Identification of Weeds in
Banana Crops (Musa × paradisiaca L.) Using NDVI
Determinación de malezas en el cultivo de
plátano (musa × paradisiaca l.) por NDVI
Pedro Vélez Duque1
Published Instituto
Tecnológico Superior Corporativo Edwards Deming. Quito - Ecuador Frequency April–June Vol.
1, No. 29, 2026 pp. 1–15 http://centrosuragraria.com/index.php/revista Dates of receipt Received: January 22, 2026 Approved: March 15, 2026 Corresponding author pvelez@uagraria.edu.ec Creative Commons License Creative Commons License,
Attribution-NonCommercial-ShareAlike
4.0 International.https://creativecommons.org/licenses/by-nc-sa/4.0/deed.es
Abstract: The purpose of this study was to identify, characterize,
and classify weeds present in banana (Musa × paradisiaca L.) crops using the
Normalized Difference Vegetation Index (NDVI) and spectral image analysis. An
applied, descriptive, documentary, and field study was conducted at the Milagro
University Campus “Dr. Jacobo Bucaram Ortiz,” where the most common weed
species, their adaptive characteristics, and their degree of impact on the crop
were recorded. The results showed a high prevalence of Cyperus rotundus (35%), Imperata cylindrica (30%), and Amaranthus sp. (20%),
species known for their resistance and ability to spread. NDVI analysis allowed
the crop areas to be categorized into four levels of infestation, identifying
critical zones with values below 0.2, which exhibited sparse vegetation and
high infestation. Likewise, the use of spectral images enabled the precise
delineation of zones with higher weed density, facilitating decision-making for
selective management. It is concluded that NDVI is an effective tool for
monitoring, diagnosing, and planning sustainable weed control strategies in
banana cultivation.
PhD. Universidad Agraria del Ecuador, Facultad
Ciencias Agrarias, Escuela de Posgrado
pvelez@uagraria.edu.ec https://orcid.org/0000-0001-5886-4387
Resumen: El presente estudio tuvo como finalidad
identificar, caracterizar y clasificar las malezas presentes en el cultivo de
plátano ( Musa × paradisiaca L.) mediante el uso del Índice de Vegetación de
Diferencia Normalizada (NDVI) y análisis de imágenes espectrales. Se desarrolló
una investigación aplicada, descriptiva, documental y de campo en el Campus
Universitario Milagro “Dr. Jacobo Bucaram Ortiz”, donde se registraron las
especies de malezas más frecuentes, sus características adaptativas y su grado
de impacto sobre el cultivo. Los resultados evidenciaron una alta prevalencia
de Cyperus rotundus (35%), Imperata cylindrica (30%) y Amaranthus sp. (20%),
especies reconocidas por su resistencia y capacidad de propagación. El análisis
de NDVI permitió categorizar las áreas del cultivo en cuatro niveles de
afectación, identificándose zonas críticas con valores menores a 0.2, que
presentaron escasa vegetación y alta infestación. Asimismo, el uso de imágenes
espectrales posibilitó la delimitación precisa de zonas con mayor densidad de
malezas, facilitando la toma de decisiones para el manejo selectivo. Se
concluye que el NDVI constituye una herramienta efectiva para el monitoreo,
diagnóstico y planificación de estrategias sostenibles de control de malezas en
el cultivo de plátano.
Palabras clave: NDVI;
malezas; imágenes espectrales; teledetección; cultivo de plátano;
geoinformación; infestación; clasificación espectral; manejo agrícola.
Introduction
Banana cultivation (Musa ×
paradisiaca L.) represents one of the pillars of agriculture in tropical areas,
particularly in Ecuador, where its production impacts the economy and food
security. However, the presence of weeds reduces the productivity of this crop,
resulting in significant economic losses. This problem persists due to the lack
of effective techniques for monitoring and specifically controlling weeds,
which affect plants in different ways depending on their type and density.
This study will propose an
innovative approach to addressing this problem through the use of the
Normalized Difference Vegetation Index (NDVI) and spectral analysis,
geoinformation tools that enable the assessment of plant health and the
detection of areas affected by weeds. By classifying the areas
most vulnerable to weed infestations, more efficient and sustainable control
measures can be prioritized.
The objective of this research is to
identify and characterize weeds in banana crops, pinpointing areas with the
highest infestation levels to optimize management. The methodology employs
spectral imagery to enhance the accuracy of detecting problem areas, thereby
providing a comprehensive approach to understanding and mitigating the impact
of weeds on banana crops.
