Artificial Intelligence in Sustainable Vertical Farming

As global challenges of population growth, climate change, and resource scarcity intensify, the agricultural landscape is at a critical juncture. Sustainable vertical farming emerges as a transformative solution to address these challenges by maximizing crop yields in controlled environments. This paradigm shift necessitates the integration of cutting-edge technologies, with Artificial Intelligence (AI) at the forefront. The paper provides a comprehensive exploration of the role of AI in sustainable vertical farming, investigating its potential, challenges, and opportunities. The review synthesizes the current state of AI applications, encompassing machine learning, computer vision, the Internet of Things (IoT), and robotics, in optimizing resource usage, automating tasks, and enhancing decision-making. It identifies gaps in research, emphasizing the need for optimized AI models, interdisciplinary collaboration, and the development of explainable AI in agriculture. The implications extend beyond efficiency gains, considering economic viability, reduced environmental impact, and increased food security. The paper concludes by offering insights for stakeholders and suggesting avenues for future research, aiming to guide the integration of AI technologies in sustainable vertical farming for a resilient and sustainable future in agriculture

Automatic Romaine Heart Harvester

The Romaine Robotics Senior Design Team developed a romaine lettuce heart trimming system in partnership with a Salinas farm to address a growing labor shortage in the agricultural industry that is resulting in crops rotting in the field before they could be harvested. An automated trimmer can alleviate the most time consuming step in the cut-trim-bag harvesting process, increasing the yields of robotic cutters or the speed of existing laborer teams. Leveraging the Partner Farm’s existing trimmer architecture, which consists of a laborer loading lettuce into sprungloaded grippers that are rotated through vision and cutting systems by an indexer, the team redesigned geometry to improve the loading, gripping, and ejection stages of the system. Physical testing, hand calculations, and FEA were performed to understand acceptable grip strengths and cup design, and several wooden mockups were built to explore a new actuating linkage design for the indexer. The team manufactured, assembled, and performed verification testing on a full-size metal motorized prototype that can be incorporated with the Partner Farm’s existing cutting and vision systems. The prototype met all of the established requirements, and the farm has implemented the redesign onto their trimmer. Future work would include designing and implementing vision and cutting systems for the team’s metal prototype

Pecuária leiteira de precisão.

O emprego das tecnologias de pecuária de precisão estimulará novas vertentes de agregação de valor e de fabricação, com grandes possibilidades de aumento de competitividade do setor de pecuária de leite. As oportunidades ligadas à pecuária de precisão podem surgir tanto dentro como fora da porteira da fazenda. Os produtores podem beneficiar-se nas áreas de automação e tomadas de decisões mais eficientes ao fazer melhor uso dos escassos e cada vez mais onerosos recursos. O uso dessas tecnologias tornará possível, por exemplo, inferir padrões de comportamento animal, fisiológicos e sanitários e ajustar o manejo para cada indivíduo, com ganhos de eficiência operacional e econômica.bitstream/item/156447/1/Pecuaria-leiteira-de-precisao-p307-323.pd

Drones: Innovative Technology for Use in Precision Pest Management.

Arthropod pest outbreaks are unpredictable and not uniformly distributed within fields. Early outbreak detection and treatment application are inherent to effective pest management, allowing management decisions to be implemented before pests are well-established and crop losses accrue. Pest monitoring is time-consuming and may be hampered by lack of reliable or cost-effective sampling techniques. Thus, we argue that an important research challenge associated with enhanced sustainability of pest management in modern agriculture is developing and promoting improved crop monitoring procedures. Biotic stress, such as herbivory by arthropod pests, elicits physiological defense responses in plants, leading to changes in leaf reflectance. Advanced imaging technologies can detect such changes, and can, therefore, be used as noninvasive crop monitoring methods. Furthermore, novel methods of treatment precision application are required. Both sensing and actuation technologies can be mounted on equipment moving through fields (e.g., irrigation equipment), on (un)manned driving vehicles, and on small drones. In this review, we focus specifically on use of small unmanned aerial robots, or small drones, in agricultural systems. Acquired and processed canopy reflectance data obtained with sensing drones could potentially be transmitted as a digital map to guide a second type of drone, actuation drones, to deliver solutions to the identified pest hotspots, such as precision releases of natural enemies and/or precision-sprays of pesticides. We emphasize how sustainable pest management in 21st-century agriculture will depend heavily on novel technologies, and how this trend will lead to a growing need for multi-disciplinary research collaborations between agronomists, ecologists, software programmers, and engineers

Effects of Irrigation Rate and Planting Density on Maize Yield and Water Use Efficiency in the Temperate Climate of Serbia

