Abstract
In recent years, greenhouse-based precision agriculture (PA) has been strengthened by utilization of Internet of Things applications and low-power wide area network communication. The advancements in multidisciplinary technologies such as artificial intelligence (AI) have created opportunities to assist farmers further in detecting disease and poor nutrition of plants. Neural networks and other AI techniques need an initial set of measurement campaigns along with extensive datasets as a training set to baseline and evolve different applications. This paper presents LoRaWAN-based greenhouse monitoring datasets over a period of nine months. The dataset has both the network and sensing information from multiple sensor nodes for tomato crops in two different greenhouse environments. The goal is to provide the research community with a dataset to evaluate performance of LoRaWAN inside a greenhouse and develop more efficient PA monitoring techniques. In this paper, we carried out an exploratory data analysis to infer crop growth by analyzing just the LoRaWAN signals and without inclusion of any extra hardware. This work uses a multilayer perceptron artificial neural network to predict the weekly plant growth, trained using RSSI value from sensor data and manual measurement of plant height from the greenhouse. We developed this proof of concept of joint communication and sensing by using generated dataset from the “Proefcentrum Hoogstraten” greenhouse in Belgium. Results for the proposed method yield a root mean square error of 10% in detecting the average plant height inside a greenhouse. In future, we can use this concept of landscape sensing for different supplementary use-cases and to develop optimized methods.
Highlights
In the wake of recent global warming, greenhouse farming has helped farmers to reduce investment risks [1], led by precision agriculture (PA) technologies
It lists different multilayer perceptron (MLP) use-cases, for example, agricultural dataset tested with MLP algorithm, identification of cucumber virus through MLP neural network classifier, and harvesting application through object detection feature trained on an MLP
artificial intelligence (AI) and datasets will play a crucial part in the evolution of greenhouse solutions
Summary
In the wake of recent global warming, greenhouse farming has helped farmers to reduce investment risks [1], led by precision agriculture (PA) technologies. Infections are seen as powdery white lesions on the tomato leaves and spread by the dispersion of its spores This impacts growth of a plant, fruit quality, and premature senescence. To support stringent monitoring of the greenhouse, does deployment of sensor network need to be exploited, but it is imperative to find newer strategies to examine plant health in the greenhouse One such strategy is to employ existing wireless sensing systems inside the greenhouse and tailor application actions . In this paper, we propose a proof of concept (POC) to predict weekly plant growth that can be used to derive other macroenvironmental features, such as crop health, using wireless communication signals only For this concept, we use received signal strength indicator (RSSI) of an LoRaWAN-based sensor network in the greenhouse to detect expected plant growth and answer the hypothesis questions such as prediction of weekly plant growth. It can enhance spatial representation of plants in the greenhouse to support KPI requirements
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