Paddy Growth Monitoring Using Terrestrial Laser Scanner

Laser Scanner Technology, 2012

In his essay, The New Organon, Bacon (1561Bacon ( -1626) ) wrote "So must we likewise from experience of every kind first endeavor to discover true causes and axioms; and seek for experiments of Light, not for experiments of Fruit. For axioms rightly discovered and established supply practice with its instruments, not one by one, but in clusters, and draw after them trains and troops of works." . While Bacon's use of English is a bit opaque by today's writing styles, his statements are still very relevant and hold true for any significant area of inquiry when a key discovery or application is uncovered. Therefore, this chapter endeavors to indicate how laser scanning data streams, a 'light' based technology, enable the art, practice, and implementation of diverse investigations of agricultural systems, gaining insight into the various ecological processes involved. Our goal is to provide insight for others to similarly develop their 'trains and troops of works' according to their interests, which will, in turn, enrich all investigators of agricultural systems through the spread of shared knowledge and techniques. Laser scanning data streams, when linked with multi-spectral, hyperspectral, apparent soil electro-conductivity (EC a ), or other kinds of geo-referenced data streams, aid in the creation of maps that allow useful applications in agricultural systems. These combinations of georeferenced information provide an opportunity to include several types of statistical www.intechopen.com Laser Scanner Technology 222 analyses, permitting the best interpretation of the information conveyed by such maps, and provide the capability of building new, detailed, and more informative maps. Such maps have enabled a past, present, and, probably, future explosion 'of works' leading to remarkably innovative methods for solving the problems of agricultural systems. Several illustrations are presented to demonstrate a few of the numerous kinds of applications enabled by laser scanning data streams in agriculture. These illustrations focus on a Mississippi cotton field and a Nebraska corn field. Topics considered include (1) describing an approach to statistically evaluate impacts upon production by site-specific management practices (such as seeding rate, nitrogen and potassium applications, irrigation, and other farming operations), including the assessment of interactions among these practices and the topographical characteristics of crop fields, (2) assessing the accuracy of laser scanning data products, (3) evaluating the spatial distribution of the abundance, dispersion and other characteristics of agricultural variables as abstractions of agroecological populations of interest, and (4) a partial topographical analysis of yield involving two topographical attributes: laser scanning elevation data and the shallow apparent soil electrical conductivity (EC a ) measured by the Veris ® cart (Veris Technologies, Salina, KS, USA), which is a proximal sensor system. Global Positioning System (GPS) equipped hand-held loggers are another technology useful for obtaining geo-referenced scouting information such as crop phenology, soil fertility, and pests. These 'on the ground' measurements have extremely sparse sample sizes in comparison to the very dense pixel counts and small ground spatial distances (GSD) provided by laser scanning, proximal, or remote sensing sensors . At the end of the production season, harvest yield monitors geo-spatially measure crop yield. Collectively, all of these kinds of information can be superimposed by a geographic information system (GIS) on a digital elevation surface built from a laser scanning mission of the agricultural field. Once assembled into a geo-database by additional geographic information system and remote sensing processing (de

Remote Sensing

Canopy hyperspectral (HS) sensing is a promising tool for estimating rice (Oryza sativa L.) yield. However, the timing of HS measurements is crucial for assessing grain yield prior to harvest because rice growth stages strongly influence the sensitivity to different wavelengths and the evaluation performance. To clarify the optimum growth stage for HS sensing-based yield assessments, the grain yield of paddy fields during the reproductive phase to the ripening phase was evaluated from field HS data in conjunction with iterative stepwise elimination partial least squares (ISE-PLS) regression. The field experiments involved three different transplanting dates (12 July, 26 July, and 9 August) in 2017 for six cultivars with three replicates (n = 3 × 6 × 3 = 54). Field HS measurements were performed on 2 October 2017, during the panicle initiation, booting, and ripening growth stages. The predictive accuracy of ISE-PLS was compared with that of the standard full-spectrum PLS (FS-PLS) via...

