# Localized Data Work as a Precondition for Data-Centric ML:
A Case Study of Full Lifecycle Crop Disease Identification in Ghana

Darlington Akogo  Issah Samori  Cyril Akafia  Harriet Fiagbor  Andrews Kangah
Donald Kwame Asiedu  Kwabena Fuachie  Luis Oala

###### Abstract

The Ghana Cashew Disease Identification with Artificial Intelligence (CADI AI)
project demonstrates the importance of sound data work as a precondition for
the delivery of useful, localized data-centric solutions for public good tasks
such as agricultural productivity and food security. Drone-collected data and
machine learning are utilized to determine crop stressors. Data, model and the
final app are developed jointly and made available to local farmers via a
desktop application.

Machine Learning, ICML

Cashew is a significant cash crop in Ghana (Rabany et al., 2015), with small
and medium farmers relying on it for income. Cashew cultivation is
concentrated in specific regions of Ghana. However, farmers face challenges
including insect, plant disease and abiotic stress factors that reduce their
yields (ICAR, ; Jayaprakash et al., 2023; Mensah et al., 2023; Timothy et al.,
2021). To address these issues, the Cashew Disease Identification With
Artificial Intelligence (CADI AI) project was launched to provide a data-
centric solution. The project encompasses three stages. Comprehensive data
work encompassed stakeholder consultation, data collection, data annotation
and labelling. The collected drone data is open for researchers and data
scientists to develop innovative machine learning applications to improve food
security. Model work involved the training of an object detection model to
diagnose and detect stress factors in cashew crop images. Finally, the model
was integrated into a desktop application for farmers, allowing them to input
their own data and receive diagnoses. The application also displays the
precise location of the image, enabling farmers to identify affected areas on
their farms.

Figure 1: A visual summary of the application lifecycle: data work (data
collection with farmers, data annotation and labelling), model work (model
training and fine-tuning), and UI application (software deployment and release
to farmers).

##  1 Data work

The data was collected from cashew farms in the Bono Region of Ghana,
necessitating two separate trips to the farms to accommodate seasonal
variations and diversity of data. In total, the data collection process
spanned six days. The dataset is diverse in maturity stages, camera angles,
time of capture, and various types of stress morphology. All images were
captured with the P4 Multi-spectral drone (Dji, ) at image resolution of 1600
x 1300 pixels. The images comprise close up shots and shots from distance of
the cashew plant abnormalities. The total number of images collected is 4,736.
Full details and datasheet in the appendices. Further improvements to the
dataset could be made by capturing across more regions during blooming cycles
or varying devices for robustness testing (Oala et al., 2023).

###  1.1 Data Annotation/Labelling

The data was annotated by the project team with labelling tools makesense
(Makesense, ) and roboflow (Roboflow, ). Refer to appendix A.1 for annotation
guidelines developed by an agricultural scientist with expertise in crop
health and disease management from a local Ghanaian university. Each stress
instance is associated with a class label based on the status of the crop. The
labels are “insect”, “disease” and “abiotic” respectively as depicted in
Figure 2.

Figure 2: Top: Sample instances from the annotated dataset. For a higher
resolution sample see the appendices. Bottom: Distribution of labels in the
annotated data.

The data was split into train, validation and test sets i.e., 3788, 710 and
238 images respectively. During the training, it was found that the dataset is
significantly skewed towards the abiotic class.

##  2 Model work

We utilized the YOLO v5X (Jocher, 2020) architecture, known for its strong
performance on object detection benchmarks, as the foundation for this study.
The experiments were conducted on the high-performance DFKI GIZ cluster.The
dataset has a significant skew towards the abiotic class (see Figure 2), and
measures were taken to balance the data by augmenting other classes. However,
preserving the skewness was important to reflect the higher occurrence of
abiotic factors on farms. The best model achieved a mean average precision
(mAP) of 0.648. See Table 1 and Figure 4 in the Appendices for detailed
experimental evaluations and baselines. The model has a few limitations that
affect its performance in distinguishing between the disease class and the
abiotic class. The primary challenge lies in the similarity between these two
classes within a typical farm setting. The model may encounter difficulties in
accurately differentiating between them due to their overlapping
characteristics. This limitation is an inherent challenge in the dataset and
can impact the model’s accuracy when classifying these instances. However, it
is worth noting that the model exhibits strong performance when it comes to
the insect class. This is attributed to the distinct characteristics of insect
class, which make them easier to identify and classify accurately.