The article titled ‘IDENTIFICATION OF WEEDS IN BANANA CROPS (MUSA ×
PARADISIACA L.) USING NDVI’ is an applied and descriptive study, as its main
objective is to identify, characterize, and categorize the most common weeds in
banana crops using the Normalized Difference Vegetation Index (NDVI) and
spectral analysis, with the aim of generating practical information to optimize
agronomic management and control strategies. By combining field data
collection, image processing, and geospatial analysis, it seeks to offer
concrete solutions to improve crop productivity and sustainability, while
detailing the distribution and extent of weed impact in a specific context,
with regard to the following objectives:
In banana cultivation, various weeds can compromise crop growth and
yield by directly competing for essential resources. Some of the most prevalent
weeds in tropical and subtropical areas include nutgrass (Cyperus rotundus),
barnyard grass (Imperata cylindrica), and
pigweed (Amaranthus sp.) (Quispe and Velázquez, 2023) .
1.
Cocillo
(Cyperus rotundus)
Cyperus rotundus
(coquillo), Imperata cylindrica (grama), and Amaranthus sp. (bledo). Cyperus rotundus is known for its
resistance to traditional control techniques and its ability to regenerate
rapidly, making it one of the most problematic weeds in tropical soils (R. F. SOUZA et al., 2021) .
2.
Cock’s-foot
(Imperata cylindrica)
Imperata
cylindrica produces rhizomes that allow it to spread rapidly, forming dense
colonies that make it difficult to eradicate and reduce the growing space for
plantains (Catalina Vidal et al., 2021) .
3.
Amaranth
(Amaranthus sp.)
Amaranthus sp., with seed production reaching thousands per plant,
ensures continuous dispersal, becoming a recurring threat in every crop cycle (Rojas Rivas et al., 2020)
Cyperus rotundus:
It is a perennial plant with a deep root system, which allows it to
withstand periods of drought and efficiently compete for nutrients in the soil.
Its ability to regenerate from tuber fragments makes this species difficult to
control, as even after the application of herbicides or mechanical weeding
techniques, it has a high regrowth capacity (R. F. SOUZA et al., 2021) .
Imperata
cylindrica:
This rhizome system allows the plant to regenerate rapidly and expand
its ground cover, competing directly with banana crops for space, light, water,
and nutrients. Imperata cylindrica is resistant to
drought and fire, allowing it to survive in diverse environmental conditions
and become a dominant weed in tropical areas, severely affecting banana crop
productivity. (Catalina Vidal et al., 2021) .
Amaranthus sp.:
It has a rapid growth rate and high seed production, reaching thousands
of seeds per plant. These characteristics make it an invasive and persistent
weed, with a remarkable ability to adapt to banana-growing areas. It
aggressively competes for soil nutrients and can grow rapidly to exceed the
height of the banana crop, reducing light availability and affecting crop
growth. Furthermore, some Amaranthus species have developed resistance to
certain herbicides, complicating their control in agricultural production
systems (Rojas Rivas et al., 2020) .
The presence of weeds in banana cultivation has a significant impact on
productivity, affecting both plant development and fruit quality. It is
estimated that weed infestation can reduce the yield of a banana crop by 20% to
40%, depending on the density and type of weed present (R. F. SOUZA et al., 2021) . Cyperus rotundus and
Imperata cylindrica, being fast-growing
weeds, represent constant competition for water and nutrients, which directly
affects the health and development of banana plants, thereby reducing their
capacity for optimal growth. Furthermore, it has been observed that in areas
with high weed infestation, the fruit produced is smaller and of lower quality,
resulting in a considerable economic impact on producers. This cumulative
impact highlights the need for proper and specific weed management to preserve
the sustainability and productivity of the crop.
The degrees of weed infestation in banana crops can be classified based
on the Normalized Difference Vegetation Index (NDVI) analysis, according to the
intensity of the infestation and the impact on crop growth. In cases of high
infestation, weeds can reduce production by more than 40%, directly affecting
growers’ profitability (Andrés González Ruiz et al.,
2023) . This categorization facilitates the identification of the most
affected areas, which is essential for applying differentiated and specific
management strategies based on the degree of infestation as determined by NDVI,
thereby optimizing the resources used.
Scarce or no vegetation (NDVI < 0.2): Represents
areas where vegetation is severely damaged or absent, possibly due to heavy
weed infestation or extreme conditions that hinder crop growth. These areas are
critical and require priority intervention.
Mixed vegetation cover (NDVI 0.2–0.4): Indicates
areas with a mix of desired vegetation and weeds. Competition for nutrients,
water, and light may be limiting banana growth. This category indicates a
moderate level of impact that requires localized management.