Scarce water resources severely limit maize (Zea mays L.) cultivation in the temperate regions of northern Serbia. A two-year field experiment was conducted to investigate the effects of irrigation and planting density on yield and water use efficiency in temperate climate under sprinkler irrigation. The experiment included five irrigation treatments (full irrigated treatment – FIT; 80% FIT, 60% FIT, 40% FIT, and rainfed) and three planting densities (PD1: 54,900 plants ha–1 ; PD2: 64,900 plants ha–1; PD3: 75,200 plants ha–1). There was increase in yield with the irrigation (1.05–80.00%) as compared to the rainfed crop. Results showed that decreasing irrigation rates resulted in a decrease in yield, crop water use efficiency (WUE), and irrigation water use efficiency (IWUE). Planting density had significant effects on yield, WUE, and IWUE which differed in both years. Increasing planting density gradually increased yield, WUE, and IWUE. For the pooled data, irrigation rate, planting density and their interaction was significant (P < 0.05). The highest two-year average yield, WUE, and IWUE were found for FIT-PD3 (14,612 kg ha–1), rainfed-PD2 (2.764 kg m–3), and 60% FITPD3 (2.356 kg m–3), respectively. The results revealed that irrigation is necessary for maize cultivation because rainfall is insufficient to meet the crop water needs. In addition, if water becomes a limiting factor, 80% FIT-PD3 with average yield loss of 15% would be the best agronomic practices for growing maize with a sprinkler irrigation system in a temperate climate of Serbia

Development of an autonomous driven robotic platform used for high-throughput-phenotyping in viticulture

