ArticlePDF Available

Airborne imaging for vineyard canopy evaluation

Authors:

Abstract and Figures

During the 1993 and 1994 growing seasons, airborne digital sensors were used to collect visible and near-infrared images of phylloxera-infested vineyards near Oakville in Napa County. Computerized processing enhanced the information content of the images with respect to leaf area of the canopy. Processed image values were strongly related to ground measurements of vine pruning weight and leaf area made within a 12-acre study site. The images were useful for mapping patterns of leaf area throughout the site and in surrounding vineyards, and for assessing year-to-year changes in canopy. The vineyard manager found the imagery valuable in planning for replacement of phylloxera-infested fields, managing for crop uniformity and segregating grapes of differing quality during harvest. This tool was particularly useful in evaluating and managing newly acquired property.
Content may be subject to copyright.
Airborne imaging aids vineyard
canopy evaluation
Lee Johnson
o
Brad Lobitz
o
Roy Arrnstrong
P
Richard Baldy
o
Ed Weber
John De Benedictis
o
Daniel Bosch
I
During the
1993
and
1994
growing
seasons, airborne digital sensors
were used
to
collect visible and
near-infrared images of phylloxera-
infested vineyards near Oakville
in Napa County. Computerized
processing enhanced the informa-
tion content of the images with
respect
to
leaf area of the canopy.
Processed image values were
strongly related
to
ground mea-
surements of vine pruning weight
and leaf area made within a
1Bacre study site. The images
were useful for mapping patterns
of leaf area throughout the site
and in surrounding vineyards,
and for assessing year-to-year
changes in canopy. The vineyard
manager found the imagery valu-
able in planning for replacement of
phylloxera-infested fields, manag-
ing for crop uniformity and segre-
gating grapes of differing quality
during harvest. This tool was par-
ticularly useful in evaluating and
managing newly acquired propetfy.
Many grape growers routinely use
interpretation of color-infrared aerial
photographs as a vineyard monitoring
tool. The aerial view reveals growth
patterns that may not be obvious from
ground level, helping growers to
locate and more effectively manage
problem areas and to assess year-to-year
changes in management practices.
Investigators at NASA/ Ames Re-
search Center in Mountain View, in
collaboration with UC Davis, UC Co-
operative Extension, California State
University at Chico and the Robert
Mondavi Winery in Oakville, (Napa
County) recently evaluated the use of
digital image processing techniques
for vineyard assessment. Images col-
lected over the Napa Valley were pro-
cessed to enhance and map spatial
patterns and show year-to-year differ-
ences in vineyard canopy size.
As the name implies, the Grapevine
Remote-sensing Analysis of Phyllox-
era Early Stress (GRAPES) project was
initiated in response to the grape phyl-
loxera
(Daktulosphaira uitifoliae
[Fitch])
infestation, which affects a number of
California grape-growing regions and
is pronounced in the North Coast
(California Agriculture
March-April
1991). The insect (Biotype
8)
is a form
of plant lice that debilitates the root
system, depriving the vine of water
and nutrients and posing increased
management problems in the form of
reduced vine growth, decreased grape
yield, retarded grape maturation and
lower wine quality. Phylloxera out-
breaks tend to appear in satellite loca-
tions, adding to management time and
cost. The infestation spreads rapidly
through the vineyard and vines typi-
cally succumb within 3 to
5
years of
initial infestation.
Pesticide application is not effective
for phylloxera control, due to the deep
rooting of grapevines and to the high
rate of phylloxera reproduction. No ef-
fective biological control agent is
known. Management practices (more
severe pruning, additional irrigation
and fertilization) may lessen phyllox-
era impact for the short term, but the
only long-term solution is to remove
the infested vines and replant with a
more resistant rootstock. Improved
knowledge of the current and poten-
tial future extent of phylloxera infesta-
tion would enable growers to make
better-informed decisions for near-
term management and for replanting.
The GRAPES project builds on the ear-
lier work of Wildman et al. (1983,
Am.
J.
