Detection and Counting of Tassels for Maize Crop Monitoring using Multispectral Images

Abstract. The application of UAV-based aerial imagery has advanced exponentially in the past two decades. This can be attributed to UAV operational flexibility, ultra-high spatial resolution, inexpensiveness, and UAV-based sensors enhancement. Nonetheless, the application of multitemporal series of multispectral UAV imagery still suffers significant misregistration errors, and therefore becoming a concern for applications such as precision agriculture. Direct image georeferencing and co-registration is commonly done using ground control points; this is usually costly and time consuming. This research proposes a novel approach for automatic co-registration of multitemporal UAV imagery using intensity-based keypoints. The Speeded Up Robust Features (SURF), Binary Robust Invariant Scalable Keypoints (BRISK), Maximally Stable Extremal Regions (MSER) and KAZE algorithms, were tested and parameters optimized. Image matching performance of these algorithms informed the decision to pursue further experiments with only SURF and KAZE. Optimally parametrized SURF and KAZE algorithms obtained co-registration accuracies of 0.1 and 0.3 pixels for intra-epoch and inter-epoch images respectively. To obtain better intra-epoch co-registration accuracy, collective band processing is advised whereas one-to-one matching strategy is recommended for inter-epoch co-registration. The results were tested using a maize crop monitoring case and the; comparison of spectral response of vegetation between the UAV sensors, Parrot Sequoia and Micro MCA was performed. Due to the missing incidence sensor, spectral and radiometric calibration of Micro MCA imagery is observed to be key in achieving optimal response. Also, the cameras have different specifications and thus differ in the quality of their respective photogrammetric outputs.

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USE OF GREENDRONE UAS SYSTEM FOR MAIZE CROP MONITORING

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w6-263-2017 ◽

2017 ◽

Vol XLII-2/W6 ◽

pp. 263-268 ◽

Cited By ~ 2

Author(s):

A. K. Nasir ◽

M. Tharani

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Low Cost ◽

Research Work ◽

Aerial Images ◽

Stress Index ◽

Maize Crop ◽

Crop Monitoring ◽

Water Stress Index ◽

Long Endurance

This research work presents the use of a low-cost Unmanned Aerial System (UAS) – GreenDrone for the monitoring of Maize crop. GreenDrone consist of a long endurance fixed wing air-frame equipped with a modified Canon camera for the calculation of Normalized Difference Vegetation Index (NDVI) and FLIR thermal camera for Water Stress Index (WSI) calculations. Several flights were conducted over the study site in order to acquire data during different phases of the crop growth. By the calculation of NDVI and NGB images we were able to identify areas with potential low yield, spatial variability in the plant counts, and irregularities in nitrogen application and water application related issues. Furthermore, some parameters which are important for the acquisition of good aerial images in order to create quality Orthomosaic image are also discussed.

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A VI-based phenology adaptation approach for rice crop monitoring using UAV multispectral images

Field Crops Research ◽

10.1016/j.fcr.2021.108419 ◽

2022 ◽

Vol 277 ◽

pp. 108419

Author(s):

Qi Yang ◽

Liangsheng Shi ◽

Jingye Han ◽

Zhuowei Chen ◽

Jin Yu

Keyword(s):

Rice Crop ◽

Multispectral Images ◽

Crop Monitoring

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Maize Crop Coefficient Estimation Based on Spectral Vegetation Indices and Vegetation Cover Fraction Derived from UAV-Based Multispectral Images

Agronomy ◽

10.3390/agronomy11040668 ◽

2021 ◽

Vol 11 (4) ◽

pp. 668

Author(s):

Mariana de Jesús Marcial-Pablo ◽

Ronald Ernesto Ontiveros-Capurata ◽

Sergio Iván Jiménez-Jiménez ◽

Waldo Ojeda-Bustamante

Keyword(s):

Temporal Resolution ◽

Vegetation Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Crop Coefficient ◽

Irrigation Scheduling ◽

Multispectral Images ◽

Maize Crop ◽

Spectral Models

Remote sensing-based crop monitoring has evolved unprecedentedly to supply multispectral imagery with high spatial-temporal resolution for the assessment of crop evapotranspiration (ETc). Several methodologies have shown a high correlation between the Vegetation Indices (VIs) and the crop coefficient (Kc). This work analyzes the estimation of the crop coefficient (Kc) as a spectral function of the product of two variables: VIs and green vegetation cover fraction (fv). Multispectral images from experimental maize plots were classified to separate pixels into three classes (vegetation, shade and soil) using the OBIA (Object Based Image Analysis) approach. Only vegetation pixels were used to estimate the VIs and fv variables. The spectral Kcfv:VI models were compared with Kc based on Cumulative Growing Degree Days (CGDD) (Kc-cGDD). The maximum average values of Normalized Difference Vegetation Index (NDVI), WDRVI, amd EVI2 indices during the growing season were 0.77, 0.21, and 1.63, respectively. The results showed that the spectral Kcfv:VI model showed a strong linear correlation with Kc-cGDD (R2 > 0.80). The model precision increases with plant densities, and the Kcfv:NDVI with 80,000 plants/ha had the best fitting performance (R2 = 0.94 and RMSE = 0.055). The results indicate that the use of spectral models to estimate Kc based on high spatial and temporal resolution UAV-images, using only green pixels to compute VI and fv crop variables, offers a powerful and simple tool for ETc assessment to support irrigation scheduling in agricultural areas.

