Estimating Cotton Stand Count Using UAV-Based Imagery

Remote Sensing, 2015

Unmanned Aerial Vehicles (UAV)-based remote sensing offers great possibilities to acquire in a fast and easy way field data for precision agriculture applications. This field of study is rapidly increasing due to the benefits and advantages for farm resources management, particularly for studying crop health. This paper reports some experiences related to the analysis of cultivations (vineyards and tomatoes) with Tetracam multispectral data. The Tetracam camera was mounted on a multi-rotor hexacopter. The multispectral data were processed with a photogrammetric pipeline to create triband orthoimages of the surveyed sites. Those orthoimages were employed to extract some Vegetation Indices (VI) such as the Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Soil Adjusted Vegetation Index (SAVI), examining the vegetation vigor for each crop. The paper demonstrates the great potential of high-resolution UAV data and photogrammetric techniques applied in the agriculture framework to collect multispectral images and OPEN ACCESS Remote Sens. 2015, 7 4027 evaluate different VI, suggesting that these instruments represent a fast, reliable, and cost-effective resource in crop assessment for precision farming applications.

Journal of Applied Remote Sensing, 2015

Effective methods are needed for timely areawide detection of regrowth cotton plants because boll weevils (a quarantine pest) can feed and reproduce on these plants beyond the cotton production season. Airborne multispectral images of regrowth cotton plots were acquired on several dates after three shredding (i.e., stalk destruction) dates. Linear spectral unmixing (LSU) classification was applied to high-resolution airborne multispectral images of regrowth cotton plots to estimate the minimum detectable size and subsequent growth of plants. We found that regrowth cotton fields can be identified when the mean plant width is ∼0.2 m for an image resolution of 0.1 m. LSU estimates of canopy cover of regrowth cotton plots correlated well (r 2 ¼ 0.81) with the ratio of mean plant width to row spacing, a surrogate measure of plant canopy cover. The height and width of regrowth plants were both well correlated (r 2 ¼ 0.94) with accumulated degree-days after shredding. The results will help boll weevil eradication program managers use airborne multispectral images to detect and monitor the regrowth of cotton plants after stalk destruction, and identify fields that may require further inspection and mitigation of boll weevil infestations. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Remote Sensing, 2018

Corn (Zea mays L.) is one of the most sensitive crops to planting pattern and early-season uniformity. The most common method to determine number of plants is by visual inspection on the ground but this field activity becomes time-consuming, labor-intensive, biased, and may lead to less profitable decisions by farmers. The objective of this study was to develop a reliable, timely, and unbiased method for counting corn plants based on ultra-high-resolution imagery acquired from unmanned aerial systems (UAS) to automatically scout fields and applied to real field conditions. A ground sampling distance of 2.4 mm was targeted to extract information at a plant-level basis. First, an excess greenness (ExG) index was used to individualized green pixels from the background, then rows and inter-row contours were identified and extracted. A scalable training procedure was implemented using geometric descriptors as inputs of the classifier. Second, a decision tree was implemented and tested using two training modes in each site to expose the workflow to different ground conditions at the time of the aerial data acquisition. Differences in performance were due to training modes and spatial resolutions in the two sites. For an object classification task, an overall accuracy of 0.96, based on the proportion of corrected assessment of corn and non-corn objects, was obtained for local (per-site) classification, and an accuracy of 0.93 was obtained for the combined training modes. For successful model implementation, plants should have between two to three leaves when images are collected (avoiding overlapping between plants). Best workflow performance was reached at 2.4 mm resolution corresponding to 10 m of altitude (lower altitude); higher altitudes were gradually penalized. The latter was coincident with the larger number of detected green objects in the images and the effectiveness of geometry as descriptor for corn plant detection.

Crop Science, 2010

RESEARCH R emote sensing off ers a practical solution to the need for largescale plant growth and health estimates. Current remote sensing platforms such as satellites, airplanes, and ground-based platforms can be used to obtain fi eld-scale imagery (

This investigation quantified the efficacy of visible and color-infrared images for weed mapping in maize fields by using remote images captured with an unmanned aerial vehicle at several flight altitudes (from 40 to 100 m). Results on weed discrimination were significantly affected by type of image and by type of weed (broad-leaved or grassy weeds). With independence of spatial resolution, weed coverage was underestimated in the visible images, mostly in the parts of the field infested with grassy weeds. On the contrary, analysis done with the color-infrared images was highly precise in the images captured at 40-m flight altitude, although progressively overestimated weed coverage at higher altitude.

