Yield prediction map generation and analysis.

<p>Yield prediction map generation and analysis.</p

The variation in productivity classes of (a) Landsat-8 and (b) Sentinel-2 yield maps.

<p>The variation in productivity classes of (a) Landsat-8 and (b) Sentinel-2 yield maps.</p

Histogram of potato yield for (a) pivot 67-S from Landsat-8, (b) pivot 67-S from Sentinel-2, (c) pivot 68-S from Landsat-8, (d) pivot 68-S from Sentinel-2, (e) pivot 44-S from Landsat-8 and (f) pivot 44-S from Sentinel-2.

<p>Histogram of potato yield for (a) pivot 67-S from Landsat-8, (b) pivot 67-S from Sentinel-2, (c) pivot 68-S from Landsat-8, (d) pivot 68-S from Sentinel-2, (e) pivot 44-S from Landsat-8 and (f) pivot 44-S from Sentinel-2.</p

Prediction of Potato Crop Yield Using Precision Agriculture Techniques

<div><p>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.</p></div

Model validation and performance indicators.

<p>Model validation and performance indicators.</p

Descriptive statistics of the actual potato yield.

<p>Descriptive statistics of the actual potato yield.</p

Total predicted vs. actual yields.

<p>Total predicted vs. actual yields.</p

Maps of predicted potato yield for (a) pivot 67-S using CSAVI from Landsat-8, (b) pivot 67-S using SAVI from Sentinel-2, (c) pivot 68-S using NDVI from Landsat-8, (d) pivot 68-S using SAVI from Sentinel-2, (e) pivot 44-S using SAVI from Landsat-8 and (f) pivot 44-S using SAVI from Sentinel-2.

<p>Maps of predicted potato yield for (a) pivot 67-S using CSAVI from Landsat-8, (b) pivot 67-S using SAVI from Sentinel-2, (c) pivot 68-S using NDVI from Landsat-8, (d) pivot 68-S using SAVI from Sentinel-2, (e) pivot 44-S using SAVI from Landsat-8 and (f) pivot 44-S using SAVI from Sentinel-2.</p

Location of the study fields.

<p>Location of the study fields.</p