Enology
and
Viticulture
34(2):83-94),
who used color-infrared photography
to monitor phylloxera damage and es-
timate spread rate.
For aerial observation, the most
pronounced symptom of phylloxera-
induced stress is decreased vegetative
growth. Because canopy reduction is a
common stress indicator in perennial
and annual crops, we believe that the
analysis presented here is relevant to
the broader agricultural community.
Measurements
&
manipulations
Shortly before the 1993 growing
season (early May), a partially infested
field of Cabernet Sauvignon vines
grafted to AxR#l rootstock was chosen
as the study site. Mondavi's 12-acre
vineyard near Oakville was planted in
1981 in Clear Lake clay and Bale clay
loam soils. The vines were trained on a
standard two-wire trellis without
shoot positioning. Rows were 12 feet
apart, oriented northeast to southwest;
within-row vine spacing was
8
feet.
The site was clean cultivated with hoe
plows and discs.
Nine study plots were established
in the study site (fig.
l),
each plot con-
sisting of a total of
40
vines (4 rows,
10
vines per row). The plots were de-
limited on the basis of grower knowl-
edge, a 1992 aerial infrared photo-
graph and a phylloxera survey
14
CALIFORNIA AGRICULTURE, VOLUME
50,
NUMBER
4
involving excavation of shallow roots
and visual examination of phylloxera
population through a hand lens (table
1). Plots
1
through 3 were visually
symptomatic, with obvious reductions
in shoot length and leaf area and some
vines with visible leaf chlorosis. Plots
4
through
6
were infested but visually
asymptomatic, and plots 7 through 9
were uninfested. The phylloxera sur-
vey showed mean ratings, based on a
subsample of nine vines per plot, of
1.5 (plots 13), 0.5 (plots 4-6) and
0.0
(plots 7-9). Two Global Positioning
System (GPS) receivers were used to
determine latitude
/
longitude coordi-
nates for the study site and plot
boundaries. The commercially avail-
able receivers computed location by
receipt of radio signals from the GPS
satellite network, maintained for mili-
tary and civilian use by the
U.S.
Air
Force.
Vine-leaf area was measured by
sampling 14 vines per plot in mid-July
and mid-August 1993. The number of
shoots on each sampled vine was re-
corded, then all leaves with widths
greater than 0.5 inch were removed
from two randomly selected shoots
per vine, placed into polyethylene
bags and stored in an ice chest. The
surface area of each leaf was measured
in the laboratory with a LI-COR Model
3100 (Lincoln
NE)
leaf area meter
within 36 hours of collection. Means
for per-vine leaf area (ft2) were 57.1
(plots 1-3), 95.0 (plots
4-6)
and 91.7
(plots 7-9). Leaf area measurements
were not made in 1994. (To a large ex-
tent, plots 4-6 were visually asymp-
tomatic in mid-1993. The small dif-
ference between leaf area in plots 4-6
and plots 7-9 was not statistically
significant.)
Canopy size was affected by man-
agement practice throughout the study
site in 1993 and 1994. In addition to
normal spring removal of suckers, the
grower balanced the crop-to-leaf ratio
by removing shoots and grape clusters
in early July 1993, which resulted in a
canopy reduction. During the follow-
ing
dormant season, the grower
pruned to a lower bud count through-
out the site. The intention was to de-
crease the number of shoots in 1994
relative to 1993, yet increase shoot
length and leaf area per shoot.
Midseason shoot removal was not per-
formed in 1994, due to a satisfactory
balance between shoots and grape
clusters.
In the dormant periods following
the 1993 and 1994 growing seasons,
pruning weights were obtained for
each of the 40 vines in each plot. The
pruning weight is the total weight
(pounds) of shoots per vine, less a
small amount of shoot growth re-
tained to support the following
season’s growth. Mean per-vine prun-
ing weight in each plot for 1993 was
related to mean per-vine leaf area as:
leaf-area
=
49.1
+
8.0*prun-wt, r2
=
0.58, n
=
9. We used the mean pruning
weight per vine in each plot (fig. 2) to
assess differences in canopy size
among plots within season, and also
per plot differences between 1993 and
1994. Infested plots
1-6
showed year-
to-year declines in pruning weight,
presumably associated with continued
phylloxera-induced stress. Plots 7-9,
which were uninfested or lightly in-
fested, had greater pruning weights in
1994 than in 1993, we believe as a re-
sult of the more aggressive pruning.