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Satellite-Based Crop Monitoring and Yield Estimation—A Review

Journal of Agricultural Science ◽

10.5539/jas.v13n1p180 ◽

2020 ◽

Vol 13 (1) ◽

pp. 180

Author(s):

Olipa N. Lungu ◽

Lydia M. Chabala ◽

Chizumba Shepande

Keyword(s):

Crop Yield ◽

Decision Makers ◽

Crop Modeling ◽

Yield Estimation ◽

Remotely Sensed Data ◽

African Countries ◽

Biophysical Parameters ◽

Maize Crop ◽

Cropping Patterns ◽

Crop Monitoring

To sustain food security and crop condition monitoring, yield estimation must improve at local and global scales. The aim of this review was to give a background of satellite-based crop monitoring and crop yield estimation, including the use of crop models. Recently, most advances in remote sensing techniques, aimed at complimenting the traditional crop harvest surveys, have focused on high-production and information-rich areas. However, there is limited research in dynamic landscapes using these techniques at local scales in most Southern African countries. Models such as the Decision Support System Agro-Technology’s (DSSAT) CERES-model, and Agricultural Production Simulator (APSIM) have been used to simulate maize biophysical parameters and yield variability in a changing climate. Despite the successes, there is still need to consider yield prediction using simplified models that decision-makers can use to plan for food support and sales. The application of freely-available satellite data with focus on maize crop as a staple for Southern Africa, highlights some challenges such as heavy reliance on agro-meteorological estimations and regional estimations of crop yield. It also raises questions of predicting across large growing belts without consideration of diverse cropping patterns. Conversely, future opportunities in crop monitoring and yield estimation using remotely sensed-data still shed a light of hope. For instance, employing multi-model configurations or multi-model ensembles is one of the major missing gaps needing consideration by crop modeling research. Other simpler, but versatile opportunities are the use of crop –monitoring applications on smart phones by small holder farmers to provide phenological data to decision makers throughout a growing season.

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Comparative Effect of Groundnut (Arachis hypogea l.) Genotypes on Yield and N Fixation in Sudan and Northern Guinea Savannahs of Nigeria

10.36265/njss.2019.290208 ◽

2019 ◽

pp. 61-67

Keyword(s):

Agricultural Research ◽

Cropping Systems ◽

Block Design ◽

Dry Weight ◽

Field Trials ◽

Yield Potential ◽

N Fixation ◽

Maize Crop ◽

Significant Interaction Effect ◽

Pod Yield

Recognition of high yielding and nitrogen (N) fixing groundnut genotypes and desegregating them in the cereal-based cropping systems common in savannah regions will enhance food security and reduce the need for high N fertilizers hence, minimize the high cost and associated environmental consequences. Field trials were conducted during the 2015 growing season at the Research Farms of Bayero University Kano (BUK) and Institute for Agricultural Research (IAR), Ahmadu Bello University, Samaru-Zaria to assess the yield potential and Biolog- ical N fixation in 15 groundnut genotypes (ICG 4729, ICGV-IS 07823, ICGV-IS 07893, ICGV-IS 07908, ICGV- SM 07539, ICGV- SM 07599, ICGV-IS 09926, ICGV-IS 09932, ICGV-IS 09992, ICGV-IS 09994, SAMNUT-21, SAMNUT-22, SAMNUT-25, KAMPALA and KWANKWAS). The groundnut genotypes and reference Maize crop (SAMMAZ 29) were planted in a randomized complete block design in three replications. N difference method was used to estimate the amount of N fixed. The parameters determined were the number of nodules, nod- ule dry weight, shoot and root dry weights, pod, and haulm yield as well as N fixation. The nodule dry weight, BNF, haulm, and pod yield were statistically significant (P<0.01) concerning genotype and location. Similarly, their interac- tion effect was also highly significant. ICGV-IS 09926 recorded the highest nod- ule dry weight of 2.07mg /plant across the locations while ICGV-IS 09932 had the highest BNF value of 140.27Kg/ha. Additionally, KAMPALA had the high- est haulm yield, while ICGV-IS 07893 had the highest pod yield across the loca- tions with a significant interaction effect. The result shows that ICGV-IS 07893 and ICGV-IS 09932, as well as ICGV-IS 09994 and SAMNUT – 22, were the best genotypes concerning BNF, haulm and pod yield in the Northern Guinea and Sudan Savannahs of Nigeria respectively with the potential for a corresponding beneficial effect.

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