Target-Aware Implicit Mapping for Agricultural Crop Inspection
Shane Kelly Alessandro Riccardi Elias Marks Federico Magistri
Tiziano Guadagnino Margarita Chli Cyrill Stachniss

Abstract— Crop inspection is a critical part of modern
agricultural practices that helps farmers assess the current
status of a field and then make crop management decisions.
Current crop inspection methods are labour-intensive tasks,
which makes them rather slow and expensive to apply. In thispaper, we exploit recent advancements in implicit mapping totackle the challenging context of agricultural environments tocreate dense maps of crop rows with high enough fidelity to
be useful for automated crop inspection. Specifically, we map
strawberry and sweet pepper crop rows using RGB images
captured by a wheeled mobile field robot inside a greenhouseand then use this data to build 3D maps to document thedevelopment of plants and fruits. Our Target-Aware Implicit
Mapping system (TAIM) uses a SLAM-based pose initialization
strategy for robust pose convergence, an efficient informationguided training sample selection framework for faster loss
reduction, and focuses on exploiting training samples for fruit
regions of the scene, which are critical for crop inspection tasks,
to create more accurate maps in less time.

I. I NTRODUCTION
Crop inspection is a key part of an informed agriculturaloperation, where farmers quantify critical characteristics oftheir fields and crops. Thorough crop inspection empowers
optimal decision making, such as selecting harvest times andscheduling appropriate disease treatment plans [4]. However, traditional methods for crop inspection are manuallyintensive, which can make the procedure slow, expensive,and prone to human error. Even modernized methods relyon equipment such as terrestrial laser scanners, which arecumbersome, expensive, and require manual operation from
specially-trained users. Researchers had success in using
UAVs for automated crop inspection tasks such as diseasediagnosis [7], yield estimation [2], weed detection [19],[18]. However, these methods rely on distant observations
from UAVs high above the relevant crops, which can limit
effectiveness. New methods for on-the-ground agricultural
field navigation [1] and view planning [37] allow for closeup
measurements of crops for automated inspection. Despite
this progress, many on-the-ground inspection methods [8],Kelly and Chli are with the Vision for Robotics Lab, ETH Zurich. Allother authors are with the University of Bonn. Stachniss is additionally with
the Department of Engineering Science at the University of Oxford, UK, andwith the Lamarr Institute for Machine Learning and Artificial Intelligence,
Germany.
This work has partially been funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence
Strategy, EXC-2070 – 390732324 – PhenoRob, by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under STA 1051/51 within the FOR 5351 (AID4Crops) and by the Federal Ministry ofFood and Agriculture (BMEL) based on a decision of the Parliament of
the Federal Republic of Germany via the Federal Office for Agriculture
and Food (BLE) under the innovation support programme under funding
no 28DK108B20 (RegisTer).

Fig. 1: Right: Our robot capturing images of crop rows inside agreenhouse. Left: Qualitative view synthesis results of our method,
TAIM, compared to a baseline, BARF. We achieve higher-fidelity
view synthesis, especially on fruit regions of the scene.

[9], [25] generate inspection results from one-shot sensor
measurements, rather than using all sensor measurements tocreate a refined crop model.
Recent works on neural rendering [24] and implicit mapping [31] have shown impressive results in using RGB
images to model 3D scenes with high enough fidelity suchthat we believe these technologies could be useful in automated crop inspection tasks when paired with recent cropreconstruction approaches [20], [21], [22]. In this paper,
we propose an implicit mapping system able to tackle
the challenging context of agricultural field environments.
Specifically, we attempt to make high-fidelity maps of strawberry and sweet pepper crops inside greenhouses from RGB
images captured by a mobile robot, as shown in Fig. 1. These
scenes have large amounts of visual repetition, are only seenfrom limited view points, require high precision to capture
the small fruits, and contain noise from motion blur effects
as the wheeled robot drives over uneven terrain [15].
The majority of existing works propose methods for
implicit mapping systems on images without known poses,
which removes a significant barrier for real-world applications. However, these pose learning strategies often aresusceptible to convergence to local minima on complex
scenes with repeated visual texture. Additionally, to the...
Fig. 2: A system overview of TAIM. All fruits within the input images are detected. A classical visual SLAM system is used to compute
approximate initial image poses. In the training loop, a loss distribution over all images is estimated and used, in combination with thefruit detections, to compute a training sampling probability for each pixel, such that pixels inside fruit regions with high loss have thehighest probability of being sampled during training. Training pixels are drawn from the sampling probability distribution, encoded using
Gaussian positional encoding, and fed to the network.

