Prediction of Potato Crop Yield Using Precision Agriculture Techniques

Abstract

Crop growth and yield monitoring over agricultural fields is an essential procedure for food security and agricultural economic return prediction. The advances in remote sensing have enhanced the process of monitoring the development of agricultural crops and estimating their yields. Therefore, remote sensing and GIS techniques were employed, in this study, to predict potato tuber crop yield on three 30 ha center pivot irrigated fields in an agricultural scheme located in the Eastern Region of Saudi Arabia. Landsat-8 and Sentinel-2 satellite images were acquired during the potato growth stages and two vegetation indices (the normalized difference vegetation index (NDVI) and the soil adjusted vegetation index (SAVI)) were generated from the images. Vegetation index maps were developed and classified into zones based on vegetation health statements, where the stratified random sampling points were accordingly initiated. Potato yield samples were collected 2–3 days prior to the harvest time and were correlated to the adjacent NDVI and SAVI, where yield prediction algorithms were developed and used to generate prediction yield maps. Results of the study revealed that the difference between predicted yield values and actual ones (prediction error) ranged between 7.9 and 13.5% for Landsat-8 images and between 3.8 and 10.2% for Sentinel-2 images. The relationship between actual and predicted yield values produced R2 values ranging between 0.39 and 0.65 for Landsat-8 images and between 0.47 and 0.65 for Sentinel-2 images. Results of this study revealed a considerable variation in field productivity across the three fields, where high-yield areas produced an average yield of above 40 t ha-1; while, the low-yield areas produced, on the average, less than 21 t ha-1. Identifying such great variation in field productivity will assist farmers and decision makers in managing their practices.

Using Sentinel-2 Data for Retrieving LAI and Leaf and Canopy Chlorophyll Content of a Potato Crop

“…Immitzer et al [23] confirmed the expected capabilities for producing reliable land cover maps in Central Europe at 10 m spatial resolution using pre-operational (August 2015) single date Sentinel-2 data. Al-Gaadi et al [24] used a single date Sentinel-2 image of early 2016 for potato yield prediction in irrigated fields in Saudi Arabia using an empirical relationship with vegetation indices. To our knowledge this is the first paper deriving agricultural crop variables from multi-date Sentinel-2 images.…”

A Precision Agriculture Approach for Durum Wheat Yield Assessment Using Remote Sensing Data and Yield Mapping

“…In the last years many studies have focused on the use of medium resolution satellite data (10-100 m) for yield estimation at broader spatial resolution (local, regional, country scales) even for long-term yield series analysis [20][21][22][23][24][25][26]. While studies are conducted by the use of very high resolution imagery [27][28][29][30] to identify within-field variability of crop growth and yield and for the definition of management zones, few [31] have used Sentinel-2 to provide an insight into field productivity variation for better future management [32][33][34]. The objectives of this study were to:…”

Effect of soil and water characteristics on yield and properties of ‘Spunta’ potatoes

“…Potato (Solanum tuberosum L.) is one of the most important field crops in Saudi Arabia because of its role in providing food and proteins (Hadi, 2012). Average yields vary between 21 and 40 t ha -1 (Al-Gaadi et al, 2016). Increased mechanization was one of the most influential factors in the ability to expand potato production and minimize the production cost, thus increasing the net income for potato producers (Al-Dosary, 2016).…”