Pruning weights were negatively cor-
related with midseason phylloxera rat-
ings in both 1993 (prun-wt
=
5.8
-
1.5*p-rating, r2
=
0.71, n
=
9) and 1994
(prun-wt
=
8.4
-
3.5*p-rating, r2
=
0.72,
n
=
9), suggesting that phylloxera
stress influenced canopy size in the
study site.
Reconciling different images
Digital sensors were used to collect
images over the Napa Valley and vi-
cinity during the 1993 and 1994 grow-
ing seasons. The images were recorded
as a matrix of numbers on a computer
disk rather than on film, each number
representing the brightness of each
picture element, or “pixel” (minimum
resolvable area on the ground). The
digital images were computer pro-
cessed to enhance information con-
tent, and were visually examined ei-
ther on a computer screen or as paper
prints.
B
B
B
Y
0
80
240
Scale
(ft)
Fig 1. Map of 12-acre study site, showing
position of nine study plots, 40 vines
each. At the time the plots were estab-
lished in May 1993, visual assessments
of
vine roots determined that plots 1-3 were
severely infested, plots
4-6
were lightly to
moderately infested and plots 7-9 were
not infested.
Study
plot
Fig
2.
Mean per-vine pruning weight in each
study plot, 1993 and 1994. For 1994 vs. 1993,
weights were lower in plots 1-6 due mostly
to continued phylloxera-induced decline, and
higher in less affected plots 7-9 due to prun-
ing practices described in the text.
CALIFORNIA AGRICULTURE, JULY-AUGUST
1996
15
a
The Compact Airborne Spectro-
graphic Imager, a commercial scanner
developed by ITRES Research
(Alberta, Canada), flew at 4,000 feet
aboard a light aircraft to collect images
over approximately 5,000 acres of
vineyard near Oakville on July 28,
1993. The images measured light re-
flected from the vineyard in the blue,
green, red and near-infrared spectral
regions. The pixel resolution of the im-
agery was
6
feet by 6 feet. Two weeks
later, film-based color-infrared photo-
graphs were collected at scales of
1:6,000 inches and 1:32,000 inches by
NASA aircraft.
The GRAPES project was designed
in part to evaluate the use of image
processing techniques to reconcile im-
ages from different sensors having
somewhat different imaging character-
istics, a situation that might be en-
countered in practical operation.
Therefore a different sensor was flown
in 1994: an electro-optic camera, devel-
oped and operated by NASA
I
Ames
Research Center. The camera acquired
imagery in the green, red and near-
infrared regions over a large portion
of the Napa Valley and adjoining
Carneros region on August
1.
Ap-
proximately 40,000 acres were im-
aged in
1
hour near midday by a
NASA ER-2 aircraft flying at 65,000
feet. The images had a pixel resolu-
tion of
15
feet by 15 feet. At the same
time, the ER-2 collected film-based
Color-infrared images
of 12-acre study site ac-
quired by airborne digi-
tal sensors on July 28,
1993 (a) and August 1,
1994 (b). High leaf area
shown as red; lower
leaf area tends toward
blue-gray. Images were
computer enhanced to
improve contrast. Loca-
tions of the nine study
plots are superim-
posed.
b
color-infrared photographs at a scale
of 1:32,000.
Using image-processing software,
the 1993 and 1994 images of an ap-
proximately 700-acre parcel including
the study site and surrounding area
were ”ground registered” (a map coor-
dinate was assigned to each image
pixel) using a translation based on the
GPS map coordinates. The GPS coordi-
nates were also used to delineate the
nine study plots in the imagery. The
1993 pixel resolution (6 feet) was de-
graded to match that of the 1994 scene
(15
feet). The software was then used
to generate a color-infrared picture
from the data, and
a
contrast enhance-
ment was applied to accentuate canopy
patterns within the site (see images
a
and
b
above).