best of our knowledge, no implicit mapping system uses
application-specific knowledge to map certain regions ofinterest with higher fidelity than others, thereby creating amore accurate map of relevant regions in less time.
The main contribution of this paper is a method to
implicitly map large and complex agricultural scenes withhigh fidelity in key fruit regions. During training, we use
a fast method for estimating a dense loss over all training
images and efficiently sample training pixels from high-loss
regions. This results in learning more information per training sample and leads to convergence in fewer iterations. We
also use application-specific knowledge to sample training
pixels more densely from fruit regions of the scene, foragricultural crop inspection. Lastly, we use a classical SLAM
system to initialize image poses and then continuously refine
the poses during training. This reduces the chances thatour pose estimates converge to local minima and increases
convergence speed. Our experiments suggest that we have:(i) an efficient dense loss estimation framework over input
images for focusing the training on high-loss regions andfruits regions of the scene, resulting in view synthesis with
better quality, (ii) a system for sampling more densely fromfruit regions of the input images during training that resultsin higher fidelity 3D fruit mapping, and (iii) a pose learning
strategy that is initialized by a classical SLAM system andthen refined in parallel with the scene during training to
improve convergence accuracy and robustness.
II. R ELATED W ORK
Most classical robot mapping systems use representations
such as point clouds [12] or voxel grids [27] to represent
the output 3D structure of the mapped scene. The unorderedstructure of a point cloud can represent a shortcoming torepresent objects such as fruits and plants, which are highly

spatially correlated. Voxel grids, on the other hand, arelimited by the discretization to a hard-coded resolution andthe prohibitive memory consumption when scaled to large
scenes. In contrast, learning-based approaches that implicitly
represent the scene do not lose accuracy due to discretizationand efficiently embed the contents of the scene within theweights of the network.
Indirect mapping methods [5], [34] use keypoints as asparse abstraction for stitching together dense map segments,
but the keypoints can sometimes fail to be discriminative,
such as in textureless scene regions, whereas neural networks
can distinguish relevant features directly from image datafor robust alignment. Direct methods [3], [6], [28] improve
performance in textureless regions, but often still split pose
estimation and scene estimation into two distinct processes,
as popularized by Klein et al. [13], which can lead to lesscoherent results compared to implicit approaches that use thesame photometric loss to simultaneously learn scene contents
and refine pose estimates. An alternative approach is the useof neural rendering to represent the 3D scene, so called implicit models. Mildenhall et al. [24] use MLPs to learn spatial
contents of a scene from RGB images. Some recent works
remove the known-pose requirement by learning image poses
through a typical SLAM framework [31], [38], which unlock
realtime mapping capabilities, but result in only approximate
scene reconstructions which are often too over-smoothed formany high-precison applications.
Other recent systems use longer training times for higher
fidelity map results while learning image poses fromscratch [16], [35], [36], but are sensitive to repeated visual
texture in the scene leading to pose convergence to local
minima or make assumptions about the scene that limits
real-world application of the systems. Some works makeadditional efforts to bring implicit mapping pipelines to fullyunconstrained real-world settings, such as accommodating
input images taken in different lighting conditions [23], [32],but still often require multiple days of training to converge.
III. O UR A PPROACH - TAIM
Our system architecture is inspired by BARF [16], arecent and impressive implicit mapping pipeline proposedby Lin that adds pose learning capabilities to the original
NeRF system [24]. It extends BARF and improves resultsin agricultural environments for crop inspection, as outlinedin Fig. 2. Instead of initializing our pose estimates to theidentity transform, we use a classical SLAM system forpose initialization to reduce the risk that the pose estimation
converges to a local minimum. Using pose initialization
also means that we do not need as wide of a basin of
convergence for pose estimation, which allows us to replace
BARF’s coarse-to-fine positional encoding with Gaussian
positional encoding [33], a more principled approach to theoriginal NeRF positional encoding that leads to faster scene
convergence that is still smooth enough for our initialized
poses to converge accurately to the true poses. We replace
BARF’s uniform training pixel sampling strategy with anefficient loss-targeted strategy that samples more often fromcomplex regions that are not yet mapped with high fidelity.
We also target fruit regions of the scene during training
sampling, such that those regions of the scene are mappedwith higher fidelity than non-fruit regions.
A. Pose Initialization
Our crop row datasets have a large amount of repeatedvisual structure, such as clusters of strawberries, that often
attract image pose estimates into local minima. The longand narrow shape of the crop rows also delays learning thescene since poses must follow the visual gradient over a large
distance before accurate learning of the scene’s contents canbegin. To avoid these issues, we use a classical SLAM system
to compute approximate poses of all our input images anduse these approximate poses to initialize our system. We thenfurther refine these initial pose estimates during training.
For pose initialization, we use ORB-SLAM [26], a wellknown classical visual SLAM pipeline that uses ORB features [29] to co-register keyframes for tracking and has
shown impressive results in many challenging environments.
B. Pose Learning
BARF removes the requirement for known image poses
from NeRF by using coarse-to-fine positional encoding tolearn image poses in parallel with the contents of the scene.
Only low-frequency spatial information is learned early on intraining, which creates smooth visual gradients over which
image poses can converge using the same view-synthesisbased loss as the original NeRF system. Unlike BARF, TAIM
removes the coarse-to-fine encoding and instead uses Gaussian positional encoding and initializes pose estimates froma classical SLAM system. In previous works, the Gaussian
positional encoding strategy [33] has been shown to leadto faster convergence during training. Gaussian positional

