DOI: 10.6084/m9.figshare.23994324.v1
title: Incorporating IoT Sensor Data and Physical parameter data for Maize Crop Yield Analysis.

description: The provided dataset was collected using a specially designed solar-powered Arduino IoT sensor device that features cloud API functionalities, facilitating continuous data logging and physical parameter measurements. This data collection process spanned an entire planting season within an experimental farm. The primary objective of the experimental farm was to study three distinct maize varieties, namely V1 (DMR-ESRY), V2 (BR9928 DMRSR), and V3 (ART-98-SW-1), which correspond respectively to DMR-ESRY (NCRI/IITA, 1991), BR9928 DMRSR (IITA, 2009), and ART-98-SW-1 (I.A.R. & T., 2001). These maize varieties were subjected to three different soil treatments across fields denoted as F1, F2, and F3.Field F1 was designated as the control field, representing the natural soil of the experimental farmland without any modifications. Field F2 encompassed areas treated with poultry manure, applied a week before planting at a rate of fifteen (15) tons per hectare (ha), equivalent to eighty (80) kilograms (kg) of Nitrogen (N) per hectare (ha), with careful consideration of pre-application manure analysis. Field F3 consisted of sections treated with NPK fertilizer, applied according to the NPK formula (400 kg NPK 20-10-10 per hectare [ha]), translating to eighty kilograms (80kg) of Nitrogen (N), forty kilograms (40kg) of phosphorous (P), and forty kilograms (40kg) of potassium (K).To manage variations stemming from diverse treatments and replicates, the chosen experimental design was the randomized complete block design (RCBD), as outlined by Anderson & McLean (2019) and Grant (2010). This approach ensured effective control over experimental factors. An available collection of weekly images captured during the experiment can be accessed at the provided link (https://doi.org/10.6084/m9.figshare.23972252.v2).The dataset includes several components:Soil-Environmental-Data: Data collected and stored in the c2snet cloud (https://iot.c2snet.org/data/data.php) via the specially designed solar-powered Arduino IoT sensor device throughout the experimental period.Physical Data: Daily measurements of maize growth physical parameters obtained from the experimental farm.Yield-Data: Measured and computed post-harvest yield data.MergedData2: A unified dataset obtained through the application of an inner merging technique to the three initial datasets.Furthermore, accompanying the dataset are the Python notebook code used for data merging and visual images depicting portions of the dataset. References:Anderson, V. L., & McLean, R. A. (2019). Randomized Complete Block Design (RCBD). In Design ofGrant, T. (2010). The Randomized Complete Block Design (RCBD). Crop Science, 1–12.I.A.R. & T. (2001). ART-98-SW-1 Maize (Zea mays) - Nigerian Seed Portal Initiative. https://www.seedportal.org.ng/variety.php?keyword=&category=&varid=203&cropid=7&task=viewIITA. (2009). BR9928 DMRSR - Maize (Zea mays) - Nigerian Seed Portal Initiative. https://www.seedportal.org.ng/variety.php?keyword=&category=&varid=218&cropid=7&task=viewNCRI/IITA. (1991). DMR-ESRY Maize (Zea mays) - Nigerian Seed Portal Initiative. https://www.seedportal.org.ng/variety.php?keyword=&category=&varid=191&cropid=7&task=viewOlayinka, Akinola Samson; Olayinka, Tosin Comfort; Adetunmbi, Adebayo Olusola; Obe, Olayinka Olumide; Ibam, Emmanuel Onwuka; Ogedegbe, Sunday; et al. (2023). Weekly Progression Images of Maize Growth in an Experimental Field in Benin City, Nigeria. figshare. Media. https://doi.org/10.6084/m9.figshare.23972252.v2<br>