Big Earth Data and Machine Learning for Sustainable and Resilient Agriculture

Big streams of Earth images from satellites or other platforms (e.g., drones and mobile phones) are becoming increasingly available at low or no cost and with enhanced spatial and temporal resolution. This thesis recognizes the unprecedented opportunities offered by the high quality and open access Earth observation data of our times and introduces novel machine learning and big data methods to properly exploit them towards developing applications for sustainable and resilient agriculture. The thesis addresses three distinct thematic areas, i.e., the monitoring of the Common Agricultural Policy (CAP), the monitoring of food security and applications for smart and resilient agriculture. The methodological innovations of the developments related to the three thematic areas address the following issues: i) the processing of big Earth Observation (EO) data, ii) the scarcity of annotated data for machine learning model training and iii) the gap between machine learning outputs and actionable advice. This thesis demonstrated how big data technologies such as data cubes, distributed learning, linked open data and semantic enrichment can be used to exploit the data deluge and extract knowledge to address real user needs. Furthermore, this thesis argues for the importance of semi-supervised and unsupervised machine learning models that circumvent the ever-present challenge of scarce annotations and thus allow for model generalization in space and time. Specifically, it is shown how merely few ground truth data are needed to generate high quality crop type maps and crop phenology estimations. Finally, this thesis argues there is considerable distance in value between model inferences and decision making in real-world scenarios and thereby showcases the power of causal and interpretable machine learning in bridging this gap.Comment: Phd thesi

Sustainable Agriculture and Advances of Remote Sensing (Volume 2)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publication of the results, among others

Remote Sensing for Precision Nitrogen Management

This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment

Gestione sostenibile del vigneto mediante Data Science e Big Data Management

Negli ultimi anni, la ricerca in ambito viticolo (Vitis vinifera L.) è stata notevolmente influenzata dalla necessità duplice di rispondere alla crescente domanda di prodotto ad elevati standard qualitativi e a mediare criticità derivanti dagli effetti del cambiamento climatico. Alla base della mediazione di tali fattori risulta fondamentale una ricalibrazione della gestione del vigneto, spostandosi da un approccio convenzionale che prevede una sua gestione come unità omogenea, verso uno che tenga in considerazione le sue discontinuità spaziali legate alle peculiarità pedoclimatiche e alle variabili biotiche, le quali, avendo riflessi eterogenei sul ciclo biologico della vite, determinano un uso non sempre razionale delle risorse. Emerge così l’esigenza di un rinnovamento dei sistemi di monitoraggio, che unisca il trasferimento tecnologico alle conoscenze scientifiche pregresse, verso usi mirati e calibrati sull'ambito viticolo, attraverso i quali poter attuare strategie previsionali che permettano la salvaguardia degli equilibri ecologici pur mantenendo inalterato il livello di produttività e qualità. Nello scenario della moderna viticoltura, il flusso di dati estratti dal campo proviene da fonti diverse tra loro. Si tratta di informazioni relative a diversi aspetti, che vanno dalla caratterizzazione della fisiologia delle piante, alla natura del contesto pedoclimatico fino a dati relativi alla gestione colturale: concimazione, irrigazione, potatura. Appare chiaro che, oltre a fornire grandi opportunità di indagine del sistema vigneto, questa abbondanza e diversificazione dei dati pone di fronte l’onere di dover gestire moli di dati spesso non strutturati che, pur avendo un grande valore intrinseco, richiedono di essere analizzate e sintetizzate affinché possano essere utilizzate in maniera proficua per la gestione agronomica del vigneto. Questi, infatti, se slegati dal contesto o se letti individualmente, danno spesso informazioni assai scarse, difficilmente leggibili, poco legate alla realtà applicativa e che in alcuni casi portano ad errori. Lo scopo dell’analisi di tali dati (chiamati non a caso Big Data) è quindi quello di individuare correlazioni, tendenze, pattern che si ripetono secondo schemi più o meno intuitivi, dinamiche di interdipendenza nascoste o comunque non facilmente identificabili, al fine di elaborare modelli simulativi costantemente aggiornati sulla base della biodiversità del panorama viticolo e dei contesti pedoclimatici, che consentano decisioni basate su dati più strettamente connessi alla realtà di campo anziché sulla semplice speculazione empirica o su serie storiche, con relativi vantaggi gestionali. Gli obiettivi della tesio sono stati quelli di: (i) sviluppare metodologie per l'acquisizione e l'analisi di immagini RGB dal contesto vigneto ed estrarre e analizzare i dati ad esse relativi per meglio comprendere le criticità, i vantaggi e le prospettive applicative di tale tecnologia; (ii) sviluppare modelli per la stima dello stato idrico della vite basati sull'analisi spazio-temporale di dati relativi al sistema pianta-suolo-atmosfera, per acquisire utili informazioni sulla gestione dell'irrigazione; (iii) applicare le metodologie e i modelli di simulazione sviluppati su casi studio reali per valutarne le prestazioni, confrontandoli con metodi esistenti, e analizzando la loro accuratezza nel fornire informazioni per la gestione sostenibile del vigneto

Land Degradation Assessment with Earth Observation

This Special Issue (SI) on “Land Degradation Assessment with Earth Observation” comprises 17 original research papers with a focus on land degradation in arid, semiarid and dry-subhumid areas (i.e., desertification) in addition to temperate rangelands, grasslands, woodlands and the humid tropics. The studies cover different spatial, spectral and temporal scales and employ a wealth of different optical and radar sensors. Some studies incorporate time-series analysis techniques that assess the general trend of vegetation or the timing and duration of the reduction in biological productivity caused by land degradation. As anticipated from the latest trend in Earth Observation (EO) literature, some studies utilize the cloud-computing infrastructure of Google Earth Engine to cope with the unprecedented volume of data involved in current methodological approaches. This SI clearly demonstrates the ever-increasing relevance of EO technologies when it comes to assessing and monitoring land degradation. With the recently published IPCC Reports informing us of the severe impacts and risks to terrestrial and freshwater ecosystems and the ecosystem services they provide, the EO scientific community has a clear obligation to increase its efforts to address any remaining gaps—some of which have been identified in this SI—and produce highly accurate and relevant land-degradation assessment and monitoring tools

Proceedings of the European Conference on Agricultural Engineering AgEng2021

This proceedings book results from the AgEng2021 Agricultural Engineering Conference under auspices of the European Society of Agricultural Engineers, held in an online format based on the University of Évora, Portugal, from 4 to 8 July 2021. This book contains the full papers of a selection of abstracts that were the base for the oral presentations and posters presented at the conference. Presentations were distributed in eleven thematic areas: Artificial Intelligence, data processing and management; Automation, robotics and sensor technology; Circular Economy; Education and Rural development; Energy and bioenergy; Integrated and sustainable Farming systems; New application technologies and mechanisation; Post-harvest technologies; Smart farming / Precision agriculture; Soil, land and water engineering; Sustainable production in Farm buildings