Electrophysiological assessment of plant status outside a Faraday cage using supervised machine learning

Living organisms have evolved complex signaling networks to drive appropriate physiological processes in response to changing environmental conditions. Amongst them, electric signals are a universal method to rapidly transmit information. In animals, bioelectrical activity measurements in the heart or the brain provide information about health status. In plants, practical measurements of bioelectrical activity are in their infancy and transposition of technology used in human medicine could therefore, by analogy provide insight about the physiological status of plants. This paper reports on the development and testing of an innovative electrophysiological sensor that can be used in greenhouse production conditions, without a Faraday cage, enabling real-time electric signal measurements. The bioelectrical activity is modified in response to water stress conditions or to nycthemeral rhythm. Furthermore, the automatic classification of plant status using supervised machine learning allows detection of these physiological modifications. This sensor represents an efficient alternative agronomic tool at the service of producers for decision support or for taking preventive measures before initial visual symptoms of plant stress appear

The future of road transport

A perfect storm of new technologies and new business models is transforming not only our vehicles, but everything about how we get around, and how we live our lives. The JRC report “The future of road transport - Implications of automated, connected, low-carbon and shared mobility” looks at some main enablers of the transformation of road transport, such as data governance, infrastructures, communication technologies and cybersecurity, and legislation. It discusses the potential impacts on the economy, employment and skills, energy use and emissions, the sustainability of raw materials, democracy, privacy and social fairness, as well as on the urban context. It shows how the massive changes on the horizon represent an opportunity to move towards a transport system that is more efficient, safer, less polluting and more accessible to larger parts of society than the current one centred on car ownership. However, new transport technologies, on their own, won't spontaneously make our lives better without upgrading our transport systems and policies to the 21st century. The improvement of governance and the development of innovative mobility solutions will be crucial to ensure that the future of transport is cleaner and more equitable than its car-centred present.JRC.C.4-Sustainable Transpor

Detecting stress caused by nitrogen deficit using deep learning techniques applied on plant electrophysiological data

Plant electrophysiology carries a strong potential for assessing the health of a plant. Current literature for the classification of plant electrophysiology generally comprises classical methods based on signal features that portray a simplification of the raw data and introduce a high computational cost. The Deep Learning (DL) techniques automatically learn the classification targets from the input data, overcoming the need for precalculated features. However, they are scarcely explored for identifying plant stress on electrophysiological recordings. This study applies DL techniques to the raw electrophysiological data from 16 tomato plants growing in typical production conditions to detect the presence of stress caused by a nitrogen deficiency. The proposed approach predicts the stressed state with an accuracy of around 88%, which could be increased to over 96% using a combination of the obtained prediction confidences. It outperforms the current state-of-the-art with over 8% higher accuracy and a potential for a direct application in production conditions. Moreover, the proposed approach demonstrates the ability to detect the presence of stress at its early stage. Overall, the presented findings suggest new means to automatize and improve agricultural practices with the aim of sustainability

Classification of plant electrophysiology signals for detection of spider mites infestation in tomatoes

Herbivorous arthropods, such as spider mites, are one of the major causes of annual crop losses. They are usually hard to spot before a severe infestation takes place. When feeding, these insects cause external perturbation that triggers changes in the underlying physiological process of a plant, which are expressed by a generation of distinct variations of electrical potential. Therefore, plant electrophysiology data portray information of the plant state. Analyses involving machine learning techniques applied to plant electrical response triggered by spider mite infestation have not been previously reported. This study investigates plant electrophysiological signals recorded from 12 commercial tomatoes plants contaminated with spider mites and proposes a workflow based on Gradient Boosted Tree algorithm for an automated differentiation of the plant’s normal state from the stressed state caused by infestation. The classification model built using the signal samples recorded during daylight and employing a reduced feature subset performs with an accuracy of 80% in identifying the plant’s stressed state. Furthermore, the Hjorth complexity encloses the most relevant information for discrimination of the plant status. The obtained findings open novel access towards automated detection of insect infestation in greenhouse crops and, consequently, more optimal prevention and treatment approaches

Assessment of the universality of the electrophysiological signal acquired from tomatoes and eggplants

The electrical signaling system in plants represents the most efficient means for rapidly transmitting information about changes in the environment to all plant parts. Recent studies have shown that the application of machine learning techniques to the electrophysiological signal acquired on tomato plants growing under typical production conditions enables highly accurate detection of stress in plants due to either drought, nutrient deficiency, or pest attack. To better understand how specific are the acquired learnings to tomato plants only, this study aims to explore the extent of the universality of the electrophysiological signal from tomatoes and eggplants. To this end, we modeled the drought response in both tomato and eggplants individually, using recordings from 34 plants from each crop, and evaluated the performance of the classification models trained on data from one crop to the data from the other crop. Different features appear as the most discriminative for each crop. Therefore, several models were taken in this analysis, namely those trained with: i) all extracted features, ii) the most discriminative groups of features for the tomatoes, iii) the most discriminative groups of features for the eggplants, and iv) the union of the most discriminative groups of features for both crops. The obtained findings showed that the models built on data from one crop are able to predict the plant state of the other crop if they are trained with the set of features enclosing the most discriminative ones for the crop on which the model is being evaluated. Such findings imply some similarities in the electrophysiological signals acquired from these two crops with a certain level of crop specificity indicated by the dissimilarities between the discriminatory information for a specific stressor