Precision phenotyping, especially the use of image analysis, allows researchers to gain information on plant properties and plant health. Aerial image detection with unmanned aerial vehicles (UAVs) provides new opportunities in precision farming and precision phenotyping. Precision farming has created a critical need for spatial data on plant density. The plant number reflects not only the final field emergence but also allows a more precise assessment of the final yield parameters. The aim of this work is to advance UAV use and image analysis as a possible high-throughput phenotyping technique. In this study, four different maize cultivars were planted in plots with different seeding systems (in rows and equidistantly spaced) and different nitrogen fertilization levels (applied at 50, 150 and 250 kg N/ha). The experimental field, encompassing 96 plots, was overflown at a 50-m height with an octocopter equipped with a 10-megapixel camera taking a picture every 5 s. Images were recorded between BBCH 13-15 (it is a scale to identify the phenological development stage of a plant which is here the 3-to 5-leaves development stage) when the color of young leaves differs from older leaves. Close correlations up to R 2 = 0.89 were found between in situ and image-based counted plants adapting a decorrelation stretch contrast enhancement procedure, which enhanced color differences in the images. On average, the error between visually and digitally counted plants was ≤5%. Ground cover, as determined by analyzing green pixels, ranged between 76% and 83% at these stages. However, the correlation between ground cover and digitally counted plants was very low. The presence of weeds and blurry effects on the images represent possible errors in counting plants. In conclusion, the final field emergence of maize can rapidly be assessed and allows more precise assessment of the final yield parameters. The use of UAVs and image processing has the potential to optimize farm management and to support field experimentation for agronomic and breeding purposes.

“Agriculture for Life, Life for Agriculture” Conference Proceedings, 2018

The paper aims to evaluate the potential of using aerial images captured from UAV’s, when compared with satellite images, for the purpose of monitoring rapeseed cultures. For this, a comparison is presented; two modern methods of gathering and processing images with the purpose of estimating the rapeseed culture yields, in contrast with the classic method. For this purpose, we use professional equipment to gather images, GPS RTK to provide advanced precision, as well as software such as Precision Flight to plan the flight routes, Precision Mapper to construct the orthophoto, custom made Python programs and LeoWorks to process and classify the resulting images.

J. Cotton Sci, 2001

Remote sensing can be a relatively inexpensive source of data for site-specific crop management and for addressing research questions concerning spatial variability of fields. Many of the potential research and management applications of remote sensing are tactical; that is, they involve responses to particular conditions or situations that arise during the course of the season. Examples include insect pest management, weed management, and irrigation scheduling. Other potential applications, however, involve strategic questions: that is, information concerning the integrated whole of the crop production system.

Semina: Ciências Agrárias, 2019

Determination of vegetation cover index under different soil management systems of cover plants by using an unmanned aerial vehicle with an onboard digital photographic camera Determinação do índice de cobertura vegetal em sistemas de manejo do solo com plantas de cobertura, utilizando veículo aéreo não tripulado com câmera fotográfica digital embarcada Abstract The permanent monitoring of vegetation cover is important to guarantee a sustainable management of agricultural activities, with a relevant role in the reduction of water erosion. This monitoring can be carried out through different indicators such as vegetation cover indices. In this study, the vegetation cover index was obtained using uncalibrated RGB images generated from a digital photographic camera on an unmanned aerial vehicle (UAV). In addition, a comparative study with 11 vegetation indices was carried out. The vegetation indices CIVE and EXG presented a better performance and the index WI presented the worst performance in the vegetation classification during the cycles of jack bean and millet, according to the overall accuracy and Kappa coefficient. Vegetation indices were effective tools in obtaining soil cover index when compared to the standard Stocking method, except for the index WI. Architecture and cycle of millet and jack bean influenced the behavior of the studied vegetation indices. Vegetation indices generated from RGB images obtained by UAV were more practical and efficient, allowing a more frequent monitoring and in a wider area during the crop cycle. Resumo O monitoramento permanente da cobertura vegetal é importante para garantir o manejo sustentável das atividades agrícolas, com relevante papel na redução da erosão hídrica. Este monitoramento pode ser realizado por meio de diferentes indicadores, como os índices de cobertura vegetal. Nesse artigo o índice de cobertura de vegetação foi obtido usando imagens RGB não-calibradas, geradas a partir de câmera fotográfica digital embarcada em um veículo aéreo não tripulado (VANT). Além disso, foi feito um estudo comparativo de 11 índices de vegetação. Os índices de vegetação CIVE e EXG apresentaram melhor desempenho e o índice WI apresentou o pior desempenho na classificação da vegetação durante o ciclo das culturas de feijão-de-porco e milheto, conforme a acurácia global e o coeficiente Kappa. Os

Remote Sensing Applications: Society and Environment, 2016

Although multispectral remote sensing using consumer-grade cameras has successfully identified fields of small cotton plants, improved detection sensitivity is needed to identify individual or small clusters of cotton plants that also can provide habitat for boll weevils. The imaging sensor of consumer-grade cameras is based on a Bayer pattern, which alternates red, green, and blue filters over individual sensor pixels. However, each pixel of the imaging sensor of consumer-grade cameras represents direct measurement of only one of the three spectral bands (red, green, and blue) and interpolation of the remaining two spectral bands. We present an analytical technique in which endmember sets were derived from bimodal histograms of each spectral band for cotton, other vegetation types and soil, and linear spectral unmixing was used to identify individual cotton plants. We achieved significant misclassification rates as low as 0.125 and 0.146 in frequently tilled plots for validation tests of remote sensing identification of volunteer okraleaf cotton plants and volunteer conventional cotton plants, respectively. Results of this study indicate that consumer-grade cameras can acquire multispectral images of sufficient quality to detect individual cotton plants at an early growth stage, which will aid boll weevil eradication programs in identifying and locating volunteer plants.