Coffee and cashew nut dataset: A dataset for detection, classification, and yield estimation for machine learning applications.

Conventional methods of crop yield estimation are costly, inefficient, and prone to error resulting in poor yield estimates. This affects the ability of farmers to appropriately plan and manage their crop production pipelines and market processes. There is therefore a need to develop automated methods of crop yield estimation. However, the development of accurate machine-learning methods for crop yield estimation depends on the availability of appropriate datasets. There is a lack of such datasets, especially in sub-Saharan Africa. We present curated image datasets of coffee and cashew nuts acquired in Uganda during two crop harvest seasons. The datasets were collected over nine months, from September 2022 to May 2023. The data was collected using a high-resolution camera mounted on an Unmanned Aerial Vehicle . The datasets contain 3000 coffee and 3086 cashew nut images, constituting 6086 images. Annotated objects of interest in the coffee dataset consist of five classes namely: unripe, ripening, ripe, spoilt, and coffee_tree. Annotated objects of interest in the cashew nut dataset consist of six classes namely: tree, flower, premature, unripe, ripe, and spoilt. The datasets may be used for various machine-learning tasks including flowering intensity estimation, fruit maturity stage analysis, disease diagnosis, crop variety identification, and yield estimation.

Implementing Smartphone-Based Telemedicine for Cervical Cancer Screening in Uganda: Qualitative Study of Stakeholders' Perceptions.

BACKGROUND: In Uganda, cervical cancer (CaCx) is the commonest cancer, accounting for 35.7% of all cancer cases in women. The rates of human papillomavirus vaccination and CaCx screening remain low. Digital health tools and interventions have the potential to improve different aspects of CaCx screening and control in Uganda. OBJECTIVE: This study aimed to describe stakeholders' perceptions of the telemedicine system we developed to improve CaCx screening in Uganda. METHODS: We developed and implemented a smartphone-based telemedicine system for capturing and sharing cervical images and other clinical data, as well as an artificial intelligence model for automatic analysis of images. We conducted focus group discussions with health workers at the screening clinics (n=27) and women undergoing screening (n=15) to explore their perceptions of the system. The focus group discussions were supplemented with field observations and an evaluation survey of the health workers on system usability and the overall project. RESULTS: In general, both patients and health workers had positive opinions about the system. Highlighted benefits included better cervical visualization, the ability to obtain a second opinion, improved communication between nurses and patients (to explain screening findings), improved clinical data management, performance monitoring and feedback, and modernization of screening service. However, there were also some negative perceptions. For example, some health workers felt the system is time-consuming, especially when it had just been introduced, while some patients were apprehensive about cervical image capture and sharing. Finally, commonplace challenges in digital health (eg, lack of interoperability and problems with sustainability) and challenges in cancer screening in general (eg, arduous referrals, inadequate monitoring and quality control) also resurfaced. CONCLUSIONS: This study demonstrates the feasibility and value of digital health tools in CaCx screening in Uganda, particularly with regard to improving patient experience and the quality of screening services. It also provides examples of potential limitations that must be addressed for successful implementation.

Using machine learning for image-based analysis of sweetpotato root sensory attributes.

The sweetpotato breeding process involves assessing different phenotypic traits, such as the sensory attributes, to decide which varieties to progress to the next stage during the breeding cycle. Sensory attributes like appearance, taste, colour and mealiness are important for consumer acceptability and adoption of new varieties. Therefore, measuring these sensory attributes is critical to inform the selection of varieties during breeding. Current methods using a trained human panel enable screening of different sweetpotato sensory attributes. Despite this, such methods are costly and time-consuming, leading to low throughput, which remains the biggest challenge for breeders. In this paper, we describe an approach to apply machine learning techniques with image-based analysis to predict flesh-colour and mealiness sweetpotato sensory attributes. The developed models can be used as high-throughput methods to augment existing approaches for the evaluation of flesh-colour and mealiness for different sweetpotato varieties. The work involved capturing images of boiled sweetpotato cross-sections using the DigiEye imaging system, data pre-processing for background elimination and feature extraction to develop machine learning models to predict the flesh-colour and mealiness sensory attributes of different sweetpotato varieties. For flesh-colour the trained Linear Regression and Random Forest Regression models attained R2 values of 0.92 and 0.87, respectively, against the ground truth values given by a human sensory panel. In contrast, the Random Forest Regressor and Gradient Boosting model attained R2 values of 0.85 and 0.80, respectively, for the prediction of mealiness. The performance of the models matched the desirable R2 threshold of 0.80 for acceptable comparability to the human sensory panel showing that this approach can be used for the prediction of these attributes with high accuracy. The machine learning models were deployed and tested by the sweetpotato breeding team at the International Potato Center in Uganda. This solution can automate and increase throughput for analysing flesh-colour and mealiness sweetpotato sensory attributes. Using machine learning tools for analysis can inform and quicken the selection of promising varieties that can be progressed for participatory evaluation during breeding cycles and potentially lead to increased chances of adoption of the varieties by consumers.

A labeled spectral dataset with cassava disease occurrences using virus titre determination protocol.