##  3 Closing the loop: UI application

Figure 3: Screenshot of the final UI application. For more details see
appendices.

A software application built with Flutter (Google, 2019) was infused with the
CNN model trained on the collected data, allowing farmers to use the model on
new data for crop disease management. Our objective is to make the CADI AI
project’s data, model, and software widely accessible to maximize their impact
within the agriculture community. The data is available through prominent
platforms such as Kaggle and Hugging Face dataset hub. These platforms provide
user-friendly interfaces, allowing researchers, developers, and enthusiasts to
access the data for their specific needs. Furthermore, the model itself can be
accessed through the Hugging Face platform, enabling users to leverage its
capabilities in their own ML applications. A summary of all resources is in
the appendices.

## References

  * AGPL (2023) AGPL, G.  Gnu affero general public license, Feb 2023.  URL https://en.wikipedia.org/wiki/GNU_Affero_General_Public_License.
  * (2) Dji.  P4 multispectral - dji.  URL https://www.dji.com/p4-multispectral.
  * Exif (2014) Exif.  Exif exchangeable image file format, Feb 2014.  URL https://www.loc.gov/preservation/digital/formats/fdd/fdd000146.shtml.
  * Google (2019) Google.  Flutter - beautiful native apps in record time, 2019.  URL https://flutter.dev/.
  * (5) ICAR.  Cashew pest database.  https://cashew.icar.gov.in/pestsite/.  (Accessed on 02/02/2023).
  * Jayaprakash et al. (2023) Jayaprakash, V., Rajagopal, M. K., et al.  Cashew dataset generation using augmentation and ralsgan and a transfer learning based tinyml approach towards disease detection.  _arXiv preprint arXiv:2304.08766_ , 2023.
  * Jocher (2020) Jocher, G.  Yolov5 by ultralytics, 2020.  URL https://github.com/ultralytics/yolov5.
  * KaraAgroAI (2023) KaraAgroAI.  Cadi ai - cashew disease identification with artificial intelligence, May 2023.  URL https://github.com/karaagro/cadi-ai.
  * (9) Makesense.  Makesense ai tool for annotation.  URL https://www.makesense.ai.
  * Mensah et al. (2023) Mensah, P. K., Akoto-Adjepong, V., Adu, K., Ayidzoe, M. A., Bediako, E. A., Nyarko-Boateng, O., Boateng, S., Donkor, E. F., Bawah, F. U., Awarayi, N. S., et al.  Ccmt: Dataset for crop pest and disease detection.  _Data in Brief_ , pp. 109306, 2023.
  * Oala et al. (2023) Oala, L., Aversa, M., Nobis, G., Willis, K., Neuenschwander, Y., Buck, M., Matek, C., Extermann, J., Pomarico, E., Samek, W., Murray-Smith, R., Clausen, C., and Sanguinetti, B.  Data models for dataset drift controls in machine learning with optical images.  _Transactions on Machine Learning Research_ , 2023.  ISSN 2835-8856.  URL https://openreview.net/forum?id=I4IkGmgFJz.
  * Rabany et al. (2015) Rabany, C., Rullier, N., and Ricau, P.  The african cashew sector in 2015.  _General Trends and Country Profiles. African Cashew Initiative (ACI) Report_ , 2015.
  * (13) Roboflow.  Roboflow: go from raw images to a trained computer vision model in minutes.  URL https://roboflow.com/.
  * Timothy et al. (2021) Timothy, M., John, O., Aibinu, A., and Adebisi, B.  Detection and classification system for cashew plant diseases using convolutional neural network.  In _The 5th International Conference on Future Networks & Distributed Systems_, pp. 225–232, 2021.

##  Appendix A Data Annotation

###  A.1 Guidelines for Annotation

Below are some guidelines for labeling or annotating the data set by team.

  * •

Provide enough space to capture the affected area without cutting any part
off, but avoid introducing too much space.

  * •

If possible, zoom into the affected area to ensure accurate annotation.

  * •

Avoid including too much gap between two affected areas. Treat a wide gap as
another annotation.

  * •

To ensure completeness, try to annotate every possible instance as long as it
is visible.

