reservoir drainage.An effective solution for the geologic disposal of nuclear waste, with no environmental risk (i.e. avoidance of harmful release of radioactive material), is a fundamental issue for the environment protection, and for the future continued reliance on nuclear power. Although geological disposal is considered as the best option, there are still elements of risk to be addressed, such as glacial retreat, which could impact the safety performance of a geological disposal facility. In this project two consecutive annual cycles of a reservoir in the Swiss Alps are used as a small scale analogue of the glacial retreat cycles, in order to investigate the response of granitic rock (as a host rock to a geologic disposal facility) to significant load changes. Assuming that the reservoir’s stress changes cause the fractured and weakened rock slopes to slip, I chose to use microseismic monitoring as a tool to monitor the reservoir induced seismicity. A seismic network was deployed in the tunnels adjacent to the reservoir and recorded continuously ground movement over a 3.5-year period (Nov 2014 – Aug 2018). In order to be able to detect microseismic slips in the acquired real field dataset I explore various algorithms from the literature and develop my own methodology. The two main problems my research focuses on are the length of the dataset (big data issues) and the signal to noise ratio of the events I want to detect (small magnitude events in a varying noisy background). My results show, albeit not all of the seismic signals were possible to locate or characterise, that the reservoir unloading increases the frequency of occurrence of microseismic events for a short time period in the region surrounding the reservoir. It is possible therefore that the construction of a geologic disposal facility will have a similar effect. However, the magnitudes of the induced events are very small and hence unlikely to have a significant effect as part of a safety case for a geologic disposal facility. The contributions of this thesis can be summarised to: (i) using a reservoir as a small-scale test site analogue for exploring the seismic hazard in radioactive deep geologic disposal facilities due to glacial retreat; (ii) sensor deployment design and sensor data cleaning with noise characterisation for microseismic monitoring over several years; (iii) proposal of a new algorithm (NpD) for detecting potential seismic signals under not well-constrained conditions and without requirement of a priori knowledge about the expected signal frequencies and amplitudes; (iv) the NpD detection algorithm and acquired 3.5 years dataset are made freely available; (v) detailed discussion of onset time picking and hypocentre localisation methodologies, where again novelty lies in using, comparing suitability and adjusting a number of well-known approaches for the purposes of my project; (vi) compilation of a seismic catalogue related to the dynamic response of the rock mass to reservoir drainage

Plant classification combining colour and spectral cameras for weed control purposes

