Crop and Weed Leaf Area Index Mapping Using Multi-Source Remote and Proximal Sensing

Abstract

Site specific management rationalizes farm inputs and mitigate environmental impacts. Traditionally, low resolution satellite imagery and soil maps are employed for site specific decisions in large scale farms. However, these approaches are not good at sub-field level due to low spatial resolution. To overcome this problem, either manual scouting is employed or extensive high resolution data collection platforms are used. In both cases, the cost outweighs the expected returns. Consequently, variable rate applications are not preferred in large fields. Leaf Area Index (LAI) is a useful measure to monitor crop growth and health for site specific management. In this paper we propose an accurate and scalable process where multispectral remote sensing and proximal sensing data is used to estimate LAI. Crop LAI (CLAI) and Weed LAI (WLAI) are estimated from limited high resolution ground image samples using semantic segmentation. These limited LAIs are extended to the whole field using remote sensing and proximal sensing data. We find that LAIs are spatially related with Soil, Water and Topography (SWAT) maps and are field specific. With increasing weed population in the fields, correlation of WLAI with the SWAT zone increases. However, CLAI remains comparatively consistent across SWAT zones due to variable rate seeding and fertilizer application based on soil variance. Our results demonstrate that LAIs can be predicted accurately from limited high resolution ground imagery, satellite imagery, SWAT, and soil properties maps.

Deep Learning-Based Estimation of Crop Biophysical Parameters Using Multi-Source and Multi-Temporal Remote Sensing Observations

“…The direct method consists of a ground measuring of the plant's parameters. These methods are usually destructive, costly, timeconsuming, and complicated [5]. The information extracted from remote sensing data is non-destructive and significantly reduces time and cost.…”

“…Considerable researches have been conducted to estimate various crop parameters using satellite Earth observations, including RADARSAT-2 [1,17,[19][20][21], RapidEye [5,19,20,22,23], Sentinel-1 [7,17,[24][25][26][27][28], Sentinel-2 [7,25,[29][30][31][32], Landsat-5 Thematic Mapper (TM) [33,34], Landsat-7 Enhanced Thematic Mapper Plus (ETM+) [34][35][36], Landsat-8 Operational Land Imager (OLI) [7,17,31,32,[35][36][37], Worldview-2/3 [17,27,28,38,39], and MODIS [40,41].…”

Emerging Line of Research Approach in Precision Agriculture: An Insight Study

“…The majority of the approaches associated with Geomapping and remote sensing are associated with capturing the field image followed by performing analysis. Proximal sensing is most recently integrated with remote sensing from multiple sources to study the leaf area index (Asad et al [47]). This work connects the health statistics of the leaf with the topographical map of the earth.…”

DeepVeg: Deep Learning Model for Segmentation of Weed, Canola, and Canola Flea Beetle Damage

“…Visual aspects and types of damages depend on the disease whereas the damage by flea beetle in canola plant can be observed as small yellowing tissue damages and shallow pits [22]. Several computer-aided solutions are reported for detection or segmentation of weed and crop [23]- [27]. These segmentation approaches can be categorised into classical image processing, supervised machine learning and unsupervised machine learning.…”