Sustainable Agriculture Practice using Machine Learning

The changing climate has caused unpredictable rainfall, unusual temperature drops, and heat waves, leading to considerable damage to the environment. Fortunately Machine Learning has provided effective tools to address global issues, including agriculture. By employing different ML algorithms, it is possible to solve the agricultural problems caused by these climate changes. The objective of this article is to develop a system for crop recommendation and disease detection in a plant. Publicly available datasets were used for both tasks. For the crop recommendation system, feature extraction was performed, and the dataset was trained using various Machine Learning algorithms, namely Decision Tree, Logistic Regression, Random Forest, Support Vector Machine (SVM) and Multilayer Perceptron. The random forest algorithm achieved an excellent accuracy of 99.31%.For the plant disease identification system, CNN architectures like - VGG16, ResNet50, and EfficientNetV2 - were trained and compared. Among these, EfficientNetV2 achieved high accuracy of 96.07%

Classification of water stress in cultured Sunagoke moss using deep learning

Water stress greatly determines plant yield as it affects plant metabolism, photosynthesis rate, chlorophyll content index, number of leaves, physiological, biochemical compound, and vegetative growth. The research aimed to detect and classify water stress of cultured Sunagoke moss into several categories i.e. dry, semi-dry, wet, and soak by using a low-cost commercial visible light camera combined with a deep learning model. Cultured Sunagoke moss is a commercial product which has the potential use as rooftop-greening and wall-greening material. This research compared the performance of four convolutional neural network models, such as SqueezeNet, GoogLeNet, ResNet50, and AlexNet. The best convolutional neural network model according to the training and validation result was ResNet50 with RMSProp optimizer, 30 epoch, and 128 mini-batch size; this also gained an accuracy rate at 87.50%. However, the best result of the convolutional neural network model on data testing using confusion matrices on different data sample was ResNet50 with Adam optimizer, 30 epoch, 128 mini-batch size, and average testing accuracy of 94.15%. It can be concluded that based on the overall results, convolutional neural network model seems promising as a smart irrigation system that real-time, non-destructive, rapid, and precise method when controlling water stress of plants

Agricultural Crop Recommendation, Crop Disease Detection and Price Prediction Using Machine Learning

India's foundation is its agriculture. With over 60% of the workforce employed and producing over 18% of the nation's GDP, it is a vital sector of the Indian economy. Although there are many ways in which we can use technology to increase product production, a farmer can only profit if he is able to sell his crops. Three laws have been passed by the Indian government to encourage the export of agricultural products across the nation. But today, we witness farmers all over the nation fighting against these regulations to protect their rights. Farmers worry that big merchants will exploit them as puppets and undercut the price at which they sell their goods. After doing a thorough analysis of the situation, we developed the concept of creating an agricultural produce application that facilitates direct communication between farmers and retailers, allows for product reviews and crop yielding rate prediction, and predicts the price of agricultural produce based on quantity produced and previous years' sales rates. Unpredictable rains, unexpected temperature decreases, and heat waves have all been brought on by the shifting climate, and the ecosystem has suffered significant harm. Thankfully, machine learning has produced useful methods for tackling international problems, such as agriculture. These climate change-related agricultural issues can be resolved by using various machine learning methods. The purpose of this piece is to Create a method to identify crop diseases and suggest crops. For both objectives, publicly accessible datasets were utilized. Regarding the crop recommendation system, feature extraction was done, and a variety of machine learning methods were used to train the dataset, including Support Vector Machine (SVM), Random Forest, Decision Tree, Logistic Regression, and Multilayer Perceptron. 99.30% accuracy was attained via the random forest algorithm.CNN architectures such as ResNet50, and EfficientNetV2 were trained and compared for the plant disease identification system. EfficientNetV2 outperformed the rest, with a high accuracy of 96.08%

Proceedings of Abstracts, School of Physics, Engineering and Computer Science Research Conference 2022

© 2022 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Plenary by Prof. Timothy Foat, ‘Indoor dispersion at Dstl and its recent application to COVID-19 transmission’ is © Crown copyright (2022), Dstl. This material is licensed under the terms of the Open Government Licence except where otherwise stated. To view this licence, visit http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3 or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: [email protected] present proceedings record the abstracts submitted and accepted for presentation at SPECS 2022, the second edition of the School of Physics, Engineering and Computer Science Research Conference that took place online, the 12th April 2022

Machine learning for the sustainable energy transition: a data-driven perspective along the value chain from manufacturing to energy conversion

