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AfPCA Proceedings 2024

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Software and Mobile Applications
Artificial Intelligence (AI) in Agriculture
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Authors
Aboneh, T
Akaba, S
Nyawasha, R
Omega, S
Rorissa, P
Topics
Artificial Intelligence (AI) in Agriculture
Software and Mobile Applications
Type
Oral
Year
2024
2020
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Filter results3 paper(s) found.

1. Socio-demographic Factors Contributing to Adoption of E-commerce by Small Agribusiness Enterprises in the Upper East Region of Ghana

Technological revolution has become inevitable to development both to develop and developing economies in the 21th century. This revolution has triggered more advance market-base approaches to the transfer of goods and services, and information. This advance market-based approach has used the internet and electronic devices to bring suppliers and buyers more closely than ever before. Therefore, the adoption of E-commerce by Small agribusiness enterprises is inevitable as market exp... S. Omega, S. Akaba

2. An Ensemble-Based Deep Learning Approach for Early and Accurate Wheat Disease Detection

Crop diseases are the primarily cause for yield loss and a factor for food security issue around the globe. Crop diseases caused by pathogens pose a significant threat to global food security, the challenge become worst particularly in developing countries like Ethiopia. Rapid population growth and accurate disease identification is crucial for timely intervention and minimizing crop losses. However, traditional methods often rely on expert analysis, which can be time-consuming and resource-i... T. Aboneh, P. Rorissa

3. Multivariate Regional Deep Learning Prediction of Soil Properties from Near-Infrared, Mid-Infrared and Their Combined Spectra

Artificial neural network (ANN) models have been successfully used in infrared spectroscopy research for the prediction of soil properties. They often show better performance than conventional methods such as partial least squares regression (PLSR). In this study we develop and evaluate a multivariate extension of ANN for predicting correlated soil properties: total carbon (C), total nitrogen (N), clay, silt, and sand contents, using visible near-infrared (vis-NIR), mid-infrared (MIR) or comb... R. Nyawasha