Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs

Precision farming robots, which target to reduce the amount of herbicides that need to be brought out in the fields, must have the ability to identify crops and weeds in real time to trigger weeding actions. In this paper, we address the problem of CNN-based semantic segmentation of crop fields separating sugar beet plants, weeds, and background solely based on RGB data. We propose a CNN that exploits existing vegetation indexes and provides a classification in real time. Furthermore, it can be effectively re-trained to so far unseen fields with a comparably small amount of training data. We implemented and thoroughly evaluated our system on a real agricultural robot operating in different fields in Germany and Switzerland. The results show that our system generalizes well, can operate at around 20Hz, and is suitable for online operation in the fields.Comment: Accepted for publication at IEEE International Conference on Robotics and Automation 2018 (ICRA 2018

California Almond Yield Prediction at the Orchard Level With a Machine Learning Approach.

California's almond growers face challenges with nitrogen management as new legislatively mandated nitrogen management strategies for almond have been implemented. These regulations require that growers apply nitrogen to meet, but not exceed, the annual N demand for crop and tree growth and nut production. To accurately predict seasonal nitrogen demand, therefore, growers need to estimate block-level almond yield early in the growing season so that timely N management decisions can be made. However, methods to predict almond yield are not currently available. To fill this gap, we have developed statistical models using the Stochastic Gradient Boosting, a machine learning approach, for early season yield projection and mid-season yield update over individual orchard blocks. We collected yield records of 185 orchards, dating back to 2005, from the major almond growers in the Central Valley of California. A large set of variables were extracted as predictors, including weather and orchard characteristics from remote sensing imagery. Our results showed that the predicted orchard-level yield agreed well with the independent yield records. For both the early season (March) and mid-season (June) predictions, a coefficient of determination (R 2) of 0.71, and a ratio of performance to interquartile distance (RPIQ) of 2.6 were found on average. We also identified several key determinants of yield based on the modeling results. Almond yield increased dramatically with the orchard age until about 7 years old in general, and the higher long-term mean maximum temperature during April-June enhanced the yield in the southern orchards, while a larger amount of precipitation in March reduced the yield, especially in northern orchards. Remote sensing metrics such as annual maximum vegetation indices were also dominant variables for predicting the yield potential. While these results are promising, further refinement is needed; the availability of larger data sets and incorporation of additional variables and methodologies will be required for the model to be used as a fertilization decision support tool for growers. Our study has demonstrated the potential of automatic almond yield prediction to assist growers to manage N adaptively, comply with mandated requirements, and ensure industry sustainability

REMOTE SENSING OF FOLIAR NITROGEN IN CULTIVATED GRASSLANDS OF HUMAN DOMINATED LANDSCAPES

Foliar nitrogen (N) concentration of plant canopies plays a central role in a number of important ecosystem processes and continues to be an active subject in the field of remote sensing. Previous efforts to estimate foliar N at the landscape scale have primarily focused on intact forests and grasslands using aircraft imaging spectrometry and various techniques of statistical calibration and modeling. The present study was designed to extend this work by examining the potential to estimate the foliar N concentration of residential, agricultural and other cultivated grassland areas within a suburbanizing watershed. In conjunction with ground-based vegetation sampling, we developed Partial Least Squares (PLS) models for predicting mass-based foliar N across management types using input from airborne and field based imaging spectrometers. Results yielded strong predictive relationships for both ground- and aircraft-based sensors across sites that included turf grass, grazed pasture, hayfields and fallow fields. We also report on relationships between imaging spectrometer data and other important variables such as canopy height, biomass, and water content, results from which show strong promise for detection with high quality imaging spectrometry data and suggest that cultivated grassland offer opportunity for empirical study of canopy light dynamics. Finally, we discuss the potential for application of our results, and potential challenges, with data from the planned HyspIRI satellite, which will provide global coverage of data useful for vegetation N estimation

Innovative Surveillance and Process Control in Water Resource Recovery Facilities