  * •

Even for images that appear to have no affected areas, zoom in and carefully
examine the image to ensure nothing is missed. It is better to have an
annotation than to remove the image altogether. However, if truly nothing
exists, discard the image from the database.

  * •

Avoid using images with human faces whenever possible.

  * •

If an image with a human face is necessary, but the face is not centered,
consider cropping it out and replacing the original image with the cropped
version

###  A.2 Deciding the Labels

  * •

Insect/ pest stress factors represent the damage to crops by insects or pests

  * •

Diseased factors represent attacks on crops by microorganisms.

  * •

Abiotic stress factors represent stress factors caused by non-living factors,
e.g. environmental factors like weather or soil conditions or the lack of
mineral nutrients to the crop.

The decision to use the labels ”abiotic”, ”disease”, and ”insect” for our
object detection task was recommended by an agricultural scientist with
expertise in crop health and disease management, Dr. Torkpor Stephen from
University of Ghana.

##  Appendix B Evaluation and Results

Table 1: Table showing Experimental Procedures using YOLO architecture

Training Data | Pretrained Model Used | Epoch | Image size | Batch Size | Comments | mAP
---|---|---|---|---|---|---
Original and background images | yolov8x | 30 | 640 | 16 | Little overfitting | 0.6
| yolov8x | 30 | 640 | 16 | More background images were included. This lead to overfiting. | 0.56
| yolo8m | 30 | 640 | 16 | Tried the above experiment with a smaller architecture | 0.57
Only original images | yolov8m | 50 | 640 | 16 | Early overfitting | 0.59
| yolov8n | 50 | 640 | 16 | Did not converge | 0.53
| yolov8l | 30 | 640 | 16 | Overfitting | 0.58
| yolov8x | 30 | 640 | 16 | Overfitting | 0.61
| yolov8n | 60 | 640 | 16 | Did not converge | 0.55
| yolov8n | 100 | 640 | 16 | Overfitting | 0.57
| yolov8m | 50 | 960 | 16 | Overfitting | 0.59
| yolov8n | 80 | 1280 | 16 | Overfitting | 0.58
Original, background and | yolov5x | 30 | 640 | 16 | Converged | 0.6
augmented images | yolov5t | 45 | 640 | 16 | Overfitting | 0.58
| yolov5m6 | 50 | 640 | 16 | Overfitting | 0.572
| yolov5x | 35 | 640 | 32 | Increased batch size lead to increased mAP | 0.615
| yolov5x | 35 | 640 | 64 | Overfitting | 0.601
| yolov5x | 35 | 640 | 48 | Little Overfitting | 0.603
| yolov5x | 25 | 640 | 56 | Batch size lead to a significant increase in performance. The model converged. | 0.648
| yolov5x | 40 | 640 | 56 | Overfitting | 0.606
| yolov5x | 40 | 640 | 72 | Overfitting | 0.616
| yolov5x | 40 | 640 | 72 | Overfitting | 0.616
| yolov5x | 35 | 640 | 48 | Overfitting | 0.58
| yolov5x | 35 | 640 | 48 | Overfitting | 0.57
| yolov8x | 40 | 640 | 32 | Overfitting | 0.55
| yolov8n | 30 | 640 | 32 | Overfitting | 0.55
| yolov8n | 30 | 640 | 32 | Overfitting | 0.48
| yolov8n | 50 | 640 | 32 | Overfitting | 0.53

Figure 4: Left: Confusion Matrix of Evaluation results. Center: F1-Curve of
Evaluation results. Right: PR-Curve of Evaluation results Figure 5: Sample
instances from the annotated dataset.

##  Appendix C CADI AI Software

Figure 6: Screenshots of the final app (from left to right, top to bottom):
About page, edit window, project history, stress locator, stress analysis,
upload screen.

The software is named CADI AI; an abbreviation for Cashew Disease
Identification with Artificial Intelligence. CADI AI has the following
features:

  * •

A desktop interface for accessing all other features of the application

  * •

A diagnosis widget where a user could select multiple number of images for
diagnoses without limit

  * •

A history widget which shows a history list of all scans made

  * •

A widget for viewing all images uploaded in a particular scan with a satellite
map that shows where the images were taken

CADI AI does not rely on an internet connection to work. It is, in an ideal
world, a totally offline application. Because the application spins up a local
flask server that enables communication with the CNN model, there is sometimes
a need to connect to the internet to download metadata which are then cashed.
The local cache can expire after some time and create the need for a re-sync
using an internet connection.