Vigorous vegetation (NDVI 0.4–0.6): Corresponds
to areas where crop vegetation predominates, but there is still a minimal
presence of weeds that does not significantly affect yield. These areas should
be monitored to prevent an increase in weed infestation.
Dense and healthy vegetation (NDVI > 0.6): Areas where the crop is in optimal condition and weeds have minimal or
no presence. These areas are ideal for maintaining preventive management and
protecting the crop.
The degree of infestation is influenced by several factors, including
weed density and their ability to spread. For example, Imperata
cylindrica and Cyperus rotundus are fast-growing weeds
with a high dispersal capacity, which increases their impact in humid growing
areas. Studies suggest that the density of these weeds is closely linked to
nutrient availability and rainfall patterns, which determine their spread and
the type of control needed to keep their impact low (Andrés González Ruiz et al., 2023) .
The extent of impact is determined by several factors, including:
·
Environmental conditions: Factors such as the availability of water, light, and nutrients directly
influence competition between the crop and weeds. For example, in areas of high
humidity, Imperata cylindrica can
quickly become dominant.
·
Crop management: Weeding practices and the use of herbicides influence the ability of
weeds to invade and affect the crop.
The impact on banana yield increases as weeds reach a high level of
infestation, since competition for water and nutrients reduces plant vigor and
fruit quality. A study conducted by (Echávez Plata, 2022) demonstrated that in
areas with high infestations of Amaranthus sp., fruit size and average
bunch weight decreased significantly. This negative impact underscores the need
to classify severity levels and adopt appropriate control measures, which
ensures the long-term sustainability and productivity of banana cultivation.
Detecting areas with high weed concentrations is crucial for optimizing
banana crop management, as it allows control efforts to be directed at specific
zones. This approach not only reduces herbicide use but also limits
environmental impact. Spectral images, such as those used by the Normalized
Difference Vegetation Index (NDVI), provide accurate data on weed distribution,
helping to identify critical areas with high populations of . In Zone 5, the use of this technology can
facilitate localized management, increasing the efficiency of agronomic
resources.
Analysis using spectral imagery allows for differentiation between
desired and undesired vegetation based on the vegetation’s reflectance. In
banana cultivation, the NDVI allows for the detection of areas where weeds
predominate over the crop, which is essential for planning selective control
measures (Rodrigo Bautista, 2019) . This approach has
proven effective in previous studies, successfully identifying areas of high
infestation with precision and reducing the time and cost of manual
interventions.
Mapping Weed Distribution
In the context of mapping weed distribution, creating maps that show
infestation density provides growers with a visual tool to identify the most
affected areas and focus control efforts on priority areas. The use of NDVI
enables real-time monitoring of the location and extent of weeds, which
facilitates the ongoing evaluation of the effectiveness of the implemented
strategies. Recent research shows that the application of spectral mapping can
reduce management costs by up to 30% in high-density crops, such as bananas,
increasing both the sustainability and profitability of production. (Duque Vazquez, 2023) .
Methodology
The study will focus
on addressing the problem of weed infestation in banana cultivation, utilizing
specialized knowledge of geoinformation and weed management to obtain practical
results that optimize control methods in the field.
A comprehensive
review of existing literature, including books, scientific articles, and
previous studies, will be conducted to establish a theoretical framework
regarding the most common weeds in banana crops, their impact, and control
methods.
In the field, direct
observation and systematic sampling of weeds present in banana plots will be
carried out. Weed density and distribution will be recorded, while in the
laboratory, samples will be identified using NDVI to understand the
relationship between weeds and the environment.
The presence,
distribution, and degree of impact of weeds on banana crops in a specific
region of Ecuador will be documented and described.
NDVI images will be
obtained via satellite, covering the entire Zone 5 of the banana-growing
region. These images will be captured at different stages of the crop cycle to
observe variations in weed infestation.
The collected images
will be processed using geospatial analysis software to calculate NDVI values.
These NDVI values will enable the identification of areas with healthy
vegetation and areas affected by weeds. Zones will be categorized as low,
medium, or high based on the level of weed infestation, according to the
identified weed density. Each category will be represented on a map to
facilitate visualization and decision-making. The data obtained will be
compared with previous studies on the use of NDVI in weed detection to refine
interpretations and ensure the accuracy of the results.
Geospatial maps will
be created to represent critical areas, allowing farmers to identify the zones
most in need of intervention. These maps will serve as a tool for planning weed
management strategies in banana cultivation.
The project will
combine the deductive method and the analytical method.