Water Resource Recovery Facilities (WRRF), previously known as Wastewater Treatment Plants (WWTP), are getting increasingly complex, with the incorporation of sludge processing and resource recovery technologies. Along with maintaining a stringent effluent water quality standard, the focus is gradually shifting towards energy-efficient operations and recovery of resources. The new objectives of the WRRF demand an economically optimal operation of processes that are subjected to extreme variations in flowrate and composition at the influent. The application of online monitoring, process control, and automation in WRRF has already shown a steady increase in the past decade. However, the advanced model-based optimal control strategies, implemented in most process industries, are less common in WRRF. The complex nature of biological processes, the unavailability of simplified process models, and a lack of cost-effective surveillance infrastructure have often hindered the implementation of advanced control strategies in WRRF. The ambition of this research is to implement and validate cost-efficient monitoring alternatives and advanced control strategies for WRRF by fully utilizing the powerful Internet of Things (IoT) and data science tools. The first step towards implementing an advanced control strategy is to ensure the availability of surveillance infrastructure for monitoring nutrient compositions in WRRF processes. In Paper A, a soft sensor, based on Extended Kalman Filter, is developed for estimating water-quality parameters in a Sequential Batch MBBR process using reliable and inexpensive online sensors. The model used in the soft sensor combines the mechanistic understanding of the nutrient removal process with a statistical correlation between nutrient composition and easy-to-measure parameters. Paper B demonstrates the universality of the soft sensor through validation tests conducted in a Continuous Multistage MBBR pilot plant. The drift in soft-sensor estimation caused by a mismatch between the mathematical model and process behavior is studied in Paper B. The robustness of the soft sensor is assessed by observing estimated nutrient composition values for a period of three months. A systematic method to calibrate the measurement model and update model parameters using data from periodic lab measurements are discussed in Paper B. The term SCADA has been ubiquitous while mentioning online monitoring and control strategy deployment in WRRFs. The present digital world of affordable communication hardware, compact single board processors, and high computational power presents several options for remote monitoring and deployment of soft sensors. In Paper C, a cost-effective IoT strategy is developed by using an open-source programming language and inexpensive hardware. The functionalities of the IoT infrastructure are demonstrated by using it to deploy a soft sensor script in the ContinuousMultistage MBBR pilot plant. A cost-comparison between the commercially available alternatives presented in Paper A and the open-source IoT strategy in Paper B and Paper C highlights the benefits of the new monitoring infrastructure. Lack of reliable control models have often been the cause for the poor performance of advanced control strategies, such as Model Predictive Controls (MPC) when implemented to complex biological nutrient removal processes. Paper D attempts to overcome the inadequacies of the linear prediction model by combining a recursive model parameter estimator with the linear MPC. The new MPC variant, called the adaptive MPC (AMPC), reduces the dependency of MPC on the accuracy of its prediction model. The performance of the AMPC is compared with that of a linear MPC, nonlinear MPC, and the traditional proportional-integral cascade control through simulator-based evaluations conducted on the Benchmark Simulator platform(BSM2). The advantages of AMPC compared to its counterparts, in terms of reducing the aeration energy, curtailing the number of effluent ammonia violations, and the use of computational resources, are highlighted in Paper D. The complex interdependencies between different processes in a WRRF pose a significant challenge in defining constant reference points for WRRFs operations. A strategy that decides control outputs based on economic parameters rather than maintaining a fixed reference set-point is introduced in Paper E. The model-based control strategy presented in Paper D is further improved by including economic parameters in the MPC’s objective function. The control strategy known as Economic MPC (EMPC) is implemented for optimal dosage of magnesium hydroxide in a struvite recovery unit installed in a WRRF. A comparative study performed on the BSM2 platform demonstrates a significant improvement in overall profitability for the EMPC when compared to a constant or a feed-forward flow proportional control strategy. The resilience of the EMPC strategy to variations in the market price of struvite is also presented in Paper E. A combination of cost-effective monitoring infrastructure and advanced control strategies using advanced IoTs and data science tools have been documented to overcome some of the critical problems encountered in WRRFs. The overall improvement in process efficiency, reduction in operating costs, an increase in resource recovery, and a substantial reduction in the price of online monitoring infrastructure contribute to the overall aim of transitioning WRRFs to a self-sustaining facility capable of generating value-added products.Water Resource Recovery Facilities (WRRF), tidligere