Normalized differences
Various combinations of near-
infrared and red reflectance have been
shown to be sensitive to the amount of
photosynthetically active vegetation
present in the plant canopy (Tucker,
1979,
Remote Sensing
of
Environment
8:127-150). Reflectance of near-infra-
red light from plant canopies tends to
be positively correlated with the
amount of leaf surface area per unit
ground area, while reflectance of red
light tends to be negatively correlated
with leaf area. For this analysis, a nor-
malized difference vegetation index
(NDVI) image was generated for both
years by calculating for each pixel the
quantity
where NIR and RED are light recorded
by the sensors in near-infrared and red
frequencies. In addition to leaf area
sensitivity, the NDVI tends to lessen
the influence of brightness differences
associated with solar illumination or
sensor viewing angle. In an earlier
study (Pearson et al., 1994,
Remote
Sensing
of
Environment
49:304-310),
NDVI imagery was used to detect
stress in a number of high-value an-
nual crops in Wisconsin.
The NDVI generally ranges from
near
0.0
for bare soil to near 1.0 for
dense canopy. Due to the relatively
large proportion of exposed soil found
in the study site (typical of vineyards),
the mean NDVI for the study plots oc-
cupied the low end of this range: 0.19-
0.38 in 1993 and 0.13-0.37 in 1994. The
1993
mean NDVI per plot was related
to field measurements of leaf area as:
NDVI
=
-0.75
+
.56*LOG(leaf_area), r2
=
0.79, n
=
9. For the combined 1993
and 1994 data set, the mean NDVI per
plot was related to mean per-vine
pruning weight as: NDVI
=
0.15
+
0.24*LOG(prun-wt), r2
=
0.72, n
=
18.
The absolute value of the NDVI
may be affected by factors unrelated to
changes in the crop canopy (for ex-
ample, year-to-year differences in at-
mosphere, sensor response). To lessen
this influence, an image processing
NDVI
=
(NIR
-
RED)
/
(NIR
+
RED)
16
CALIFORNIA AGRICULTURE, VOLUME
50,
NUMBER
4
a
b
Relative vegetation index image of study site for 1993 (a) and 1994 (b). The index, which
is sensitive to leaf area, was first computed for each image pixel as the difference of the
infrared and red channels divided by the sum of the infrared and red channels. Next, ar-
eas of bare soil or very low leaf area were assigned black (level
0).
An image processing
routine was used to assign each remaining pixel to one of
12
levels ranging from low to
high index value, and to color code output as shown in the legend.
routine was used to assign each NDVI
pixel to one of
12
levels ranging from
low to high NDVI. These relative
.
NDVIs were color coded to facilitate
visual discrimination, with brown cor-
responding to the lowest NDVI level
(l),
dark green corresponding to the
highest NDVI level
(12)
and black
(0)
corresponding to areas of no apparent
vegetation (images
a
and
b
above). For
the combined 1993 and 1994 data set,
the relationship of mean relative
NDVI to mean per-vine pruning
weight (fig.
3)
was somewhat stronger
(r2=
0.76) and more linear than that
observed with absolute NDVI.
To show year-to-year changes in
canopy, the 1994 relative NDVI image
was digitally superimposed upon and
subtracted pixel by pixel from the 1993
image (see image p.
18).
Consistent
with the pruning weights, the result-
ing image shows a decline in canopy
at
and near plots
1-6,
due mostly to
phylloxera infestation, and constant val-
ues or year-to-year increases at and near
less infested and uninfested plots 7-9.
Practical evaluation
Color-infrared and relative NDVI
images were provided to the grower
both as paper prints and as digital files
compatible with the grower’s geo-
graphic information system software
(ArcView
1.0;
Environmental Systems
Research Institute, Redlands). Using
this software on a laptop computer,
the grower was able to enlarge, view
and print the images. The images were
shared with various company person-
nel, who generally felt that it agreed
with their perception of the property.