According to the special report Global Warming of 1.5 °C of the IPCC, climate action is not only necessary but more than ever urgent. The world is witnessing rising sea levels, heat waves, events of flooding, droughts, and desertification resulting in the loss of lives and damage to livelihoods, especially in countries of the Global South. To mitigate climate change and commit to the Paris agreement, it is of the uttermost importance to reduce greenhouse gas emissions coming from the most emitting sector, namely the energy sector. To this end, large-scale penetration of renewable energy systems into the energy market is crucial for the energy transition toward a sustainable future by replacing fossil fuels and improving access to energy with socio-economic benefits. With the advent of Industry 4.0, Internet of Things technologies have been increasingly applied to the energy sector introducing the concept of smart grid or, more in general, Internet of Energy. These paradigms are steering the energy sector towards more efficient, reliable, flexible, resilient, safe, and sustainable solutions with huge environmental and social potential benefits. To realize these concepts, new information technologies are required, and among the most promising possibilities are Artificial Intelligence and Machine Learning which in many countries have already revolutionized the energy industry. This thesis presents different Machine Learning algorithms and methods for the implementation of new strategies to make renewable energy systems more efficient and reliable. It presents various learning algorithms, highlighting their advantages and limits, and evaluating their application for different tasks in the energy context. In addition, different techniques are presented for the preprocessing and cleaning of time series, nowadays collected by sensor networks mounted on every renewable energy system. With the possibility to install large numbers of sensors that collect vast amounts of time series, it is vital to detect and remove irrelevant, redundant, or noisy features, and alleviate the curse of dimensionality, thus improving the interpretability of predictive models, speeding up their learning process, and enhancing their generalization properties. Therefore, this thesis discussed the importance of dimensionality reduction in sensor networks mounted on renewable energy systems and, to this end, presents two novel unsupervised algorithms. The first approach maps time series in the network domain through visibility graphs and uses a community detection algorithm to identify clusters of similar time series and select representative parameters. This method can group both homogeneous and heterogeneous physical parameters, even when related to different functional areas of a system. The second approach proposes the Combined Predictive Power Score, a method for feature selection with a multivariate formulation that explores multiple sub-sets of expanding variables and identifies the combination of features with the highest predictive power over specified target variables. This method proposes a selection algorithm for the optimal combination of variables that converges to the smallest set of predictors with the highest predictive power. Once the combination of variables is identified, the most relevant parameters in a sensor network can be selected to perform dimensionality reduction. Data-driven methods open the possibility to support strategic decision-making, resulting in a reduction of Operation & Maintenance costs, machine faults, repair stops, and spare parts inventory size. Therefore, this thesis presents two approaches in the context of predictive maintenance to improve the lifetime and efficiency of the equipment, based on anomaly detection algorithms. The first approach proposes an anomaly detection model based on Principal Component Analysis that is robust to false alarms, can isolate anomalous conditions, and can anticipate equipment failures. The second approach has at its core a neural architecture, namely a Graph Convolutional Autoencoder, which models the sensor network as a dynamical functional graph by simultaneously considering the information content of individual sensor measurements (graph node features) and the nonlinear correlations existing between all pairs of sensors (graph edges). The proposed neural architecture can capture hidden anomalies even when the turbine continues to deliver the power requested by the grid and can anticipate equipment failures. Since the model is unsupervised and completely data-driven, this approach can be applied to any wind turbine equipped with a SCADA system. When it comes to renewable energies, the unschedulable uncertainty due to their intermittent nature represents an obstacle to the reliability and stability of energy grids, especially when dealing with large-scale integration. Nevertheless, these challenges can be alleviated if the natural sources or the power output of renewable energy systems can be forecasted accurately, allowing power system operators to plan optimal power management strategies to balance the dispatch between intermittent power generations and the load demand. To this end, this thesis proposes a multi-modal spatio-temporal neural network for multi-horizon wind power forecasting. In particular, the model combines high-resolution Numerical Weather Prediction forecast maps with turbine-level SCADA data and explores how meteorological variables on different spatial scales together with the turbines' internal operating conditions impact wind power forecasts. The world is undergoing a third energy transition with the main goal to tackle global climate change through decarbonization of the energy supply and consumption patterns. This is not only possible thanks to global cooperation and agreements between parties, power generation systems advancements, and Internet of Things and Artificial Intelligence technologies but also necessary to prevent the severe and irreversible consequences of climate change that are threatening life on the planet as we know it. This thesis is intended as a reference for researchers that want to contribute to the sustainable energy transition and are approaching the field of Artificial Intelligence in the context of renewable energy systems

Stress Analysis of Operating Gas Pipeline Installed by Horizontal Directional Drilling and Pullback Force Prediction During Installation

