Analysis, Modeling and Multi-Spectral Sensing for the Predictive Management of Verticillium Wilt in Olive Groves

The intensification and expansion in the cultivation of olives have contributed to the significant spread of Verticillium wilt, which is the most important fungal problem affecting olive trees. Recent studies confirm that practices such as the use of innovative natural minerals (Zeoshell ZF1) and the application of beneficial microorganisms (Micosat F BS WP) restore health in infected trees. However, for their efficient implementation the above methodologies require the marking of trees in the early stages of infestation—a task that is impractical with traditional means (manual labor) but also very difficult, as early stages are difficult to perceive with the naked eye. In this paper, we present the results of the My Olive Grove Coach (MyOGC) project, which used multispectral imaging from unmanned aerial vehicles to develop an olive grove monitoring system based on the autonomous and automatic processing of the multispectral images using computer vision and machine learning techniques. The goal of the system is to monitor and assess the health of olive groves, help in the prediction of Verticillium wilt spread and implement a decision support system that guides the farmer/agronomist.

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Predicting Alzheimer’s disease progression trajectory and clinical subtypes using machine learning

10.1101/792432 ◽

2019 ◽

Author(s):

Vipul K. Satone ◽

Rachneet Kaur ◽

Anant Dadu ◽

Hampton Leonard ◽

Hirotaka Iwaki ◽

...

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Disease Progression ◽

Disease Onset ◽

Machine Learning Techniques ◽

Progression Rate ◽

Early Stages ◽

Age Related ◽

Learning Techniques

AbstractBackgroundAlzheimer’s disease (AD) is a common, age-related, neurodegenerative disease that impairs a person’s ability to perform day-to-day activities. Diagnosing AD is challenging, especially in the early stages. Many patients still go undiagnosed, partly due to the complex heterogeneity in disease progression. This highlights a need for early prediction of the disease course to assist its treatment and tailor therapy options to the disease progression rate. Recent developments in machine learning techniques provide the potential to not only predict disease progression and trajectory of AD but also to classify the disease into different etiological subtypes.Methods and findingsThe work shown here clusters participants in distinct and multifaceted progression subgroups of AD and discusses an approach to predict the progression rate from baseline diagnosis. We observed that the myriad of clinically reported symptoms summarized in the proposed AD progression space corresponds directly with memory and cognitive measures, which are routinely used to monitor disease onset and progression. Our analysis demonstrated accurate prediction of disease progression after four years from the first 12 months of post-diagnosis clinical data (Area Under the Curve of 0.96 (95% confidence interval (CI), 0.92-1.0), 0.81 (95% CI, 0.74-0.88) and 0.98 (95% CI, 0.96-1.0) for slow, moderate and fast progression rate patients respectively). Further, we explored the long short-term memory (LSTM) neural networks to predict the trajectory of an individual patient’s progression.ConclusionThe machine learning techniques presented in this study may assist providers in identifying different progression rates and trajectories in the early stages of the disease, hence allowing for more efficient and personalized healthcare deliveries. With additional information about the progression rate of AD at hand, providers may further individualize the treatment plans. The predictive tests discussed in this study not only allow for early AD diagnosis but also facilitate the characterization of distinct AD subtypes relating to trajectories of disease progression. These findings are a crucial step forward for early disease detection. These models can be used to design improved clinical trials for AD research.

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Estimating Productivity, Detecting Biotic Disturbances and Assessing the Health State of Traditional Olive Groves, Using Nondestructive Phenotypic Techniques

Sustainability ◽

10.3390/su14010391 ◽

2021 ◽

Vol 14 (1) ◽

pp. 391

Author(s):

Yiannis G. Zevgolis ◽

Efstratios Kamatsos ◽

Triantaphyllos Akriotis ◽

Panayiotis G. Dimitrakopoulos ◽

Andreas Y. Troumbis

Keyword(s):