Note that CADI AI only runs scans on images that are having coordinates in
their Exif metadata (Exchangeable Image Format).

The Exif specifications define a pair of file types, mainly intended for
recording technical details associated with digital photography (Exif, 2014).

Exif is a metadata standard that defines formats for sharing metadata related
to images, sound, and ancillary tags used by digital cameras, scanners and
other systems that handle image recorded by digital cameras.

In this case, it is required that images selected for scan in CADI AI must
have metadata following the Exif standard, and must have geographical location
coordinates in the metadata before these images can be scanned by the CNN
model.

This is because the application shows location pins on a satellite map that
enables farmers to locate the region of the farm where a disease, abiotic
stress or pest is found present. Without GPS coordinates or geographical
location coordinates, farmers would be oblivious to where exactly issues are
found in their farms.

The development of CADI AI was started in-house at KaraAgro AI and the
codebase was later published as an open source repository welcoming
contributions from the open source community and serving up the application to
the general public on github (KaraAgroAI, 2023)

##  Appendix D Resource Coordinates

Table 2: Table Showing URLs to Open Source Resources | Location
---|---
Data | https://www.kaggle.com/datasets/karaagroaiprojects/cadi-ai
| https://huggingface.co/datasets/KaraAgroAI/CADI-AI
Model | https://huggingface.co/KaraAgroAI/CADI-AI
App Code and Executable | https://github.com/karaagro/cadi-ai

##  Appendix E Contribution and Commercial Use

As an open-source project under the GNU Affero General Public License(GNU
AGPL)(AGPL, 2023), the CADI AI project encourages contributions from the wider
community. Users are free to modify and build upon the existing resources,
tailoring them to their unique needs. Moreover, the resources can be utilized
for commercial purposes, providing attribution to the original creators is
duly given. This flexibility fosters collaboration and allows the project’s
benefits to extend to diverse domains and industries.

##  Appendix F Datasheet

###  F.1 3.1 Motivation

_For what purpose was the data set created? Was there a specific task in
mind?_

The creation of this dataset represents a first contribution of drone data to
the field of cashew crop research: Providing an open and accessible resource
of high-quality, well-labeled drone imagery collected from Ghana Bono-Region,
this dataset will offer data scientists, researchers, and social entrepreneurs
within Sub-Saharan Africa and beyond, opportunities for innovative machine
learning experiments and the development of solutions for infield cashew crop
disease diagnosis and spatial analysis.

_Was there a specific gap that needed to be filled? Please provide a
description._

Yes. The threat of pests and diseases to the agricultural sector in Ghana is a
constant concern, with climate change contributing to the potential for new
and more damaging types of outbreaks (Yeboah et al., 2023). Based on multi-
stakeholder engagements conducted by KaraAgro AI, also with women smallholder
cashew farmers, stakeholders have identified pest and disease detection and
yield estimation as critical concerns.

However, the current methods of identifying agricultural pest and disease
outbreaks, such as land surveys and on-site observations by individuals, are
limited in their effectiveness and efficiency. Thus, there is a need for more
innovative and efficient solutions to improve the monitoring and management of
crop health. This highlights a gap in the available tools and resources, which
can be addressed through the use of advanced technologies such as machine
learning and image analysis.

The creation of an open and accessible cashew dataset with well-labeled,
curated, and prepared imagery can provide a valuable resource for data
scientists, researchers, and social entrepreneurs to develop innovative
solutions towards infield pest and disease detection and yield estimation.

Who created this data set (e.g. which team, research group) and on behalf of
which entity (e.g. company, institution, organization)?

The dataset was created by a team of data scientists from the KaraAgro AI
Foundation, with support from agricultural scientists and officers.

Who funded the creation of the data set? If there is an associated grant,
please provide the name of the grantor and the grant name and number.