With the development of the natural gas industry, the demand for pipeline construction has also increased. In the context of advocating green construction, horizontal directional drilling (HDD), as one of the most widely utilized trenchless methods for pipeline installation, has received extensive attention in industry and academia in recent years. The safety of natural gas pipeline is very important in the process of construction and operation. It is necessary to conduct in-depth study on the safety of the pipeline installed by HDD method. In this dissertation, motivated by the following considerations, two aspects of HDD installation are studied. First, through the literature review, one issue that has not received much attention so far is the presence of stress problem during the operation condition. Thus, two chapters (Chapters 3 and 4) in this dissertation are related to the pipe stress analysis during the operation. Regarding this problem, two cases are considered according to the fluidity of drilling fluid. The more dangerous situation is determined by comparing the pipeline stress in the two working conditions. The stress of pipeline installed by HDD method and open-cut method is also compared, and it indicates that the stress of pipeline installed by HDD method is lower. Moreover, through the analysis of influence factors and stress sensitivity, the influence degree of different parameters on pipeline stress is obtained. Secondly, literature review indicates that the accurate prediction of pullback force in HDD construction is of great significance to construction safety and construction success. However, the accuracy of current analytical methods is not high. In the context of machine learning and big data, three new hybrid data-driven models are proposed in this dissertation (Chapter 5) for near real-time pullback force prediction, including radial basis function neural network with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN-RBFNN), support vector machine using whale optimization algorithm with CEEMDAN (CEEMDAN-WOA-SVM), and a hybrid model combines random forest (RF) and CEEMDAN. Three novel models have been verified in two projects in China. It is found that the prediction accuracy is dramatically improved compared with the original analytical models (or empirical models). In addition, through the feasibility analysis, the great potential of machine learning model in near real-time prediction is proved. At the end of this dissertation, in addition to summarizing the primary conclusions, three future research directions are also pointed out: (1) stress analysis of pipelines installed by HDD in more complex situations; (2) stress analysis of pipeline during HDD construction; (3) database establishment in HDD engineering

Stress analysis of operating gas pipeline installed by horizontal directional drilling and pullback force prediction during installation

With the development of the natural gas industry, the demand for pipeline construction has also increased. In the context of advocating green construction, horizontal directional drilling (HDD), as one of the most widely utilized trenchless methods for pipeline installation, has received extensive attention in industry and academia in recent years. The safety of natural gas pipeline is very important in the process of construction and operation. It is necessary to conduct in-depth study on the safety of the pipeline installed by HDD method. In this dissertation, motivated by the following considerations, two aspects of HDD are studied. First, through the literature review, one issue that has not received much attention so far is the presence of stress problem during the operation condition. Thus, two chapters (Chapters 3 and 4) in this dissertation are related to the pipe stress problem during the operation. Regarding this problem, two cases are considered according to the fluidity of drilling fluid. The more dangerous situation is determined by comparing the pipeline stress in the two working conditions. The stress of pipeline installed by HDD method and open-cut method is compared, and it indicates that the stress of pipeline installed by HDD method is lower. Moreover, through the analysis of influence factors and stress sensitivity, the influence degree of different parameters on pipeline stress is obtained. Secondly, literature review indicates that the accurate prediction of pullback force in HDD construction is of great significance to construction safety and construction success. However, the accuracy of current analytical methods is not high. In the context of machine learning and big data, three new hybrid data-driven models are proposed in this dissertation (Chapter 5) for near real-time pullback force prediction, including radial basis function neural network with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN-RBFNN), and support vector machine using whale optimization algorithm with CEEMDAN (CEEMDAN-WOA-SVM), and a hybrid model combines random forest (RF) and CEEMDAN. Three novel models have been verified in two projects across the Yangtze River in China. It is found that the prediction accuracy is dramatically improved compared with the original analytical models (or empirical models). In addition, through the feasibility analysis, the great potential of machine learning model in near real-time prediction is proved. At the end of this dissertation, in addition to summarizing the main conclusions obtained, three future research directions are also pointed out: (1) stress analysis of pipelines installed by HDD in more complex situations; (2) stress analysis of pipeline during HDD construction; (3) database establishment in HDD engineering

Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives

Deep learning (DL), a subset of machine learning approaches, has emerged as a versatile tool to assimilate large amounts of heterogeneous data and provide reliable predictions of complex and uncertain phenomena. These tools are increasingly being used by the plant science community to make sense of the large datasets now regularly collected via high-throughput phenotyping and genotyping. We review recent work where DL principles have been utilized for digital image–based plant stress phenotyping. We provide a comparative assessment of DL tools against other existing techniques, with respect to decision accuracy, data size requirement, and applicability in various scenarios. Finally, we outline several avenues of research leveraging current and future DL tools in plant science