encoding requires encoding the input 3D coordinates with amatrix B of a chosen size and initialized by drawing values
from a standard normal distribution multiplied by a chosen
scale parameter. The objective for simultaneous pose andscene optimization, as formulated by BARF and used by oursystem, ismin

p1:M ,Θ
M

M X
X
i=1

2

Î(u; pi , Θ) − Ii (u)

u

,

(1)

2

M

where {Ii }i=1 and {pi }i=1 are the images and associatedposes, u is the set of all pixels in all training images, Î isthe rendering function, and Θ is the network parameters. The
rendering function is differentiable and thus allows gradientsto be traced end-to-end from the computed loss back to thepose and scene parameters, which are updated during training
to encode the optimized poses of the input images and thehigh-fidelity spatial contents of the scene.
C. Loss-Targeted Sampling
The complexity of our scene is highly irregular, with a mix
of extremely low-complexity regions, such as large solidblack plant boxes, and extremely high-complexity regions,such as fruits or vines. A simple uniform sampling strategy
for selecting image pixels s to train on
 
1
(2)
s∼U
|u|
results in inefficiently over-sampling from the lowcomplexity regions, and under-sampling from the highcomplexity regions. To correct this problem, Sucar et al. [31]propose estimating an approximate loss distribution, L, overan 8×8 pixel grid in each training image, and then sampling
training pixels proportionally to this distribution
s∼

L
.
|u|

(3)

We found that this strategy still resulted in over-samplingfrom low-complexity regions that were in the same gridsection as high-complexity regions, and vice-versa, due to the
low resolution of the loss estimate. Additionally, we foundthis method to be prohibitively computationally expensive
due to the large number of pixels being rendered on every
training iteration just to estimate the loss distribution.
We propose a loss distribution estimation method that
results in a pixel-level resolution loss estimate without needing any additional scene render computations. On the firsttraining iteration, we sample uniformly over all training
images, then set the loss estimate to a flat distribution of
the average loss of all samples. On every subsequent training
iteration we sample training pixels proportionally to a lightly
blurred version of the loss estimate, render those samples as
we normally would, and then update the loss estimate at
those pixel locations to the loss values we just computed.
Since we already compute loss during training, this systemdoes not add additional computation. We sample from ablurred version of the loss estimate so that each pixel in theloss estimate can propagate local effects to nearby regions.

Fig. 3: Qualitative view synthesis results on our 3 meters strawberry dataset with over 200 fruits. During view synthesis, TAIM results
have crisp edges, while BARF tends to over-blur, and NERF is susceptible to high-frequency noise.

In practice, the loss estimate can become stale in some extremely low-loss regions that are not sampled for many thousands of training iterations. Therefore, we use an explorationexploitation framework to balance between sampling highloss regions to learn the contents of the scene and sampling
low-loss regions to update our loss estimate. We implement
this by imposing a minimum probability for sampling any
pixel, smin , using a variable, α, that tunes the ratio between
the highest sampling probability and the lowest sampling
probability. In other words, smax = α smin . Therefore, we
sample training pixels such that
s∼