The creation of this dataset was made possible through funding of the Deutsche
Gesellschaft für Internationale Zusammenarbeit (GIZ) through their projects
“Market-Oriented Value Chains for Jobs & Growth in the ECOWAS Region (MOVE)”
and ”FAIR Forward - Artificial Intelligence for All”, which GIZ implements on
behalf the German Federal Ministry for Economic Cooperation and Development
(BMZ).
The MOVE initiative aims to support market-oriented and resilient value chains
that contribute to the creation of income and employment in the ECOWAS region
The FAIR Forward initiative aims to promote a more open, inclusive, and
sustainable approach to AI on an international level, by partnering with
countries such as Ghana, India, Indonesia, Kenya, Rwanda, South Africa and
Uganda.

###  F.2 3.2 Composition

_What do the instances that comprise the data set represent ( e.g., documents,
photos, people, countries)?_

Each instance in the dataset includes crop image (JPEG), image status
(Disease, Abiotic, and Insect), file type (images and bounding box
annotations) and location (this variable though is without values).

_Are there multiple types of instances (e.g., movies, users, and ratings;
people and interactions between them; nodes and edges)? Please provide a
description._

No. There is only one instance type, which represents cashew crops based on
drone images with various attributes.

_Does the data set contain all possible instances or is it a sample ( not
necessarily random) of instances from a larger set? If the data set is a
sample, then what is the larger set? Is the sample representative of the
larger set (e.g., geographic coverage)? If so, please describe how this
representation was validated/verified. If it is not representative of the
larger set, please describe why not (e.g., to cover a more diverse range of
instances, because instances were withheld or unavailable)._

The dataset contains various instances that were captured in the Bono region,
which is renowned for its cashew production. The data was collected in two
rounds: The first data collection happened in November 2022, the second in
January 2023.
The data captured represents cashew data from a year where cashew blooming was
particularly late. Given that the data was collected punctually only twice, it
might be that not all blooming variations of Cashews have been captured,
potentially influencing the variety of the collected data.

Thus, the dataset collected is not representative. While more continuous data
collection across various regions during the blooming cycle could have been
beneficial, we still consider our dataset to be sufficiently diverse. This is
due to the inclusion of different maturity stages, camera angles, time of
capture, and various types of stress morphology.

_What data does each instance consist of? “Raw” data (e.g., unprocessed text
or images) or features? In either case, please provide a description._

Each instance includes: the crop image and location (gps coordinates).

_Is there a label or target associated with each instance? If so, please
provide a description_.

Each instance is associated with a class label based on the status of the
crop. The labels are “ _insect/pest_ ”, “ _disease_ ” and “ _abiotic_ ”
respectively as depicted in Figure 1:

  * •

> Insect/ pest stress factors represent the damage to crops by insects or
> pests

  * •

> Diseased factors represent attacks on crops by microorganisms.

  * •

> Abiotic stress factors represent stress factors caused by non-living
> factors, e.g. environmental factors like weather or soil conditions or the
> lack of mineral nutrients to the crop.

The decision to use the labels ”abiotic”, ”disease”, and ”insect” for our
object detection task was recommended by an agricultural scientist with
expertise in crop health and disease management, Dr. Torkpor Stephen from
University of Ghana.

It is important to note that while these labels provide a general
categorization of crop damage, they may not fully capture the complexity of
the underlying causes. In addition, the labels may not be exhaustive and other
types of damage may not be captured by these categories. As with any dataset,
users should be aware of the limitations and context of the labels used and
exercise caution when interpreting the results of models trained on this data.

Examples of the limitations and complexities involved includes

  * •

> A plant may exhibit symptoms of both insect damage and disease, making it
> difficult to assign a single label to the damage.

  * •

> Damage caused by abiotic factors such as drought or nutrient deficiency may
> be similar to damage caused by disease or insect infestation, leading to
> confusion when assigning labels.

  * •

> Damage caused by multiple factors may not fit neatly into a single label
> category, requiring more nuanced and complex labeling.

  * •

> Different species of insects or diseases may cause similar damage to crops,
> making it difficult to distinguish between them using only the three labels.

  * •

> Other factors such as environmental stress, mechanical damage, or chemical
> exposure may also cause damage to crops, but may not be captured by the
> current labels.

After extensive discussions, the project team decided to opt for more general
labels, e.g. “pest”, instead of specific labels for certain diseases, e.g.
Helopeltis. The reasons for this decision were related to the AI use case for
which the data was planned to be further used (a more general early-warning
system for farmers) and also a weighting of the available resources, e.g. for
collecting and annotating data.