kjent som avløpsrenseanlegg (WWTP), blir stadig mer komplekse ettersom flere prosess steg tillegges anleggene i form av slambehandling og ressursgjenvinningsteknologi. Foruten hovedmålet om å imøtekomme strenge avløpsvannskvalitetskrav, har anleggenes fokus gradvis skiftet mot energieffektiv drift og gjenvinning av ressurser. Slike nye mål krever økonomisk optimal drift av prosesser som er utsatt for ekstreme variasjoner i volum og sammensetning av tilløp. Bruk av online overvåking, prosesskontroll og automatisering i WRRF har jevnt økt det siste tiåret. Likevel er avanserte modellbaserte kontrollstrategier for optimalisering ikke vanlig i WRRF, i motsetning til de fleste prosessindustrier. Komplekse forhold i biologiske prosesser, mangel på tilgang til pålitelige prosessmodeller og mangel på kostnadseffektiv overvåkingsinfrastruktur har ofte hindret implementeringen av avanserte kontrollstrategier i WRRF. Ambisjonen med denne avhandlingen er å implementere og validere kostnadseffektive overvåkingsalternativer og avanserte kontrollstrategier somutnytter kraftige Internet of Things (IoT) og datavitenskapelige verktøy i WRRF sammenheng. Det første steget mot implementering av en avansert kontrollstrategi er å sørge for tilgjengelighet av overvåkingsinfrastruktur for måling av næringsstoffer i WRRF-prosesser. Paper A demonstrerer en virtuell sensor basert på et utvidet Kalman filter, utviklet for å estimere vannkvalitetsparametere i en sekvensiell batch MBBR prosess ved hjelp av pålitelige og rimelige online sensorer. Modellen som brukes i den virtuelle sensoren kombinerer en mekanistisk forståelse av prosessen for fjerning av næringsstoffer fra avløpsvann med et statistisk sammenheng mellom næringsstoffsammensetning i avløpsvann og parametere som er enkle å måle. Paper B demonstrerer det universale bruksaspektet til den virtuelle sensoren gjennom valideringstester utført i et kontinuerlig flertrinns MBBR pilotanlegg. Feilene i sensorens estimering forårsaket av uoverensstemmelse mellom den matematiske modellen og prosesseatferden er undersøkt i Paper B. Robustheten til den virtuelle sensoren ble vurdert ved å observere estimerte næringssammensetningsverdier i en periode på tre måneder. En systematisk metode for å kalibrere målemodellen og oppdatere modellparametere ved hjelp av data fra periodiske laboratoriemålinger er diskutert i Paper B. Begrepet SCADA har alltid vært til stede når online overvåking og kontrollstrategi innen WRRF er nevnt. Den nåværende digitale verdenen med god tilgjengelighet av rimelig kommunikasjonsmaskinvare, kompakte enkeltkortprosessorer og høy beregningskraft presenterer flere muligheter for fjernovervåking og implementering av virtuelle sensorer. Paper C viser til utvikling av en kostnadseffektiv IoT-strategi ved hjelp av et programmeringsspråk med åpen kildekode og rimelig maskinvare. Funksjonalitetene i IoT-infrastruktur demonstreres gjennom implementering av et virtuelt sensorprogram i et kontinuerlig flertrinns MBBR pilotanlegg. En kostnadssammenligning mellom de kommersielt tilgjengelige alternativene som presenteres i Paper A og åpen kildekode-IoT-strategi i Paper B og Paper C fremhever fordelene med den nye overvåkingsinfrastrukturen. Mangel på pålitelige kontrollmodeller har ofte vært årsaken til svake resultater i avanserte kontrollstrategier, som for eksempel Model Predictive Control (MPC) når de implementeres i komplekse biologiske prosesser for fjerning av næringsstoffer. Paper D prøver å løse manglene i MPC ved å kombinere en rekursiv modellparameterestimator med lineær MPC. Den nye MPC-varianten, kalt Adaptiv MPC (AMPC), reduserer MPCs avhengighet av nøyaktigheten i prediksjonsmodellen. Ytelsen til AMPC sammenlignes med ytelsen til en lineær MPC, ikke-lineær MPC og tradisjonell proportionalintegral kaskadekontroll gjennom simulatorbaserte evalueringer utført på Benchmark Simulator plattformen (BSM2). Fordelene med AMPC sammenlignet med de andre kontrollstrategiene er fremhevet i Paper D og demonstreres i sammenheng redusering av energibruk ved lufting i luftebasseng, samt redusering i antall brudd på utslippskrav for ammoniakk og bruk av beregningsressurser. De komplekse avhengighetene mellom forskjellige prosesser i en WRRF utgjør en betydelig utfordring når man skal definere konstante referansepunkter for WRRF under drift. En strategi som bestemmer kontrollsignaler basert på økonomiske parametere i stedet for å opprettholde et fast referansesettpunkt introduseres i Paper E. Den modellbaserte kontrollstrategien fra PaperDforbedres ytterligere ved å inkludere økonomiske parametere iMPCs objektiv funksjon. Denne kontrollstrategien kalles Economic MPC (EMPC) og er implementert for optimal dosering av magnesiumhydroksid i en struvit utvinningsenhet installert i en WRRF. En sammenligningsstudie utført på BSM2 plattformen viste en betydelig forbedring i den totale lønnsomheten ved bruk av EMPC sammenlignet med en konstant eller en flow proportional kontrollstrategi. Robustheten til EMPC-strategien for variasjoner i markedsprisen på struvit er også demonstrert i Paper E. En kombinasjon av kostnadseffektiv overvåkingsinfrastruktur og avanserte kontrollstrategier ved hjelp av avansert IoT og datavitenskapelige verktøy er brukt for å løse flere kritiske utfordringer i WRRF. Den samlede forbedringen i prosesseffektivitet, reduksjon i operasjonskostnader, økt ressursgjenvinning og en betydelig reduksjon i pris for online overvåkningsinfrastruktur bidrar til det overordnede målet om å gå over til bærekraftige WRRF som er i stand til å generere verdiskapende produkter.DOSCON A