The imagery provided an objective
measure of canopy cover that corrobo-
rated field observations and measure-
ments. The consensus was that the
relative NDVI images (above) were far
easier to interpret visually than either
the contrast-enhanced color-infrared
images (such as the images on p.
16)
or
film-based color-infrared photographs,
which were believed to be about equal
in information content.
The imagery, essentially a map of
strong and weak areas throughout the
vineyard, was used to target areas for
investigation and possible remedial
action to improve vineyard unifor-
mity. Field verification showed that
canopy differences observed in the im-
ages were generally related to either
phylloxera infestation or soil water-
holding capacity. Considered along
with other routine measurements and
observations, the imagery was useful
for evaluating the viability of particu-
lar fields with regard to phylloxera
stress (as judged by canopy size and
uniformity) and in making replanting
decisions during the study period. The
images were used to place backhoe
pits for soil investigation prior to re-
planting, resulting in establishment of
new block boundaries that more
closely match soil patterns and thus
should simplify management and en-
hance crop uniformity. This tool was
found to be particularly useful on
newly acquired property, with which
management was less familiar. The
imagery was also used for strategic
placement of sampling sites to monitor
brix, and we feel is potentially useful
for establishing sample sites for node
levels and cluster numbers for im-
proved yield prediction.
E
0
m
al
-=
6-
4-
c
Q
5
.-
-
iii
2-
t
0
T
I
I
02468
0
I
Mean pruning weight per vine
(Ib)
Fig.
3.
Mean per-vine pruning welght In
each study plot vs. mean relative vegeta-
tion index assigned to each plot by pro-
cessing of images acquired In 1993 and
1994. No significant difference was seen
in the slope of the 1993 and 1994 regres-
sion lines. Strong goodness-of-fit (r2
=
0.76)
for the combined regression under-
scores the effectiveness of the relative
NDVI approach to monitoring canopy size
over time.
CALIFORNIA AGRICULTURE, JULY-AUGUST
1996
17
Change in relative vegetation index from 1993 to 1994 in the study site. The image was
generated by digitally superimposing and subtracting on a per-pixel basis the 1994 rela-
tive vegetation index image from the 1993 image (see Images, page 17). Areas of de-
cline, generally associated with phylloxera infestation, are displayed as yellow and red.
Maturity measures (Brix, titratable
acidity, pH and taste) were different
between the strong and weak areas
shown in the imagery. In a limited
test, the imagery was used to subdi-
vide fields for harvest based on ob-
served patterns of strength and weak-
ness. Resulting wine quality was
found to be dramatically different be-
tween the strong and weak areas;
strong areas were of “Reserve” qual-
ity, weak areas were of lower quality.
Thus, where uniformity cannot be im-
proved, the imagery will help to pre-
vent the combining of differing quality
harvest into the same wine lot.
The imagery provided a direct com-
parison of the current and previous
season’s canopy (such as the image
above). With this, the grower was able
to
assess the rate
of
phylloxera spread
(by vine and block) and to project its
future extent, again in support of re-
planting decisions. Year-to-year com-
parison was also useful for assessing
the effect
of
pruning and other man-
agement practices on canopy size and
uniformity.
Availability
of
the technology
In the GRAPES project, airborne
digital sensors acquired imagery over
increasingly large regions during each
year of the project. In 1993, coverage
was limited to the Oakville vicinity in
the Napa Valley. In 1994, coverage in-
cluded most of the Napa Valley and
the Carneros region (southwest of the
city of Napa). In 1995, the project ac-
quired imagery over much
of
the vine-
yard region in Napa and Sonoma
counties. Since 1995, at least one com-
mercial service in California collects
and processes digital imagery over
agricultural lands, delivering color-
infrared images on paper or computer
media to clients within 48 hours of
overflight. These images are suitable
for subsequent processing into abso-
lute and relative NDVI products. Be-
ginning in 1998, planned commercial
satellite systems will acquire visible
and near-infrared imagery with suffi-
cient pixel resolution (about
15
feet) to
be of use in agricultural management.