Population Level ◽

Olive Tree ◽

Leaf Spot Disease ◽

Health State ◽

Olive Grove ◽

Thermal Indices ◽

Health States ◽

Significant Information ◽

Olive Groves ◽

Olive Trees

Conservation of traditional olive groves through effective monitoring of their health state is crucial both at a tree and at a population level. In this study, we introduce a comprehensive methodological framework for estimating the traditional olive grove health state, by considering the fundamental phenotypic, spectral, and thermal traits of the olive trees. We obtained phenotypic information from olive trees on the Greek island of Lesvos by combining this with in situ measurement of spectral reflectance and thermal indices to investigate the effect of the olive tree traits on productivity, the presence of the olive leaf spot disease (OLS), and olive tree classification based on their health state. In this context, we identified a suite of important features, derived from linear and logistic regression models, which can explain productivity and accurately evaluate infected and noninfected trees. The results indicated that either specific traits or combinations of them are statistically significant predictors of productivity, while the occurrence of OLS symptoms can be identified by both the olives’ vitality traits and by the thermal variables. Finally, the classification of olive trees into different health states possibly offers significant information to explain traditional olive grove dynamics for their sustainable management.

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Influence of weed cover on leaf-level CO2 and H2O fluxes in an olive grove

10.5194/egusphere-egu2020-5181 ◽

2020 ◽

Author(s):

Sergio Aranda-Barranco ◽

Andrew S Kowalski ◽

Penélope Serrano-Ortiz ◽

Enrique P Sánchez-Cañete

Keyword(s):

Random Trees ◽

The Other ◽

Organic Carbon Content ◽

Olive Grove ◽

Weed Cover ◽

Leaf Level ◽

Olive Groves ◽

Ecosystem Level ◽

Olive Trees

The management of olive groves has a direct impact on the environment in the Mediterranean region since it is one of the most representative crops in this area. In order to prevent erosion and improve the physical-chemical conditions of the soil in these crops, the maintenance of weed cover in the alleys is an increasingly common practice. It increases the organic carbon content in the soil, improves biodiversity indices and enhances various ecosystem services such as pollination and infiltration. Now, the role of vegetation cover in olive groves on biogeochemical cycles is being studied. Although previous studies have quantified the combined effect of weed cover and olive trees on carbon and water at ecosystem level, the role of this conservation practice at the leaf level has not yet been explored.The aim of this study is to quantify the effect of weed cover on the net CO2 assimilation (An) and transpiration (T) rates in an irrigated olive grove. To do this, two plots of olive trees with irrigation (Olea europea L. "Arbequina") in southeast Spain were sampled. In the weed-cover one (WC), spontaneous vegetation is maintained until it is mechanically mowed and left in place. In the weed-free (WF) a glyphosate-based herbicide is applied. The data were taken with a portable gas analyzer (LI-6800, Li-Cor) controlling the following environmental variables on olive leaves: atmospheric CO2, relative humidity, photosynthetic active radiation and temperature. One campaign per month was carried out (from January-2018 to January-2019) where 10 random trees were analysed in each treatment. In addition, an eddy covariance tower provided CO2 and H2O fluxes at ecosystem level and they were compared with the fluxes obtained from leaf-level campaigns.The results shown significant differences for T only in the period after mowing with Twc= 2.0 &#177; 0.7 mmol H2O m-2s-1 vs Twf = 2.5 &#177; 1.0 mmol H2O m-2s-1. However, in this period ET is equal in both treatments, which suggests that the alleys with mowed weed has more ET than bare soil in the other treatment. On the other hand, there are significant differences for Anet only in the period before mowing with Anet-wc = 5.5 &#177; 3.1 &#956;mol CO2 m-2s-1 vs Anet-wf = 8.0 &#177; 3.6 &#956;mol CO2 m-2s-1. When the weeds are mowed, Anet is matched in both treatments. However, higher values of NEEwc than NEEwf &#160;are observed in the period before mowing. This suggest that the weed-cover olive groves at ecosystem level take up more carbon when the weed-cover is established although the leaves of olive trees are capturing less CO2.

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The Ecology and History of the Mediterranean Olive Grove: The Spanish Great Expansion, 1750 - 2000

Rural History ◽

10.1017/s0956793312000052 ◽

2012 ◽

Vol 23 (2) ◽

pp. 161-184 ◽

Cited By ~ 17

Author(s):

JUAN INFANTE-AMATE

Keyword(s):

Large Scale ◽

Olive Grove ◽

Crop Cultivation ◽

Industrial Enterprises ◽

History Of ◽

Olive Groves ◽

Olive Trees ◽

Peasant Farmers ◽

Changing Role

AbstractThis article argues that the landscape dominated by olive groves that is now seen as characteristic of southern Spain is a relatively recent phenomenon. In the eighteenth, nineteenth and much of the twentieth century, olives were not an industrial crop, grown on a large scale for the production of oil. Instead, olive trees were largely grown by small peasant farmers and used to produce timber and fodder as well as foodstuffs, forming one component of a diverse peasant economy. This article will analyse the changing role of the olive within the landscape of the Spanish Mediterranean, and explore the process by which production moved towards single crop cultivation by large industrial enterprises.