This study was conducted to design and evaluate a novel dual camera sensor for use in an accurate single leaf level plant detection and classification system for weed control purposes. The system was to utilise and combine the benefits of colour and spectral imaging technologies together with novel data processing techniques. Such combination of colour and spectral imaging devices has not been previously used in precision agriculture. Environmental consciousness and requirements for production volumes of organic produce are constantly increasing. Reductions or total elimination of chemical spraying is needed, and a technological solution of automating the weed control has been seen as one solution to solve the limitations in current crop production methods. Recent studies have shown automatic plant detection and classification to be the only economically viable solution for the problem of automatic weed control. Previous detection systems have shown adequate capabilities to detect and classify weeds and crop plants with certain limitations. Depending on the system, these limitations have been in spatial accuracy, operation in certain lighting conditions or selection of plants to be classified. A flexible system capable of robust plant classification under any circumstances and plant combinations has not yet been realised. It would be desirable to introduce a system capable of detecting any plant species and plants separately, thus allowing targeted and optimal weed control methods for each plant species. The proposed system addressed the problem of automatic plant detection and classification by providing sub-centimetre level information on plant part locations separately for each plant species. This information could then be directly used to guide mechanical weeding tools or precision sprayers. The detection system was based on a novel combination of a sub-millimetre level colour camera and an accurate hyperspectral line scanning camera (spectrometer) in the spectral range of 400 – 1000 nm. The spatial accuracy of the spectrometer was approximately five times lower than that of the colour camera. The system operated under controlled lighting conditions. The colour camera allowed precise segmentation of plant borders, while the spectral camera produced detailed reflectance information to discriminate between plant types. The system was able to collect data for classification from an area on a plant of approximately 6.5 mm by 6.5 mm, although typically areas as small as 3.5 mm by 3.5 mm were detected. These were also the spatial resolution boundaries of the system with the used test settings. The system was first designed and evaluated in laboratory conditions using controlled lighting and a selection of leaves from 6 plants. Data collection and analysis methods were designed for a scanning system with simultaneous image acquisition from both cameras. Shape, colour and spectral reflectance information were used to correctly classify these individual leaves with a probability of up to 98% using linear stepwise discriminant analysis. A method of classifying separate leaves is not robust in a real field environment where plant leaves are often overlapping. This makes the use of shape calculations difficult. A novel method of extracting data from the small windows was proposed. Colour and spectral data within these windows was classified separately and the windows formed a grid like structure with approximately 3.5 mm spacing between them. This allowed spatial filtering of the classification data and noise reduction for the results by utilising information in a 3 by 3 window neighbourhood around each data window. During laboratory tests the windows for 6 plant leaves were correctly classified at 97.8% when the leaves were separated, and at 85.2% with overlapping leaves. The system operation was also evaluated in real field conditions. Four crop plants and 16 weed plant types were imaged on a field over a period of 11 to 25 days after sowing. Total average classification performance in field conditions with linear discriminant analysis was up to 85.1%, while classification results investigated as a two-class case of crop vs. weed plants was up to 99.5% and 83.8%, respectively. The spatial filtering method was shown to improve results on average by 7.5%. Plant reflectance measurements on different days allowed a novel analysis of short term temporal changes in the plant spectra due to growing conditions and growth stages. Analysis on short term spectral changes and their effects on classification accuracies have not been found in the literature. The temporal analysis showed that the average spectra of any plant type changes considerably over a period of just few days, and has a trend like behaviour when investigated at individual wavelengths. Classification models with training set data from previous days did not perform well. This indicates the need to have an up to date training set available at all times explaining the subtleties in local conditions. The proposed detection and classification system with intelligent data processing methods has been shown to perform at a comparable level with previous systems. The novel system does not suffer from the typical limitations of previous systems, and is flexible to be used with any plant types in their early growth stages. There is also potential to include plant height, shape or any other relevant feature to the classification for increased robustness. The presented data processing method allows considerable processing and data reductions within the camera hardware. Only small fractions of the processed image data would need to be transferred via the camera interface. This would compensate for the increased data flow created by using two cameras. Therefore, the real-time implementation of the system is thought possible with the right hardware choices and optimised data processing algorithms.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Disruptive Technologies in Agricultural Operations: A Systematic Review of AI-driven AgriTech Research

YesThe evolving field of disruptive technologies has recently gained significant interest in various industries, including agriculture. The fourth industrial revolution has reshaped the context of Agricultural Technology (AgriTech) with applications of Artificial Intelligence (AI) and a strong focus on data-driven analytical techniques. Motivated by the advances in AgriTech for agrarian operations, the study presents a state-of-the-art review of the research advances which are, evolving in a fast pace over the last decades (due to the disruptive potential of the technological context). Following a systematic literature approach, we develop a categorisation of the various types of AgriTech, as well as the associated AI-driven techniques which form the continuously shifting definition of AgriTech. The contribution primarily draws on the conceptualisation and awareness about AI-driven AgriTech context relevant to the agricultural operations for smart, efficient, and sustainable farming. The study provides a single normative reference for the definition, context and future directions of the field for further research towards the operational context of AgriTech. Our findings indicate that AgriTech research and the disruptive potential of AI in the agricultural sector are still in infancy in Operations Research. Through the systematic review, we also intend to inform a wide range of agricultural stakeholders (farmers, agripreneurs, scholars and practitioners) and to provide research agenda for a growing field with multiple potentialities for the future of the agricultural operations