The image processing steps outlined
here are within the capabilities
of
com-
mercial remote sensing and geo-
graphic information system vendors,
and perhaps could be incorporated at
the level of the agricultural consultant.
Currently and for the near future, the
most common way to generate a digital
image is to scan a color-infrared aerial
photograph. In GRAPES, a common
desktop scanner was used to convert
1993 and 1994 film products
of
the
study site to digital format. Limited in-
vestigation produced relative NDVI
results similar to those reported here
for the digital sensors. One disadvan-
tage of this approach is slower turn-
around time due to film processing,
which may prove unacceptable for
some agricultural applications that re-
quire quick response.
Finally, trends in technology are
combining to enable the manipulation
of processed imagery and other spatial
data at the grower or grower-consultant
level. These include dramatic improve-
ments in the cost
/
performance of per-
sonal computers and image display!
geographic information system soft-
ware, and the continued evolution of
commercial GPS receivers to permit
accurate and rapid collection of geo-
graphic data for coupling fieldwork
with imagery.
L.
Johnson and
B.
Lobitz are Remote Sens-
ing Research Scientists, JCWS Inc., NASA
Ames Research Center (Earth Science
Division); R. Armstrong
is
Biological
Oceanographer, Department of Marine
Sciences, University
of
Puerto Rico; R.
Baldy is Horticulturist, School of Agricul-
ture, CSU Chico;
E.
Weber is Viticulture
Farm Advisor, UC Cooperative Extension,
Napa County;
J.
De Benedictis is Stajf
Research Associate, Department of
Entomology, UC Davis; and D. Bosch is
Vineyard Technical Manager, Robert
Mondavi Winery.
Additional information
on
the GRAPES
project is available
on
the World Wide
Web at
http:
/
/
geo.arc.nasa.gov
/
sge
/
grapes/ grapes.htm1
Interested parties are welcome to contact
Johnson regarding the availability
of
image
data sets for Napa and Sonoma counties.
18
CALIFORNIA AGRICULTURE, VOLUME
50,
NUMBER
4
... A complex digital imaging system linked to reflectance and density of canopy under varying levels of phylloxera stress was developed by Johnson [30]. Using Cook and Cook's multiregional NIR cinematography, the seasonal growth of the soil fungal complex and the southern root-knot nematode (Meloidogyne incognita Chitwood) in kenaf (Hibiscus cannabinus L.), an affiliate, was seen. ...
Article
Full-text available
Globally, the topic of agricultural automation is becoming more and more popular. Crop management system enables the systematic management of crops, incorporating all aspects of farming. India offers the opportunity to grow a wide variety of horticultural crops due to its diverse soil and climate conditions as well as its varied agro-ecological regions. These crops enhance farm output, generate employment opportunities, and supply raw materials to a variety of food-processing industries, all of which have a substantial positive economic impact on India. Although very little area is set specifically for horticulture, there is a strong demand for the production of Review Article Kale et al.; J. 421 horticulture crops. As a result, it can be difficult to meet demand with the least amount of resources. However, this can be done by introducing revolutionary technological interventions, such as nuclear technology, artificial intelligence, blockchain technology, Internet of Things technological interventions, remote sensing, various breeding programs,hydroponics systems, and others. Remote sensing technologies for monitoring and recognizing plants, weeds, pests, and diseases have been developed and used by means of recent advancements in computer vision, robotics, artificial intelligence, and machine learning. Numerous studies examine the new digital tools and services that farmers may use to purchase inputs, handle their money, and obtain input-output pricing and farm management data.
... Nevertheless, the prevalent importance of NDVI also backs the hypothesis that VI variability can be linked to the variability of yield [52]. The well-established stability of vigour variability in the medium term when no major vine management changes or other exogenous factors induce changes ( [18,53], suggests that some form of within-block vigour zoning can be used together with vine size variability to map VRB, particularly since this has been demonstrated with the use of NDVI to predict winter pruning mass [40,54,55], a wellestablished proxy for VRB. ...