Figure 7: Class labels associated with data (from left to right): Insect,
disease, abiotic

_Can you provide a brief description of the number of images and instances in
the dataset, as well as any skewness or imbalances in the data?_

The dataset is significantly skewed towards the abiotic class, which could
potentially introduce bias into machine learning models trained on the data.
To address this issue, we took experimental measures during model development,
such as augmenting other classes to balance the data. However, we found that
preserving the skewness was also important, as it reflected the higher
occurrence of abiotic factors in a typical farm. This approach helped the
model recognize the prevalence of abiotic factors without overemphasizing
their importance in predicting other classes.

_Is any information missing from individual instances? If so, please provide a
description, explaining why this information is missing ( e.g., because it was
unavailable). This does not include intentionally removed information, but
might include, e.g., redacted text._

No, the data set contains all the required information.

_Is the data set self-contained?_

Yes, the data is self-contained, it does not rely on any other external
sources.

_Please provide descriptions of all external resources and any restrictions
associated with them, as well as links or other access points, as appropriate.
Does the data set contain data that might be considered confidential (e.g.,
data that is protected by legal privilege or by doctor-patient
confidentiality, data that includes the content of individuals’ nonpublic
communications)? If so, please provide a description._

No, the datasheet is not confidential and it is self-contained.

_Does the data set contain data that, if viewed directly, might be offensive,
insulting, threatening, or might otherwise cause anxiety? If so, please
describe why._

No.

_Does the data set relate to people?_

No. Given the slight possibility to use the GPS location of cashew trees to
identify individual farms, the project team decided to strip the GPS location
data in the published dataset.

###  F.3 3.3 Collection process

_How was the data associated with each instance acquired? Was the data
directly observable (e.g., raw text, movie ratings), reported by subjects
(e.g., survey responses), or indirectly inferred/derived from other data
(e.g., part-of-speech tags, model-based guesses for age, or language)? If data
was reported by subjects or indirectly inferred/derived from other data, was
the data validated/verified? If so, please describe how._

The data associated with each instance was acquired from Bono region cashew
farms in Ghana.

_What mechanisms or procedures were used to collect the data ( e.g., hardware
apparatus or sensor, manual human curation, software program, software API)?
How were these mechanisms or procedures validated?_

The images for the cashew data collection process were captured using a drone
that was flown manually. The drone was flown at different altitudes to ensure
that comprehensive information about the cashew crops was gathered. The photos
of the cashew crop were taken at different angles with altitudes ranging from
2 to 10 meters. This altitude range provides a good balance between capturing
a close-up view of the fruits and their growth stages and a wider perspective
that allows for variation.

_What quality management systems were put in place to ensure the validity,
accuracy, and reliability of data collected?_

  1. 1.

> Careful selection of flight altitude and angle to ensure comprehensive data
> collection.

  2. 2.

> Use of high-quality drone cameras to capture images of the cashew crops.

  3. 3.

> Careful storage and recording of images, including GPS coordinates and
> timestamps, to provide a record of location and time.

  4. 4.

> Exclusion of blurry or indistinct images, as well as those with defects such
> as low lighting, to maintain the quality of the dataset.

_What were the challenges faced in using drones for data collection and how
did they impact the dataset?_

Challenges faced in data collection using drones:

  * •

> Limitations of battery life and flight time

  * •

> Weather conditions affecting drone performance

  * •

> Difficulties in navigating terrain with obstacles

  * •

> Drone whirling up leaves and thus making steady pictures difficult

Impacts on the dataset:

  * •

> Limited coverage area due to battery life and flight time restrictions

  * •

> Incomplete data due to weather conditions and obstacles

  * •

> Need for manual inspection and correction of data to ensure accuracy

To overcome these challenges, the team implemented measures such as carefully
selecting flight paths, checking weather conditions, regularly calibrating and
maintaining drones, and manually inspecting and correcting data (example, if
collected data seemed blurry or not visible)

Despite these challenges, the use of drones likely provided a more efficient
and cost-effective means of data collection compared to traditional methods
(traditional methods may involve manual inspections or measurements of crops.)
However, the limitations of drone technology and the need for careful data
inspection should also be considered when analyzing and interpreting the
dataset.

