In-field Experiments for Performance Evaluation of a New Low-Cost Active Multispectral Crop Sensor

Aristotelis C. Tagarakis (), Marko Kostić (), Natasa Ljubičić (), Bojana Ivošević (), Goran Kitić () and Miloš Pandžić ()
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Aristotelis C. Tagarakis: University of Novi Sad
Marko Kostić: University of Novi Sad
Natasa Ljubičić: University of Novi Sad
Bojana Ivošević: University of Novi Sad
Goran Kitić: University of Novi Sad
Miloš Pandžić: University of Novi Sad

A chapter in Information and Communication Technologies for Agriculture—Theme I: Sensors, 2022, pp 305-325 from Springer

Abstract: Abstract Recent developments in agricultural technologies have made available for use by the farmers a variety of sensors and sensing services. Remote sensing has become particularly popular especially after the release of free satellite images form several vendors across the globe. In addition, the use of unmanned aerial systems (UAS) equipped with diverse optical sensors is getting very popular for field scouting and mapping applications in agriculture since the unmanned aerial vehicles (UAV) have become cost-affordable to almost any farmer. To many farmers, the UAVs equipped with optical sensing systems seem like hi-tech toys which can offer detailed insight of in-field hotspots. However, most satellite and UAV derived observations are based on passive sensing systems and require high level data pre-processing before used in the field. Therefore, the data processing requirements work as a constraint for most farmers, while the limitations of the passive sensing systems that are affected by the weather and atmospheric conditions, make them unpractical when on-the-go farming applications, such as variable rate spraying or fertilizing, are needed. During the past decades, active proximal sensing has been increasingly used to provide information about canopy properties and take real-time decisions in a large range of crops. Numerous proximal sensing instruments have been developed and are commercially available. However, there are several limitations in the use of most of these devices, such as high complexity in the operation and data processing, high cost, poor accuracy, etc., that work as barriers in the adoption of these devices by small and medium size farms. Therefore, there is still room for new advancements in the development of new more cost effective and farmer friendly proximal sensing solutions. In this study a new low cost, active multispectral optical device named Plant-O-Meter was tested in real conditions comparing it with the well-proven GreenSeeker handheld device. The latter sensor is a widely used commercial canopy sensor well-accepted both by the farmers and the scientific community. It was selected as a reference sensor in the study as it works using the same operating principles, is relatively low cost and has similar measuring characteristics to the Plant-O-Meter. The study took place at two experimental fields cultivated with maize (Zea mays L.) using a randomized complete block design with three replications. Nitrogen (N) fertilization rate experiments were set in order to create variations in canopy development, vigor and greenness across the fields, providing the ability to compare sensors’ detectability and other performance characteristics in simulated field conditions. Thus, a wide range of sensor readings, from very low to very high, was expected. Treatments included five nitrogen (N) fertilization rates (0, 50, 100, 150 and 200 kg of N ha−1) applied during sowing. Three maize hybrids were scanned for Normalized Difference Vegetation Index (NDVI) using both Plant-O-Meter and GreenSeeker sensors at V4, V6 and V8 growth stages. During full maturity, the central part of each plot was hand-harvested for grain (two middle rows 6 m long). Based on the present findings, the optimum timing for scanning using GreenSeeker or Plant-O-Meter was between V7 and V8 stage. Measuring within this growth stage window good estimation of end-of-season yield was achieved. In addition, the overall results indicated that NDVI obtained using GreenSeeker were quite similar to the NDVI measured by the Plant-O-Meter showing an almost 1:1 relationship. These results indicate that Plant-O-Meter exhibits strong potential for accurate plant canopy measurements and for real time variable rate fertilization applications in maize.

Keywords: Proximal sensors; Multispectral; Crop sensing; Yield estimation (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/978-3-030-84144-7_13

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