Omar Janvier Sinayobye

The prediction of biogas production is essential for optimizing operational conditions, enhancing process efficiency, and supporting sustainable energy systems. Traditional biogas yield prediction methods struggle to capture the nonlinear and complex interactions among influential factors such as feedstock composition, temperature, pH, and retention time. Machine learning (ML) models provide a promising alternative by analyzing patterns in historical data to make accurate, data-driven predictions. This study evaluates the effectiveness of six ML models Linear Regression (LR), Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), k-nearest Neighbors (k-NN), and Artificial Neural Networks (ANNs) for predicting biogas production on dataset of an experiment performed in 5 years from January 1, 2019, to October 30, 2024. Each model's performance was assessed using common evaluation metrics for regression analysis, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Squared (R²) Score, to compare their accuracy, robustness, and suitability for biogas data, which often involves nonlinear relationships and multivariate interactions. The findings demonstrate that DT and RF outperform simpler approaches in terms of accuracy with of 0.999 and 0.998 respectively, making them ideal for complex biogas prediction tasks. This study underscores the potential of ML models in optimizing biogas production systems and contributes to developing efficient, scalable solutions for renewable energy management.

Coffee leaf diseases are a significant challenge for coffee cultivation. They can reduce yields, impact bean quality, and necessitate costly disease management efforts. Manual monitoring is labor-intensive and time-consuming. This research introduces a pioneering mobile application equipped with global positioning system (GPS)-enabled reporting capabilities for on-site coffee leaf disease detection. The application integrates advanced deep learning (DL) techniques to empower farmers and agronomists with a rapid and accurate tool for identifying and managing coffee plant health. Leveraging the ubiquity of mobile devices, the app enables users to capture high-resolution images of coffee leaves directly in the field. These images are then processed in real-time using a pre-trained DL model optimized for efficient disease classification. Five models, Xception, ResNet50, Inception-v3, VGG16, and DenseNet, were experimented with on the dataset. All models showed promising performance; however, DenseNet proved to have high scores on all four-leaf classes with a training accuracy of 99.57%. The inclusion of GPS functionality allows precise geotagging of each captured image, providing valuable location-specific information. Through extensive experimentation and validation, the app demonstrates impressive accuracy rates in disease classification. The results indicate the potential of this technology to revolutionize coffee farming practices, leading to improved crop yield and overall plant health.

Rwandan coffee holds significant importance and immense value within the realm of agriculture, serving as a vital and valuable commodity. Additionally, coffee plays a pivotal role in generating foreign exchange for numerous developing nations. However, the coffee plant is vulnerable to pests and diseases weakening production. Farmers in cooperation with experts use manual methods to detect diseases resulting in human errors. With the rapid improvements in deep learning methods, it is possible to detect and recognize plan diseases to support crop yield improvement. Therefore, it is an essential task to develop an efficient method for intelligently detecting, identifying, and predicting coffee leaf diseases. This study aims to build the Rwandan coffee plant dataset, with the occurrence of coffee rust, miner, and red spider mites identified to be the most popular due to their geographical situations. From the collected coffee leaves dataset of 37,939 images, the preprocessing, along with modeling used five deep learning models such as InceptionV3, ResNet50, Xception, VGG16, and DenseNet. The training, validation, and testing ratio is 80%, 10%, and 10%, respectively, with a maximum of 10 epochs. The comparative analysis of the models’ performances was investigated to select the best for future portable use. The experiment proved the DenseNet model to be the best with an accuracy of 99.57%. The efficiency of the suggested method is validated through an unbiased evaluation when compared to existing approaches with different metrics.