_Who was involved in the data collection process (e.g., students, crowd
workers, contractors) and how were they compensated (e.g., how much were crowd
workers paid)?_

The KaraAgro AI team took upon themselves to collect this data.

_Over what time frame was the data collected?_

The data was collected in two rounds: The first data collection happened in
November 2022, the second in January 2023.

_Does_ _this time frame match the creation time frame of the data associated
with the instances (e.g., a recent crawl of old news articles)? If not, please
describe the time frame in which the data associated with the instances was
created._

_Were any ethical review processes conducted (e.g., by an institutional review
board)? If so, please describe these review processes, including the outcomes,
as well as a link or other access point to any supporting documentation._

No, there were no ethical review processes conducted.

_Does the data set relate to people? If not, you may skip the remaining
questions in this section._

No, the data set does not relate to people.

###  F.4 3.4 Preprocessing/ Cleaning/ labeling

_Was any preprocessing/cleaning/labeling of the data done (e.g.,
discretization or bucketing, tokenization, part-of-speech tagging, SIFT
feature extraction, removal of instances, processing of missing values)? If
so, please provide a description. If not, you may skip the remainder of the
questions in this section._

Yes, preprocessing and labeling of the data were done during the data
annotation stage using annotation tools (makesense.ai). Preprocessing of the
data involved removing crop images in which human figures or faces were
accidentally captured. Also blurry images were deleted.

_How was it ensured that the annotation of data was performed accurately and
efficiently, and what methods were used to validate the data and ensure that
the annotations were consistent and of high quality?_

The data was labeled by data scientists of KaraAgro AI who worked on this
project. To ensure the accurate and efficient annotation of data, the team
used advanced annotating tools (makesense.ai, roboflow) that offered various
annotation formats (xml, yolo). Before the annotation process began, an expert
in Agricultural Science reviewed the cashew images and provided comprehensive
training on the annotation process, including appropriate labels (abiotic,
insect, and diseased) to assign to each image.

During the annotation process, the team was diligent in checking the images
and ensuring that they aligned with the correct labels. Any inconsistent
images were sent to the expert for further analysis and suggestions. The team
only included clear and high-quality images in the annotated dataset and
excluded blurry, indistinct, or defective images to maintain the quality of
the dataset. The team also checked and removed any images not related to the
desired crop to prevent inaccuracies or confusion in the analysis.

Following the annotation process, the team conducted a thorough peer review of
the annotated cashew images to ensure the quality and accuracy of the
annotations made by team members. This step was crucial in ensuring that all
annotations were consistent and comprehensive, and that the annotated dataset
was of high quality.

_Was the “raw” data saved in addition to the preprocessed/cleaned/labeled data
(e.g., to support unanticipated future uses)?_

The raw unprocessed data (consisting of labeled images) has been saved.

_Is the software used to preprocess/clean/label the instances available? If
so, please provide a link or other access point._

Yes, the annotation tool _makesense_ can be accessed here.

###  F.5 3.5 Distribution

_Will the dataset be distributed to third parties outside of the entity (e.g.,
company, institution, organization) on behalf of which the dataset was
created? If so, please provide a description._

Yes, the dataset will be distributed to third parties outside of the entity
since it will be made publicly available for different companies,
institutions, and organizations.

_How will the dataset be distributed (e.g., tarball on the website, API,
GitHub)_

The dataset can be distributed on Kaggle and Dataverse.

_When will the dataset be distributed?_

Distribution is planned for May 2023.

_What license (if any) is it distributed under? Are there any copyrights on
the data?_

The data will be licensed under the GNU AGPL license where the credit must be
given to the creator, the data can be used for commercial use and adaptations
and it will be shared under identical terms.

_Do any export controls or other regulatory restrictions apply to the dataset
or to individual instances? If so, please describe these restrictions, and
provide a link or other access point to, or otherwise reproduce, any
supporting documentation._

No, there are no fees or regulatory restrictions that apply to this dataset.

###  F.6 3.6 Uses

_Has the dataset been used for any tasks already? If so, please provide a
description._

The dataset was utilized for an object detection task, where a model was
trained to recognize areas affected by abiotic factors, diseases, and insect
infestations of cashew fields.