L 1−α
Lmax + α
|u|

,

(4)

where Lmax is the maximum value in the loss estimate. The
numerator scales the loss estimate distribution, L, between α
and 1 − α and the denominator normalizes it to a probability
distribution. An example training image and the resulting
blurred loss estimate in Fig. 2 show that complex scene
regions, such as regions with large gradients, are clearly
detected as having high loss and thus sampled more.
D. Fruit-Targeted Sampling
Fruits are a key focus of the crop inspection process so we
aim to map them with higher fidelity than non-fruit regionsof the scene. To accomplish this, we use a neural network
for image segmentation to create a mask of all fruit pixels
in our training images. We then slightly dilate this mask toensure that all edges of the fruits are captured and then apply
Gaussian blurring to create a smooth transition from fruit
regions to non-fruit regions. This results in a distribution F
over all pixels in our training images that describes how
fruity each pixel is. An example training image and theresulting fruit mask are in Fig. 2. We scale F to be between 1

Strawberry
Whole Scene PSNR

Fruit Only PSNR

# Training Iters.

10k

30k

50k

10k

30k

50k

NERF [24]BARF [16] w/ pose
TAIM (ours)

19.83
16.58
21.07

21.60
20.34
23.36

22.36
21.41
24.25

18.34
12.47
21.03

20.55
18.39
24.50

21.53
19.93
26.05

Sweet Pepper
Whole Scene PSNR

Fruit Only PSNR

# Training Iters.

10k

30k

50k

10k

30k

50k

NERF [24]BARF [16] w/ pose
TAIM (ours)

17.51
14.37
17.61

18.66
16.94
18.93

19.15
17.73
19.44

19.44
15.22
20.24

20.86
18.50
21.91

21.19
19.40
22.73

TABLE I: Quantitative view synthesis results. PSNR quantifies thesimilarity between rendered images and ground truth images.
Whole Scene

Fruit Only

precision

recallf-scoreprecision

recallf-Score

46.25
44.31
58.08

65.70
73.62
77.00

54.29
55.32
66.22

10.87
16.36
16.34

39.65
53.24
63.32

17.06
25.03
25.98

NERF [24]BARF [16] w/ pose
TAIM (ours)

TABLE II: Quantitative 3D scene reconstruction results.

and β and then modify our loss-targeted sampling procedure
to weigh each pixel’s loss according to its fruityness

s∼




L 1−α
Lmax + α (F (β − 1) + 1)
|u|

,

(5)

where β can be tuned to set how much more often fruit
regions should be sampled compared to non-fruit regions.
We found that increasing β improves the fidelity of fruitregions of the map, though with increasingly diminishingreturns, at the cost of lower non-fruit region fidelity.

Fig. 4: Qualitative view synthesis results on 5 meters long sweet pepper dataset. TAIM synthesizes views with crisp and accurate edges,
whereas BARF produces blurred edges and NERF renderings have a large amount of high-frequency noise.

IV. E XPERIMENTAL E VALUATION
The main focus of this work is to robustly and accurately
map strawberry and sweet pepper crop rows. The experiments presented here support our key claims, i.e., that we
propose (i) a framework for focusing the training on highloss regions of the scene, resulting in better quality view
synthesis, (ii) a system for sampling most densely from fruit
regions, resulting in higher fidelity 3D fruit mapping, and (iii)
a classically-initialized pose learning strategy that is refinedin parallel with the scene during training to improve convergence accuracy and robustness. Our experiments qualitatively
and quantitatively evaluate our ability to synthesize 2D views
and reconstruct 3D scenes compared to BARF and NERF.
A. Experimental Setup
We evaluate our implicit mapping pipeline on strawberryand sweet pepper crop rows inside a greenhouse near Bonn,
Germany. For the sweet pepper, we used a part of the
BUP20 dataset [8], [30] consisting of 200 images withover 50 prominent fruits. We collect our own strawberry
dataset consisting of 146 images with over 200 prominent
fruits. For the strawberry dataset, we additionally obtained
a high-precision point cloud using a terrestrial laser scannerthat we use as ground truth. Fruits within the ground truth
strawberry point cloud are hand labeled such that points
belonging to fruits can be isolated from the rest of the point
cloud to compute fruit-only and whole-scene reconstruction
metrics. Since BARF without pose initialization converges
to extremely incorrect pose estimates, we compare TAIM to
BARF with pose initialization in the following experiments.
B. Implementation Details
Similar to BARF, our MLP uses four, 256-node hidden
layers with ReLU activation and we use the Adam optimizer