IOP Conference Series: Earth and Environmental Science

Sentinel satellite imagery using radar sensors for one particular area with the same orbit can be compared every 6 days and freely available to be downloaded. This advantage can be exploited to regularly identify land surface changes in a particular region. Paddy is a fastgrowing crop with an approximately 120-day life cycle and specific growth stages related to phenological aspect, especially to changes in plant height. Hence, paddy growth stages can be monitored using this radar sentinel satellite with dual polarization data. Radar satellite is also cloud-free and therefore suitable for tropical regions such as Indonesia. This paper describes the results of a study on paddy field observation based on a correlation analysis between backscatter values of Sentinel-1A data with paddy growth stage observation. Sentinel-1A data from January 2018 to May 2018 were downloaded and processed using SNAP software. The study observed 5 stages: early vegetative (V1), late vegetative (V2), generative (G), Harvest (P), and Land Preparation (PL). An experiment was conducted by collocating the backscatter value of pixels at the location of the paddy growth stage observations, grouping and calculating the frequency for each class and then removing the class that had small frequency. These steps were carried out by way of iterations until the condition was above 5% of the threshold. Classification was performed based on range value obtained from the experiment to create paddy growth stage polygons. Correlation value between backscatter value σ0VH and paddy growth stage observation was 0.758. While correlation value between backscatter value σ0VV and paddy growth stage observation was 0.537.

Information Processing in Agriculture, 2017

The optimum rate and application timing of Nitrogen (N) fertilizer are crucial in achieving a high yield in rice cultivation; however, conventional laboratory testing of plant nutrients is time-consuming and expensive. To develop a site-specific spatial variable rate application method to overcome the limitations of traditional techniques, especially in fields under a double-cropping system, this study focused on the relationship between Soil Plant Analysis Development (SPAD) chlorophyll meter readings and N content in leaves during different growth stages to introduce the most suitable stage for assessment of crop N and prediction of rice yield. The SPAD readings and leaf N content were measured on the uppermost fully expanded leaf at panicle formation and booting stages. Grain yield was also measured at the end of the season. The analysis of variance, variogram, and kriging were calculated to determine the variability of attributes and their relationship, and finally, variability maps were created. Significant linear relationships were observed between attributes, with the same trends in different sampling dates; however, accuracy of semivariance estimation reduces with the growth stage. Results of the study also implied that there was a better relationship between rice leaf N content (R 2 = 0.93), as well as yield (R 2 = 0.81), with SPAD readings at the panicle formation stage. Therefore, the SPAD-based evaluation of N status and prediction of rice yield is more reliable on this stage rather than at the booting stage. This study proved that the application of SPAD chlorophyll meter paves the way for real-time paddy N management and grain yield estimation. It can be reliably exploited in precision agriculture of paddy fields under double-cropping cultivation to understand and control spatial variations.

Spatial Information Research, 2019

Rice is an important staple food for the billions of world population. Mapping the spatial distribution of paddy and predicting yields are crucial for food security measures. Over the last three decades, remote sensing techniques have been widely used for monitoring and management of agricultural systems. This study has employed Sentinel-based both optical (Sentinel-2B) and SAR (Sentinel-1A) sensors data for paddy acreage mapping in Sahibganj district, Jharkhand during the monsoon season in 2017. A robust machine learning Random Forest (RF) classification technique was deployed for the paddy acreage mapping. A simple linear regression yield model was developed for predicting yields. The key findings showed that the paddy acreage was about 68.3-77.8 thousand hectares based on Sentinel-1A and 2B satellite data, respectively. Accordingly, the paddy production of the district was estimated as 108-126 thousand tonnes. The paddy yield was predicted as 1.60 tonnes/hectare. The spatial distribution of paddy based on RF classifier and accuracy assessment of LULC maps revealed that the SAR-based classified paddy map was more consistent than the optical data. Nevertheless, this comprehensive study concluded that the SAR data could be more pronounced in acreage mapping and yield estimation for providing timely information to decision makers.