Der Anbau von Weinreben blickt auf eine lange Tradition zurück, die jedoch gleichzeitig im Zeichen der stetigen Weiterentwicklung steht. Bei der Züchtung neuer Rebsorten wird der Pilzwiderstandsfähigkeit eine große Bedeutung beigemessen. In der Bewirtschaftung der Rebanlagen kommen zunehmend Methoden der präzisen Landwirtschaft in Adaption zum Einsatz. Das Forschungsprojekt PHENOvines versuchte diese beiden Bereiche miteinander zu verknüpfen. Um die während der Rebenzüchtung notwendigen Phänotypisierungen zu beschleunigen und zu objektivieren, wurde die automatisierte, selbstfahrende Boniturplattform PHENObot entwickelt. Im Zentrum der vorliegenden Arbeit stehen die konzeptionellen und konstruktiven Arbeiten zur Erstellung dieser Boniturplattform, deren Navigation, sowie die Führung des Sensorsystems zur Bilddatenerfassung. Ein weiterer Bestandteil ist zudem die experimentelle Erprobungs- und Versuchsphase. Zu Beginn der Projektlaufzeit wurden Antriebskonzepte evaluiert und schließlich ein bereits auf dem Markt befindlicher, mit elektrischen Bandlaufwerken betriebener Großkistentransporter als Trägerfahrzeug ausgewählt. Die automatische Spurführung entlang, mittels NAVSTAR GPS erfasster Stockkoordinaten, konnte durch die Ausrüstung mit einem RTK-Navigationssystem sichergestellt werden. Allein die Positionsdaten werden zur Navigation genutzt. Daneben kommen Ultraschallsensoren, sowie ein mechanisches Anfahrschild zur Hindernisabtastung als Sicherheitseinrichtungen zum Einsatz. Bedient wird das Bonitursystem über die eigens entwickelte Steuerungsapplikation PHENObotControl 1.0. Dazu sind textbasierte Jobdateien notwendig, deren Erzeugung anhand der Stockkoordinaten mittels skriptbasierter Transformation und Verarbeitung in der GIS-Anwendung GRASS GIS erfolgt. Der Boniturvorgang ist gegliedert in Anfahrt zum Haltepunkt direkt vor dem Rebstock, Nivellierung des Multi-Kamerasystem (MKS), Bildauslösung, Bildspeicherung und Weiterfahrt zum nächsten Stock. Die Nivellierung wird anhand der Daten eines Neigungssensors in einem Lageregelungssystem mit vier möglichen Freiheitsgraden durchgeführt. Bei Erreichen der vorgegebenen Position sendet das Navigationssystem einen Auslösebefehl mit Positions- und Identifikationsdaten an das Bilderfassungssystem, das neben fünf Kamers unterschiedlicher Wellenlängenbereiche auch ein LED-Beleuchtungssystem enthält. Zur Gewährleistung der Objektivität und gleichbleibender Bildqualität findet die Bonitur vornehmlich bei Dunkelheit oder neutralen Lichtbedingungen statt. Die Energie für den elektrischen Antrieb wird in einem Akkumulatorenpaket bereitgehalten, das zusätzlich über einen Generator nach dem Prinzip des Hybridantriebs wiederaufgeladen werden kann. In der Erprobungsphase wurden alle Funktionen des PHENObot getestet und weiterentwickelt. Messfahrten dienten zur Feststellung und Quantifizierung der auftretenden Positionsfehler und deren Quellen. Im Mittel waren die transversalen Positionsabweichungen quer zur Fahrtrichtung bei der Bonitur kleiner als 50 mm. Dies ist ausreichend genau genug, um das MKS optimal zur Bildaufnahme vor einem Rebstock ausrichten zu können. Ein Anwendungsversuch in zwei aufeinanderfolgenden Nächten beendete die Versuchsphase am Standort Siebeldingen. Dabei wurden Bildinformationen von 2726 Rebstöcken bei einer Boniturleistung von gut 280 Stöcken pro Stunde oder knapp 600 m² h-1 erhoben. Im Vergleich zur manuellen Bonitur des Parameters Ertrag konnte somit die Leistung um das knapp 19 fache gesteigert werden. Darüber hinaus liegen durch die aufgenommenen Bilder weitere Informationen zur Beurteilung anderer Parameter vor. In der zusätzlich zur Verfügung stehenden Zeit kann sich der Anwender der Auswertung und Interpretation der gewonnenen phänotypischen Daten widmen. Somit kann ein Beitrag zur Reduzierung der Zuchtdauer einer neuen Rebsorte geleistet werden.The cultivation of grape vines has a long tradition, whilst at the same time being subject to continuous development. When developing new strains of grape vines, great importance is attached to fungal resistance. Vineyard management is also making increased use of adapted precision agriculture methods. The PHENOvines research project was an attempt to link these two trends. The PHENObot automated, self-propelled plant assessment platform was developed for the purpose of accelerating and objectifying the phenotyping required in vine cultivation. The focus of the current work is the design and construction of the assessment platform, its navigation, and the guidance of the sensor system used for image data acquisition. Another element is the experimental trial and testing phase. Drive system designs were evaluated at the beginning of the project. From these, a commercially available bulk bin transporter with electrically propelled caterpillar tracks was selected as the carrier vehicle. The equipment included an RTK navigation system to ensure reliable automatic tracking along the vine coordinates determined by NAVSTAR GPS. Navigation is based solely on the position data. The equipment also includes safety features such as ultrasound sensors and a mechanical, obstacle-sensing collision protector. The plant assessment system is operated via the PHENObotControl 1.0 purpose-designed control software. The software works with text-based job files, which are created using the vine coordinates via script-based transformation and processing in the GIS application, GRASS GIS. The plant assessment procedure is structured as follows: the machine travels to a point directly in front of the vine; the multi-camera system (MKS) is levelled; the image is captured and saved; the machine continues to the next vine. The levelling process is carried out using data from a tilt sensor in a position control system with four possible degrees of freedom. Once the machine has reached the specified position, the navigation system sends a signal (including position and identification data) that triggers the image acquisition system. In addition to five cameras with different wavelength ranges, this system is equipped with LED lighting. In order to ensure objectivity and consistent image quality, the plant assessment procedure usually takes place either in the dark or in neutral lighting conditions. The power for the electric drive system is stored in a battery pack, which can also be recharged via a generator that works on the same principle as a hybrid drive system. All of the PHENObot´s functions were tested and refined during the trial phase. Test runs were used to assess and quantify positioning errors and their causes. During the plant assessment, the transversal positioning deviations perpendicular to the direction of travel were on average under 50 mm. This is sufficiently precise to enable the multi-camera system to be positioned accurately in front of a grape vine for image capturing purposes. Tests carried out on two consecutive nights completed the trial phase at the Siebeldingen site. During the tests, 2,726 images of grape vines were collected at a rate of at least 280 vines per hour, or approximately 600 m² h 1. In comparison with the manual alternative, this represents an approximately 19-fold increase in speed in assessing the grapes. The images also provide additional information that can be used to evaluate other parameters. Not only that, but users can devote the time they saved to evaluating and interpreting the phenotypical data collected. The process can thus help to reduce the time taken to cultivate new grape varieties

2014, UMaine News Press Releases

This is a catalog of press releases put out by the University of Maine Division of Marketing and Communications between January 6, 2014 and December 31, 2014

2023, UMaine News Press Releases

This is a catalog of press releases put out by the University of Maine Division of Marketing and Communications between January 3, 2023 and November 1, 2023