to compute updates for both scene and pose parameters.
We use a scene learning rate of 10−3 . Since our poseparameters are initialized with an approximate guess, we use
a lower pose learning rate of 10−6 . Our Gaussian positional
embedding matrix is 3 × 256 and is initialized using ascale of 12. For fruit detection in our training images, we
use two Mask R-CNN networks [10] with the ResNet50
architecture [11] pretrained on the COCO dataset for object
segmentation [17] and then refined to segment strawberries
or sweet peppers using about 200 images each.
C. 2D View Synthesis
The first experiment shows that our loss estimation framework results in better quality of the synthesized views. We
compare the ability of TAIM to synthesize views against the
NERF and BARF baselines. We also qualitatively compareview synthesis results in Fig. 3 and Fig. 4, which showthat we achieve higher synthesis quality on both color anddepth renderings, especially in fruit regions of the scene,compared to BARF and NERF. TAIM generalizes well toboth strawberries and peppers despite extreme differences infruit size, texture, and color. We use peak signal to noise
ratio (PSNR) to quantify the similarity between rendered
images and ground truth images. Tab. I shows that TAIM
achieves higher PSNR on whole-image and fruit-only regions
at every evaluated training iteration interval compared toboth NERF and BARF, due to its efficient sampling during
training. At the final training iteration of the strawberry
dataset, TAIM improves fruit-only PSNR by 21% and 31%
compared to NERF and BARF, respectively. NERF is able tooutperform BARF on these metrics due to BARF’s coarseto-fine encoding, which delays the schedule at which the
network can learn high-frequency contents of the scene.

Fig. 5: Segments of the point clouds from TAIM and BARF compared to the ground truth. Relative to BARF, TAIM achieves a noticeably
more well-defined reconstruction that better matches the ground truth in both the whole scene (left) and the fruit-only regions (right).

Fig. 6: Pose estimation of BARF and TAIM on the strawberry
dataset with and without pose initialization from a SLAM system.
Pose initialization is necessary for convergence to accurate poses.

D. 3D Scene Reconstruction
We evaluate the 3D mapping performances on the strawberry dataset by extracting a point cloud after training
and comparing it to ground truth. We extract whole-scene
point clouds by unprojecting each pixel of the RGB anddepth renderings from every input training image into thescene and obtain fruit-only point clouds by filtering basedon fruit detection on images. A qualitative comparisonof cropped ground truth, TAIM, and BARF point clouds
is shown in Fig. 5, which shows that TAIM generates
clearer reconstructions with less blurring than BARF. For
quantitative evaluation, following Knapitsch et al. [14], we
compute precision, recall, and f-score on the point cloudreconstructions from NERF, BARF, and TAIM relative tothe ground truth. As shown in Tab. II, compared to bothBARF with pose initialization and NERF, TAIM improved
the combined f-score metric on both the whole scene andthe fruit-only scene. The achieved f-scores of NERF, BARF,
and TAIM were 17.06, 25.03, and 25.98, respectively. BARF
achieved a very slightly higher precision score on the fruitonly scene, which is sensitive to depth bleeding effects.
E. Pose Convergence
To show that pose initialization is necessary for ourchallenging agricultural scene, we qualitatively evaluate thepose estimation of BARF and TAIM with and withoutpose initialization on the strawberry dataset. Fig. 6 showsthat BARF with pose initialization and TAIM have pose
estimates that closely resemble the pose estimates provided
by a classical SLAM system, which indicates that the pose
estimates have converged to approximately correct values.
However, the pose estimates for BARF and TAIM withoutpose initialization do not resemble the estimates from the
SLAM system, indicating convergence to incorrect values.
While the initial pose estimates are of good quality, we
found that further pose refinement during training was stillnecessary to ensure crisp mapping without “doubled” edges.
V. C ONCLUSION
In this paper, we presented a novel approach to implicit
mapping in agricultural environments. Our system leverages
a novel loss estimation method over all training images toefficiently focus training on high-information complex scene
regions. Our method places additional training focus on
fruit regions of the scene, a key part of agricultural crop
inspection, to map them with higher fidelity than non-fruit
regions. Additionally, our system uses a classical SLAM
system to initialize pose estimates for each input image,which are then refined in parallel with scene mapping during
training. We evaluated our system on data from strawberryand sweet pepper rows inside a greenhouse and compared its
ability to do 2D view synthesis and 3D scene reconstruction
against BARF, a state-of-the-art implicit mapping system,
and its well-known predecessor, NERF. Our experiments suggest that TAIM produces higher-fidelity synthesized views
and more accurate scene reconstructions than both BARF
and NERF, especially in critical fruit regions of the scene.
Potential areas for further improvement include the use ofmulti-camera platforms with different viewing angles formore accurate depth estimation, and targeting critical regions
beyond just fruits during training, such as leaves and vines,
whose intricate structure is challenging to map with highfidelity.

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