Automatic delineation algorithm for site-specific management zones based on satellite remote sensing data

Abstract

In light of the increasing demand for food production, climate change challenges for agriculture, and economic pressure, precision farming is an ever-growing market. The development and distribution of remote sensing applications is also growing. The availability of extensive spatial and temporal data-enhanced by satellite remote sensing and open-source policies-provides an attractive opportunity to collect, analyze and use agricultural data at the farm scale and beyond. The division of individual fields into zones of differing yield potential (management zones (MZ)) is the basis of most offline and mapoverlay precision farming applications. In the process of delineation, manual labor is often required for the acquisition of suitable images and additional information on crop type. The authors therefore developed an automatic segmentation algorithm using multi-spectral satellite data, which is able to map stable crop growing patterns, reflecting areas of relative yield expectations within a field. The algorithm, using RapidEye data, is a quick and probably low-cost opportunity to divide agricultural fields into MZ, especially when yield data is insufficient or non-existent. With the increasing availability of satellite images, this method can address numerous users in agriculture and lower the threshold of implementing precision farming practices by providing a preliminary spatial field assessment.

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

“…This is also facilitated by the fact that, even if multiple surveys are done during crop development, NDVI and yield are strongly correlated at stem elongation and heading stages, which are among the most important for agronomic management to support and improve durum wheat yield and quality. Unfortunately, all the empirical relationships determined over the whole study period cannot be applied elsewhere, since a universal conversion from vegetation indices to yield values does not exist, as pointed out by Georgi et al [9]. Many efforts have been made to determine this relationship [17,70,71], with results indicating that replicability is mostly limited by crop type and climate zone, confirming our case study findings.…”

“…The low and non-significant relationship in late May is probably due to the fact that the drying process had already started unevenly in some areas of the field [33]. Unfortunately, all the empirical relationships determined over the whole study period cannot be applied elsewhere, since a universal conversion from vegetation indices to yield values does not exist, as pointed out by Georgi et al [9]. Many efforts have been made to determine this relationship [17,70,71], with results indicating that replicability is mostly limited by crop type and climate zone, confirming our case study findings.…”

“…In light of this, it is increasingly urgent to provide information to optimize crop management and estimate crop yields before harvest for a sustainable agricultural income and ensuring food security. Precision agriculture (PA) has been used for ≥25 years to optimize the use of farm inputs such as fertilizers and herbicides [8,9], and thus maximize profit and minimize negative environmental impacts [10] by addressing spatial variability.…”

“…During the same period many studies have been published using satellite imagery to estimate crop parameters and yields [17][18][19], many of these using empirical relationships between yields and various vegetation indices (VIs) with limited applicability to different areas or years [9], especially in the recent era of prolific satellite data availability [20].…”

A New CNN-Bayesian Model for Extracting Improved Winter Wheat Spatial Distribution from GF-2 imagery

“…As extraction of crop spatial distribution mainly relies on pixel-based image classification, correctly determining pixel features for accurate classification is the basis for this approach [9][10][11][12].The spectral characteristics of low-and middle-resolution remote sensing images are usually stable. Vegetation indexes are generally used as pixel features in studies using data from sources including the Moderate Resolution Imaging Spectroradiometer (MODIS) [6,[13][14][15][16], Enhanced Thematic Mapper/Thematic Mapper [13,17], and Systeme Probatoire d' Observation de la Terre [7,10]. These indices include the normalized difference vegetation index (NDVI) [5,6,[13][14][15], relationship analysis of NDVI [8], and enhanced vegetation index (EVI) [3,18], which are extracted from band values.…”