Deductive method:
This will be used to start from general principles of remote sensing and weed
management, applying them to the specific context of banana cultivation. This
allows for a logical application of prior knowledge about geoinformation to
local needs.
Analytical method:
This will allow the data collected from spectral images to be broken down into
categories of weed infestation and specific locations. Through this analysis,
infestation patterns can be identified and selective control interventions applied,
promoting sustainability and effectiveness in weed management.
The research
techniques used include:
Direct observation
through the analysis of NDVI images, which allows for the observation of weed
distribution without interfering with the crop. Measurement of spectral
parameters to analyze the intensity of weed infestation and its distribution in
the field. Comparative analysis of data obtained from different points within
the study area, facilitating the identification of high-density weed zones and
the evaluation of effective control methods.
Results
Most common weed types affecting the
cultivation of plantains ( ).
Figure
1. Georeferenced map of the Milagro
canton
Author: (Vélez, 2024)
Likewise, a detailed analysis was
conducted of the impact that each weed species has on the development of the
plantain crop ( ), considering factors such as competition for nutrients,
light, and space. During the evaluation process, it was determined that some
species exhibit greater aggressiveness and resistance to conventional control
methods. To complement the information obtained, a morphological
characterization of the predominant weeds was performed, identifying their main
distinctive features. This analysis allowed us to establish relationships
between the abundance of certain species and the soil conditions of the land.
Additionally, the management practices implemented by local farmers were
analyzed, evaluating their effectiveness and sustainability. The data collected
served as the basis for proposing integrated control strategies. In this way,
the aim is to reduce the incidence of weeds without affecting the ecological
balance of the agroecosystem.
Table 1. Most common weeds identified in
banana cultivation
|
Common Name |
Scientific Name |
Relative Frequency
(%) |
|
Coquillo |
Cyperus rotundus |
35% |
|
Bermuda grass |
Imperata cylindrica |
30% |
|
Amaranth |
Amaranthus sp. |
20% |
|
Barnyard grass |
Barnyardgrass |
10% |
|
Purslane |
Portulaca oleracea |
5% |
Source: Observations made at the
Milagro University Campus.
Analysis Results
Dominance of Cyperus rotundus: This
species was the most common, accounting for 35% of the weeds found. Its
propagation via tubers and its resistance to traditional control methods
explain its high prevalence.
Impact of Imperata
cylindrica and Amaranthus sp.: These species ranked second and third with
relative frequencies of 30% and 20%, respectively. Both possess adaptive
characteristics that make them competitive, such as rhizomes in Imperata cylindrica and high seed production in Amaranthus
sp.
Minor presence of Echinochloa crus-galli and Portulaca oleracea: Although
less frequent, these species have an impact on specific areas of the crop due
to their rapid growth and competition for nutrients.
Degrees of weed infestation in banana crops
To categorize the degrees of weed
infestation in banana crops, Normalized Difference Vegetation Index (NDVI)
values were used. The data obtained allowed for the classification of crop
areas into four categories: sparse or no vegetation, mixed vegetation cover,
vigorous vegetation, and dense, healthy vegetation.
Table 2. Categories of impact based on NDVI
in banana cultivation
|
Category |
NDVI Range |
Area (%) |
Description |
|
Little or no vegetation |
< 0.2 |
15% |
Severely
affected areas, predominantly bare soil or weeds. |
|
Mixed vegetation cover |
0.2–0.4 |
35% |
Significant
presence of weeds competing with the crop. |
|
Vigorous vegetation |
0.4–0.6 |
30% |
Predominant
crop, but with moderate weed presence. |
|
Dense and healthy vegetation |
> 0.6 |
20% |
Crop in optimal
conditions with minimal or no weeds. |
Source: Analysis of spectral images at the
Milagro University Campus.
Critical areas: 15% of the crop
showed little or no vegetation, indicating areas with high weed infestation or conditions
unfavorable for crop growth. These areas require priority attention.
Significant weed presence: 35% of
the areas showed mixed vegetation cover, indicating direct competition between
weeds and the banana crop. These zones require localized and consistent
management.
Moderately Affected Crop: In 30% of
the area, vigorous vegetation indicates good crop development, but with weeds
present that could increase their impact without proper control.
Optimal zones: 20% of the area
showed dense and healthy vegetation, representing the desired crop condition.
These zones require preventive monitoring to maintain their condition. Identification of the largest weed population in the
banana crop using spectral imagery at the Milagro University Campus "Dr.
Jacobo Bucaram Ortiz" Based on the analysis of spectral imagery and
the data reflected in the image, the affected areas were identified according
to vegetation levels and their distribution in square meters.