In this work, we present a novel dataset composed of spectral data and images of cassava crops with and without diseases. Together with the description of the dataset, we describe the protocol to collect such data in a controlled environment and in an open field where pests are not controlled. Crop disease diagnosis has been done in the past through the analysis of plant images taken with a smartphone camera. However, in some cases, disease symptoms are not visible. Furthermore, for some cassava diseases, once symptoms have manifested on the aerial part of the plant, the root which is the edible part of the plant has been totally destroyed. The goal of collecting this multimodality of the crop disease is early intervention, following the hypothesis that diseased crops without visible symptoms can be detected using spectral information. We collected visible and near-infrared spectra captured from leaves infected with two common cassava diseases namely; Cassava Brown Streak Disease and Cassava Mosaic Disease, as well as from healthy plants. Together, we also captured leaf imagery data that corresponds to the spectral information. In our experiments, biochemical data is collected and taken as the ground truth. Finally, agricultural experts provided a disease score per plant leaf from 1 to 5, 1 representing healthy and 5 severely diseased. The process of disease monitoring and data collection took 19 and 15 consecutive weeks for screenhouse and open field, respectively, until disease symptoms were visibly seen by the human eye.

Performance and usability of a new mobile application for measuring respiratory rate in young children with acute lower respiratory infections.

OBJECTIVES: Respiratory rate (RR) measurement is critical to diagnosing pneumonia in resource-constrained settings, but accurate RR measurement is challenging. The acute lower respiratory illness treatment and evaluation (ALRITE) mobile phone application (app), designed to help healthcare workers (HCWs) manage pediatric respiratory illnesses, includes a semiautomated RR counter. This study aimed to evaluate the accuracy and usability of the ALRITE RR counter and a commercially available RR counter app, RRate, with a reference standard. METHODS: This was a cross-sectional observational study of HCWs. Participants used both apps to measure the RR of pediatric patients from standardized videos. The reference standard was determined by consensus of a manual 1-min count by two providers. We assessed agreement using Spearman's rank correlation coefficient and constructed Bland-Altman plots to determine bias and limits of agreement. Participants completed a usability survey. RESULTS: Thirty-nine HCWs participated. The agreement between the apps and reference standard (Spearman's coefficient) was 0.83 (95% confidence interval [CI]: 0.78-0.87) for ALRITE and 0.62 (95% CI: 0.52-0.70) for RRate. ALRITE had a bias of -2 breaths/min (lower limit of agreement [LoA] -16 to +12) and RRate had a bias of -0.4 breaths/min (LoA -24 to +23) compared to the reference standard. Both apps had a poorer agreement at higher RRs. Based on usability survey responses, 95% found ALRITE easy to use. CONCLUSIONS: The ALRITE RR counter has acceptable accuracy for counting RR in infants with respiratory distress, appears to be more accurate than a commercially available option, and was user-friendly. The ALRITE RR counter is a promising tool for meriting evaluation in real-world settings.

A dataset of cassava whitefly count images.

Whiteflies are insect vectors that affect a variety of plants such as tomatoes, cabbages, sweet potatoes, eggplants, and cassava. In Uganda, whiteflies are a major contributor to the spread of Cassava Brown Streak Disease (CBSD). By suckling on infected cassava plants, whiteflies can potentially transfer the Cassava Brown Streak Virus that causes CBSD to uninfected clean plants nearby when they migrate. When they attack the cassava plants in large numbers, whiteflies can also cause significant physical damage through suckling. This eventually can lead to leaf loss or plant death. Whiteflies also excrete "honeydew", which harbors a fungus known as "sooty mold" that covers the leaves, limiting access to sunlight which in turn affects plant food production. As part of their work, the cassava breeders often conduct studies to assess the population of whiteflies in cassava fields through a manual process of visual inspection which can be arduous and time-consuming. This paper presents a cassava whitefly dataset that has been curated to enable researchers to build solutions for the automation of the count and detection of whiteflies. The dataset contains 3,000 images captured in a whitefly trial site in Uganda. It depicts different variations of whitefly infestation from low to high infestation. This data has already been used to provide a proof-of-concept solution for whitefly counting based on Machine Learning approaches.

Integration of design smells and role-stereotypes classification dataset.

Design smells are recurring patterns of poorly designed (fragments of) software systems that may hinder maintainability. Role-stereotypes indicate generic responsibilities that classes play in system design. Although the concepts of role-stereotypes and design smells are widely divergent, both are significant contributors to the design and maintenance of software systems. To improve software design and maintainability, there is a need to understand the relationship between design smells and role stereotypes. This paper presents a fine-grained dataset of systematically integrated design smells detection and role-stereotypes classification data. The dataset was created from a collection of twelve (12) real-life open-source Java projects mined from GitHub. The dataset consists of 18 design smells columns and 2,513 Java classes (rows) classified into six (6) role-stereotypes taxonomy. We also clustered the dataset into ten (10) different clusters using an unsupervised learning algorithm. Those clusters are useful for understanding the groups of design smells that often co-occur in a particular role-stereotype category. The dataset is significant for understanding the non-innate relationship between design smells and role-stereotypes.

A dataset of necrotized cassava root cross-section images.

Cassava brown streak disease is a major disease affecting cassava. Along with foliar chlorosis and stem lesions, a very common symptom of cassava brown streak disease is the development of a dry, brown corky rot within the starch bearing tuberous roots, also known as necrosis. This paper presents a dataset of curated image data of necrosis bearing roots across different cassava varieties. The dataset contains images of cassava root cross-sections based on trial harvests from Uganda and Tanzania. The images were taken using a smartphone camera. The resulting dataset consists of 10,052 images making this the largest publicly available dataset for crop root necrosis. The data is comprehensive and contains different variations of necrosis expression including root cross-section types, number of necrosis lesions, presentation of the necrosis lesions. The dataset is important and can be used to train machine learning models which quantify the percentage of cassava root damage caused by necrosis.