Article
Full-text available
Knowledge of the spatial variation in vine yield at different scales is crucial for the wine business, and combined with estimations of vine size variability enables within-block mapping of vegetative-reproductive balance. Remote sensing combined with other data that excludes field sampling appears as an optimal approach for yield estimation for a broad range of scales. In this study, mean yield and factors known to affect yield components were collected for over 8000 blocks, over 18 seasons, in the Western oasis of Mendoza, Argentina. Partial Least Squares (PLS) and Random Forest (RF) models were used to analyse the relationship between these factors and yield. The PLS model delivered very poor results, with coefficients of determination lower than 0.08. RF models with 49 to 19 variables produced predictions with coefficients of determination of 0.96 to 0.90, respectively. Some factors traditionally considered important in yield determination, such as trellis, frost occurrence, or planting density had limited influence, whereas location weighed heavily. Results suggest a successful approach to spatial prediction of yield that requires no fieldwork and indicates VRB mapping at block-scale may be possible with these tools. Several improvements to inputs are proposed.
... A complex digital imaging system linked to reflectance and density of canopy under varying levels of phylloxera stress was developed by Johnson [30]. Using Cook and Cook's multiregional NIR cinematography, the seasonal growth of the soil fungal complex and the southern root-knot nematode (Meloidogyne incognita Chitwood) in kenaf (Hibiscus cannabinus L.), an affiliate, was seen. ...
Article
Full-text available
Globally, the topic of agricultural automation is becoming more and more popular. Crop management system enables the systematic management of crops, incorporating all aspects of farming. India offers the opportunity to grow a wide variety of horticultural crops due to its diverse soil and climate conditions as well as its varied agro-ecological regions. These crops enhance farm output, generate employment opportunities, and supply raw materials to a variety of food-processing industries, all of which have a substantial positive economic impact on India. Although very little area is set specifically for horticulture, there is a strong demand for the production of Review Article Kale et al.; J. 421 horticulture crops. As a result, it can be difficult to meet demand with the least amount of resources. However, this can be done by introducing revolutionary technological interventions, such as nuclear technology, artificial intelligence, blockchain technology, Internet of Things technological interventions, remote sensing, various breeding programs, hydroponics systems, and others. Remote sensing technologies for monitoring and recognizing plants, weeds, pests, and diseases have been developed and used by means of recent advancements in computer vision, robotics, artificial intelligence, and machine learning. Numerous studies examine the new digital tools and services that farmers may use to purchase inputs, handle their money, and obtain input-output pricing and farm management data.
... It has been proved that remote sensing can be a useful tool for early detection of diseases and identifying, managing pests and nematodes by detecting changes in plant pigments, leaf skeletonising caused by pest damage and identifying plant susceptible areas (Usha et al., 2013). Johnson et al. (1996) developed an airborne multispectral digital imaging system related crop canopy reflectance and canopy density under various degrees of phylloxera stress. Seasonal progression of the southern root knot nematode (Meloidogyne incognita Chitwood) and soilborne fungi complex in kenaf (Hibiscus cannabinus L.) an associate, was monitored using multi temporal NIR videography by Cook ...
Chapter
Full-text available
Organic farming is integral part of agricultural cultivation practice which is popular in our country since ancient times. It is clearly mentioned in the Holy Quran that at least one third of what you take out from soils must be returned to it implying recycling of the post-harvest residues. Organic farming is the natural method of growing crops using organic manures like cow dung and organic compost. Organic farming is a method of farming system, which avoids or largely excludes the use of fertilizers, pesticides, growth regulators, etc. and using organic wastes (crop, animal and farm wastes) and other biological materials including beneficial microbes (bio-fertilizers) to maintain soil health and productivity and bio-pesticides for control of weeds, pests and diseases in an eco-friendly pollution free environment.