Opportunities and limitations of crop phenotyping in southern european countries

ReviewThe Mediterranean climate is characterized by hot dry summers and frequent droughts. Mediterranean crops are frequently subjected to high evapotranspiration demands, soil water deficits, high temperatures, and photo-oxidative stress. These conditions will become more severe due to global warming which poses major challenges to the sustainability of the agricultural sector in Mediterranean countries. Selection of crop varieties adapted to future climatic conditions and more tolerant to extreme climatic events is urgently required. Plant phenotyping is a crucial approach to address these challenges. High-throughput plant phenotyping (HTPP) helps to monitor the performance of improved genotypes and is one of the most effective strategies to improve the sustainability of agricultural production. In spite of the remarkable progress in basic knowledge and technology of plant phenotyping, there are still several practical, financial, and political constraints to implement HTPP approaches in field and controlled conditions across the Mediterranean. The European panorama of phenotyping is heterogeneous and integration of phenotyping data across different scales and translation of “phytotron research” to the field, and from model species to crops, remain major challenges. Moreover, solutions specifically tailored to Mediterranean agriculture (e.g., crops and environmental stresses) are in high demand, as the region is vulnerable to climate change and to desertification processes. The specific phenotyping requirements of Mediterranean crops have not yet been fully identified. The high cost of HTPP infrastructures is a major limiting factor, though the limited availability of skilled personnel may also impair its implementation in Mediterranean countries. We propose that the lack of suitable phenotyping infrastructures is hindering the development of new Mediterranean agricultural varieties and will negatively affect future competitiveness of the agricultural sector. We provide an overview of the heterogeneous panorama of phenotyping within Mediterranean countries, describing the state of the art of agricultural production, breeding initiatives, and phenotyping capabilities in five countries: Italy, Greece, Portugal, Spain, and Turkey. We characterize some of the main impediments for development of plant phenotyping in those countries and identify strategies to overcome barriers and maximize the benefits of phenotyping and modeling approaches to Mediterranean agriculture and related sustainabilityinfo:eu-repo/semantics/publishedVersio

Bidirectional optimization of the melting spinning process

This is the author's accepted manuscript (under the provisional title "Bi-directional optimization of the melting spinning process with an immune-enhanced neural network"). The final published article is available from the link below. Copyright 2014 @ IEEE.A bidirectional optimizing approach for the melting spinning process based on an immune-enhanced neural network is proposed. The proposed bidirectional model can not only reveal the internal nonlinear relationship between the process configuration and the quality indices of the fibers as final product, but also provide a tool for engineers to develop new fiber products with expected quality specifications. A neural network is taken as the basis for the bidirectional model, and an immune component is introduced to enlarge the searching scope of the solution field so that the neural network has a larger possibility to find the appropriate and reasonable solution, and the error of prediction can therefore be eliminated. The proposed intelligent model can also help to determine what kind of process configuration should be made in order to produce satisfactory fiber products. To make the proposed model practical to the manufacturing, a software platform is developed. Simulation results show that the proposed model can eliminate the approximation error raised by the neural network-based optimizing model, which is due to the extension of focusing scope by the artificial immune mechanism. Meanwhile, the proposed model with the corresponding software can conduct optimization in two directions, namely, the process optimization and category development, and the corresponding results outperform those with an ordinary neural network-based intelligent model. It is also proved that the proposed model has the potential to act as a valuable tool from which the engineers and decision makers of the spinning process could benefit.National Nature Science Foundation of China, Ministry of Education of China, the Shanghai Committee of Science and Technology), and the Fundamental Research Funds for the Central Universities

The IUCN Red List of Ecosystems: motivations, challenges, and applications

Abstract In response to growing demand for ecosystem-level risk assessment in biodiversity conservation, and rapid proliferation of locally tailored protocols, the IUCN recently endorsed new Red List criteria as a global standard for ecosystem risk assessment. Four qualities were sought in the design of the IUCN criteria: generality; precision; realism; and simplicity. Drawing from extensive global consultation, we explore trade-offs among these qualities when dealing with key challenges, including ecosystem classification, measuring ecosystem dynamics, degradation and collapse, and setting decision thresholds to delimit ordinal categories of threat. Experience from countries with national lists of threatened ecosystems demonstrates well-balanced trade-offs in current and potential applications of Red Lists of Ecosystems in legislation, policy, environmental management and education. The IUCN Red List of Ecosystems should be judged by whether it achieves conservation ends and improves natural resource management, whether its limitations are outweighed by its benefits, and whether it performs better than alternative methods. Future development of the Red List of Ecosystems will benefit from the history of the Red List of Threatened Species which was trialed and adjusted iteratively over 50 years from rudimentary beginnings. We anticipate the Red List of Ecosystems will promote policy focus on conservation outcomes in situ across whole landscapes and seascapes