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Research on Classification Method of Eggplant Seeds Based on Machine Learning and Multispectral Imaging Classification Eggplant Seeds

Journal of Sensors ◽

10.1155/2021/8857931 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Lei Sun ◽

Xiaofei Fan ◽

Sheng Huang ◽

Shuangxia Luo ◽

Lili Zhao ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Convolutional Neural Network ◽

Multispectral Imaging ◽

Machine Learning Techniques ◽

Multispectral Images ◽

One Dimensional ◽

Learning Techniques ◽

Discriminant Analyses ◽

Genetic And Environmental Factors

In this study, eggplant seeds of fifteen different varieties were selected for discriminant analyses with a multispectral imaging technique. Seventy-eight features acquired with the multispectral images were extracted from individual eggplant seeds, which were then classified using SVM and a one-dimensional convolutional neural network (1D-CNN), and the overall accuracy was 90.12% and 94.80%, respectively. A two-dimensional convolutional neural network (2D-CNN) was also adopted for discrimination of seed varieties, and an accuracy of 90.67% was achieved. This study not only demonstrated that multispectral imaging combining machine learning techniques could be used as a high-throughput and nondestructive tool to discriminate seed varieties but also revealed that the shape of the seed shell may not be exactly the same as the female parents due to the genetic and environmental factors.

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Towards Precision Medicine in Psychosis: Benefits and Challenges of Multimodal Multicenter Studies—PSYSCAN: Translating Neuroimaging Findings From Research into Clinical Practice

Schizophrenia Bulletin ◽

10.1093/schbul/sbz067 ◽

2019 ◽

Vol 46 (2) ◽

pp. 432-441 ◽

Cited By ~ 21

Author(s):

Stefania Tognin ◽

Hendrika H van Hell ◽

Kate Merritt ◽

Inge Winter-van Rossum ◽

Matthijs G Bossong ◽

...

Keyword(s):

Statistical Power ◽

External Validation ◽

Analytical Techniques ◽

Biological Data ◽

Machine Learning Techniques ◽

Small Samples ◽

Multicenter Studies ◽

Early Stages ◽

Learning Techniques ◽

Cognitive Data

Abstract In the last 2 decades, several neuroimaging studies investigated brain abnormalities associated with the early stages of psychosis in the hope that these could aid the prediction of onset and clinical outcome. Despite advancements in the field, neuroimaging has yet to deliver. This is in part explained by the use of univariate analytical techniques, small samples and lack of statistical power, lack of external validation of potential biomarkers, and lack of integration of nonimaging measures (eg, genetic, clinical, cognitive data). PSYSCAN is an international, longitudinal, multicenter study on the early stages of psychosis which uses machine learning techniques to analyze imaging, clinical, cognitive, and biological data with the aim of facilitating the prediction of psychosis onset and outcome. In this article, we provide an overview of the PSYSCAN protocol and we discuss benefits and methodological challenges of large multicenter studies that employ neuroimaging measures.

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Using machine learning techniques to reduce data annotation time

PsycEXTRA Dataset ◽

10.1037/e577762012-020 ◽

2006 ◽

Author(s):

Christopher Schreiner ◽

Kari Torkkola ◽

Mike Gardner ◽

Keshu Zhang

Keyword(s):

Machine Learning ◽

Machine Learning Techniques ◽

Data Annotation ◽

Learning Techniques

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Using Machine Learning Algorithms on Prediction of Stock Price

Journal of Modeling and Optimization ◽

10.32732/jmo.2020.12.2.84 ◽

2020 ◽

Vol 12 (2) ◽

pp. 84-99

Author(s):

Li-Pang Chen

Keyword(s):

Machine Learning ◽

Stock Price ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Short Term ◽

Learning Techniques ◽

Historical Database ◽

Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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