Author: (Vélez, 2024)
Table 3. Areas affected by weeds according to
NDVI values
|
NDVI range |
Classification |
Total Area
(m²) |
|
< 0.2 |
Little or
no vegetation |
39,318 |
|
0.2–0.3 |
Mixed vegetation cover |
38,837 |
|
0.3–0.4 |
Vigorous vegetation |
26,361 |
|
> 0.4 |
Dense and healthy
vegetation |
13.7 |
Source: NDVI analysis at Milagro
University Campus, 2024.
Critical areas (NDVI < 0.2):
These represent the largest affected area at 39,318 m². These zones require
immediate management to prevent further damage to crops.
Mixed vegetation cover (NDVI
0.2–0.3): These cover a significant area of 38,837 m². These zones contain a
mix of weeds and desired vegetation, indicating the need for regular management
to prevent weed proliferation.
Vigorous vegetation (NDVI 0.3–0.4):
These areas cover 26,361 m². In these areas, competition with weeds is
moderate, and the crop shows good development but still requires monitoring.
Dense and healthy vegetation (NDVI
> 0.4): Covering 13,700 m², these areas reflect optimal vegetation. It is
important to maintain these conditions through good agricultural practices.
Figure
3. Map of weed identification using
NDVI at the “CUM”
Author: (Vélez, 2024)
The results obtained in this study
demonstrate that the use of the Normalized Difference Vegetation Index (NDVI)
is an effective tool for the detection and characterization of weeds ( ) in
banana cultivation. The high frequency of species such as Cyperus rotundus and Imperata cylindrica confirms the findings of Souza et al.
(2021) and Vidal et al. (2021), who highlight these species’ ability to adapt
to adverse conditions and resist conventional control methods.
Analysis of NDVI values allowed for
the establishment of a clear relationship between vegetation vigor and the
degree of infestation. Areas with values below 0.2 showed minimal coverage and
a high presence of weeds, while sectors with values above 0.6 exhibited dense
and healthy vegetation. These findings are consistent with those reported by
González Ruiz et al. (2023), who demonstrated that a decrease in NDVI is
directly associated with competition for light, water, and nutrients between
weeds and the main crop.
Furthermore, the classification of
affected areas using spectral imagery allowed for a more precise spatial
interpretation of the problem. This geospatial approach, similar to that
employed by Duque-Vázquez et al. (2023), demonstrated that multitemporal NDVI
analysis can optimize the planning of selective management strategies, reducing
the indiscriminate use of herbicides and promoting localized weed control.
The differentiated behavior of the
observed species also suggests a strong influence of edaphic and agricultural
management factors. In particular, the dominance of Cyperus rotundus in areas
with high humidity is consistent with the findings of Echávez
Plata et al. (2022), who found that microclimatic conditions determine the
persistence and aggressiveness of certain invasive species in tropical crops.
Meanwhile, the expansion of Amaranthus sp. in areas of high radiation indicates
its remarkable adaptability, a pattern previously noted by Rojas-Rivas et al.
(2020).
The results also reveal the
usefulness of NDVI as an indicator for continuous crop monitoring. The index’s
ability to differentiate between desired and undesired vegetation provides a
quantitative basis for agronomic decision-making. In this regard, the integration
of spectral imagery and NDVI analysis could complement traditional management
practices, improving crop sustainability by reducing costs and minimizing
environmental impacts, as suggested by Vigabriel
Navarro et al. (2024).
Finally, it is important to note
that, while NDVI made it possible to identify critical areas and categorize
levels of damage, factors such as image spatial resolution, cloud cover, and
temporal variability can limit the accuracy of the results. Future research
should consider the combined use of additional spectral indices (such as EVI or
SAVI) and machine learning techniques to improve weed detection and
classification. This would strengthen the integrated management of banana
cultivation and advance toward more efficient and sustainable agroecological
management.
Conclusions
The analysis identified the most
common weeds in banana cultivation at the Milagro University Campus "Dr.
Jacobo Bucaram Ortiz," revealing a high prevalence of species such as
Cyperus rotundus and Imperata cylindrica, which pose
a significant challenge for crop management.
The degrees of weed infestation were
successfully categorized, allowing impact levels to be established based on
NDVI values. It was found that the highest infestation occurs in areas with
sparse or no vegetation, highlighting the need to implement differentiated
management strategies according to the detected level of infestation.
The use of spectral imagery allowed
for the precise identification of areas with the highest weed populations,
contributing to the geographic localization of critical zones. This technology
proved to be an effective tool for monitoring and managing weeds in the crop,
optimizing agricultural decision-making.
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