_Is there a repository that links to any or all papers or systems that use the
dataset? If so, please provide a link or other access point. What (other)
tasks could the dataset be used for?_

_What were the main challenges faced by the team during the data collection
and annotation process, and how were these challenges overcome to ensure the
quality of the data collected?_

During the data collection and annotation process, the team faced several
challenges, such as incomplete or inconsistent labeling, difficulty in
distinguishing between similar classes, and limited availability of experts
for domain-specific knowledge. To address these challenges, the team
implemented several strategies:

  * •

> The team established clear guidelines for labeling by inviting Dr.Stephen
> Torkpor from University of Ghana who is an Agricultural Scientist to provide
> extensive training to team annotators to ensure consistency and accuracy.

  * •

> The team shared ambiguous or uncertain labeling cases with Dr. Stephen
> Torkpor of University of Ghana for clarification.

  * •

> The team conducted regular team-internal evaluations of the annotated data
> to ensure that the dataset was of high quality and suitable for the intended
> use.

Below are some guidelines for labeling or annotating the data set by team.

  * •

> Provide enough space to capture the affected area without cutting any part
> off, but avoid introducing too much space.

  * •

> If possible, zoom into the affected area to ensure accurate annotation.

Annotation of large affected areas

  * •

> Avoid including too much gap between two affected areas. Treat a wide gap as
> another annotation.

  * •

> To ensure completeness, try to annotate every possible instance as long as
> it is visible.

Careful annotation

  * •

> Even for images that appear to have no affected areas, zoom in and carefully
> examine the image to ensure nothing is missed. It is better to have an
> annotation than to remove the image altogether. However, if truly nothing
> exists, discard the image from the database.

Avoid human faces

  * •

> Avoid using images with human faces whenever possible.

Cropping out human faces

  * •

> If an image with a human face is necessary, but the face is not centered,
> consider cropping it out and replacing the original image with the cropped
> version.

_Is there anything about the composition of the dataset or the way it was
collected and pre-processed/cleaned/labeled that might impact future uses? For
example, is there anything that a future user might need to know to avoid uses
that could result in unfair treatment of individuals or groups (e.g.,
stereotyping, quality of service issues) or other undesirable harms (e.g.,
financial harms, legal risks) If so, please provide a description? Is there
anything a future user could do to mitigate these undesirable harms?_

None that the KaraAgro AI team is aware of.

###  F.7 3.7 Maintenance

_Who will be supporting/hosting/maintaining the dataset?_

KaraAgro AI will support, host, and maintain the dataset.

_How can the dataset owner/curator/manager be contacted (e.g., email
address)?_

Darlington Akogo can be contacted on his email address - darlington@gudra-
studio.com.

_Is there an erratum? If so, please provide a link or other access point._

No

_Will the dataset be updated (e.g., correcting labeling errors, adding new
instances, deleting instances)? If so, please describe how often, by whom, and
how updates will be communicated to users (e.g., mailing list, GitHub)?_

The dataset will be open sourced such that people can contribute to it. Any
changes to the dataset will be documented in a form of dataset updates

_If the dataset relates to people, are there applicable limits on the
retention of the data associated with the instances (e.g., were individuals in
question told that their data would be retained for a fixed time and then
deleted)? If so, please describe these limits and explain how they will be
enforced._

Not applicable.

_Will older versions of the dataset continue to be
supported/hosted/maintained? If so, please describe how. If not, please
describe how its obsolescence will be communicated to users._

The dataset will be hosted on public servers. Older version links will still
be available in a form of versioning.

_If others want to extend/augment/build on/contribute to the dataset, is there
a mechanism for them to do so? If so, please provide a description. Will these
contributions be validated/verified? If so, please describe how. If not, why
not? Is there a process for communicating/distributing these contributions to
other users? If so, please provide a description. Other researchers are
allowed to extend this dataset._

The interested researchers should send an email to<EMAIL_ADDRESS>owned by Darlington Akogo and they will be able to discuss the dataset
building, extensions, and contributions.

_[ Refer to sustainability strategy documentation on how to contribute to this
project ]_

References

  1. 1.

> Patrick Ateah Yeboah, Bismarck Yelfogle Guba, Emmanuel K. Derbile,
> Smallholder cashew production and household livelihoods in the transition
> zone of Ghana, Geo: Geography and Environment, 10.1002/geo2.120, 10, 1,
> (2023).