Article
Full-text available
The use of passive optical remote sensing (RS) has a rich history in precision viticulture (PV), with the use of RS technologies being employed in a myriad of PV applications. The present work undertakes a scoping review to examine past and current trends in the use of RS in grapevine production. It aims to identify literature gaps and new research opportunities. The Scopus database facilitated the search for relevant articles published between 2014 and 2023 using a search string of keywords. A total of 640 articles were produced by the Scopus search. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting framework, the 640 articles were reviewed based on predefined inclusion and exclusion criteria, resulting in 388 articles being deemed eligible for further data extraction. Four research questions were defined to guide the data extraction process, and a coding scheme was implemented to address these questions. The scoping review found Italy and the United States to be leading contributors to the research field, with vineyard mapping, yield estimation, and grapevine water status being the most extensively studied RS–PV applications. However, the use of RS to map vineyard soil properties and grapevine cultivars remains underexplored, presenting promising avenues for future research.
Book
Full-text available
In the current scenario, marked by a continual improvement in living standards, it becomes imperative to boost the productivity or rather the efficiency of agriculture, especially horticulture, which holds the potential for significant economic prosperity aligning with SDG goal number 8, ‘Decent Work and Economic Growth’. Modern technological interventions, such as geospatial technology and Geographic Information System (GIS) technology, can be harnessed to yield effective results in addressing challenges and providing enhanced decision support, particularly in the planning of horticultural resource management. Fresh produce cultivation and production face several challenges, including prolonged juvenile phases and reproductive cycles with extended breeding periods, creating bottlenecks in the process. The evolving trends in biotechnology offer promising solutions for improving the selection of desirable traits. Biotechnological techniques aimed at improving fruit efficiency encompass tissue culture, induction of genetic variability, germplasm conservation, and molecular breeding/genomics. These methods involve the study of genetic diversity, DNA fingerprinting, and QTL analysis for marker-assisted selection. Over the past few decades, the global population has consistently risen, raising concerns about the ability of the current food system to adequately feed the anticipated 10 billion people in the next 30 years. While this challenge is deemed achievable, certain changes in both food production and consumption systems are essential to ensure sustainability, reduce food loss and waste, and contribute to a global shift towards healthier and more sustainable diets. Implementing sustainable models of crop production represents a significant undertaking. To address the growing food demand amid deteriorating production environments, there is a need for promising technologies and effective management options to enhance productivity. This book is poised to be a valuable resource for horticultural scientists operating in universities, government agencies, and research centers, offering insights into achieving sustainable cultivation practices for fruits. It stands out as the first of its kind, providing in-depth knowledge on environmentally friendly methods for cultivating temperate fruit crops, to reduce harmful emissions and pollution. The book will delve into the application of Geographic Information Systems (GIS) for estimating horticulture area expansion and crop yield. Additionally, it will encompass recent biotechnological interventions in horticulture, circular agriculture models, and emerging non-thermal food preservation techniques as significant components. Features: *Aims to provide a comprehensive and integrated overview of current techno-statistical techniques employed in horticulture, delving into the associated livelihood benefits derived from the practice. *Explores the novel geographical trends to identify the site suitability indices of several temperate fruits. *Offers a comprehensive and integrated exploration of recent trends in biotechnological approaches aimed at enhancing food production, quality, and safety.
Chapter
Vineyards are variable. In spite of this, conventional approaches to winegrape production infer that the optimal practice is to apply uniform management strategies in vineyards on the assumption of homogeneity. The availability of a suite of technologies, which have collectively become known as Precision Viticulture, provides grapegrowers and winemakers with the means to move away from this “one-size-fits all” approach. Instead, management may be targeted within vineyards according to variation in their inherent characteristics and particular goals in terms of grape yield and quality. Thus, better patches can be exploited while weaker areas may be improved. This chapter provides a summary and review of recent research into vineyard variability and the development and adoption of Precision Viticulture. A particular feature of this work has been its focus on improved fruit quality and wine flavor and aroma outcomes.
ResearchGate has not been able to resolve any references for this publication.