<p>There is a growing demand for indoor farm management systems that can track plant growth, allow automatic control and aid in real-time decision making. Internet of Thing (IoT)-based solutions are being applied to meet these needs and numerous researchers have created prototypes for meeting specific needs using sensors, algorithms, and automations. However, limited studies are available that report on comprehensive large-scale experiments to test various aspects related to availability, scalability and reliability of sensors and actuators used in low-cost indoor farms. The purpose of this study was to develop a low-cost, IoT devices driven indoor farm as a testbed for growing microgreens and other experimental crops. The testbed was designed using off-the-shelf sensors and actuators for conducting research experiments, addressing identified challenges, and utilizing remotely acquired data for developing an intelligent farm management system. The sensors were used for collecting and monitoring electrical conductivity (EC), pH and dissolved oxygen (DO) levels of the nutrient solution, light intensity, environmental variables, and imagery data. The control of light emitting diodes (LEDs), irrigation pumps, and camera modules was carried out using commercially available components. All the sensors and actuators were remotely monitored, controlled, and coordinated using a cloud-based dashboard, Raspberry Pis, and Arduino microcontrollers. To implement a reliable, real-time control of actuators, edge computing was used as it helped in minimizing latency and identifying anomalies.</p> <p>Decision making about overall system performance and harvesting schedule was accomplished by providing alerts on anomalies in the sensors and actuators and through installation of cameras to predict yield of microgreens, respectively. A split-plot statistical design was used to evaluate the effect of lighting, nutrition solution concentration, seed density, and day of harvest on the growth of microgreens. This study complements and expands past efforts by other researchers on building a low cost IoT-based indoor farm. While the experience with the testbed demonstrates its real-world potential of conducting experimental research, some major lessons were learnt along the way that could be used for future enhancements.</p>

This study aimed to develop a novel end-to-end plant diagnosis model for the analysis of plant health conditions in near real-time to optimize the rate of production on farmlands for an intensive, yet environmentally safe farming production to preserve the natural environment. First, field research was conducted to determine the extent of the problems faced by farmers in agricultural production. This allowed us to refine the research statement and the level of technology involved in the production processes. The advantages of unmanned aerial systems were exploited in the continuous monitoring of farm plantations to develop automated and accurate measures of farm conditions. To this end, this thesis applies the Precision Agricultural technology as a data based management system that takes into account spatial variations by using the Global Positioning System, Geographical Information System, remote sensing, yield monitors, mapping, and guidance system for variable rate applications. An unmanned aerial vehicle embedded with an optic and radiometric sensor was used to obtain high spectral resolution images of plantation status during normal production/growth cycle. Then, an ensemble of classifiers with Convolution Neural Networks (CNN) was used as off the shelf feature extractor to train images to develop an end-to-end feature detection and multiclass classification system for plant overall health’s conditions. Whereby previous works have concentrated on using CNN as off the shelf feature extractor and model training to detect only plant diseases from plants. To date, no research has yet been carried out to develop an end-to-end model for the overall plant diagnosis system. Previous studies focused on the detection of diseases at any given time, making it difficult to implement comprehensive real-time PA systems. Applying the pretrained model to the new images showed that the model can accurately predict any plant condition with an average of 97% accuracy.

Landtechnik der Zukunft Die fortschreitende Digitalisierung betrifft sämtliche Lebensbereiche. Als Chance und Herausforderung zugleich geht es momentan darum auch in der Landwirtschaft Nutzen und Ziele zu definieren, Voraussetzungen zur Einführung zu schaffen und Anwender und Verbraucher auf dem Weg mitzunehmen. Die Veranstaltung Landtechnik der Zukunft, beleuchtete diese Themen am 23. Januar 2018 in der Vertretung des Freistaates Sachsen beim Bund in Berlin. Zukunftstechnologien und deren Praxisanforderungen wurden mit renommierten Referenten interaktiv diskutiert und auch dokumentiert. Szenarien einer digitalisierten und nachhaltigen Landwirtschaft gekoppelt mit aktuellen Ernährungstrends prägten die Veranstaltung, deren Teilnehmer querbeet aus Industrie, Politik und Hochschule kamen. Schwarmtechnologien wie auch die echtzeitfähige Funkvernetzung für Arbeitsmaschinen und -prozesse erzeugten viele Nachfragen, wobei Technikentwicklung im ökologischen Landbau und Technikentwicklung für Nachhaltigkeit aus der Sicht der Hersteller im Kern des Interesses waren.

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