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.

Wastewater generated by academic institutions poses an environmental burden due to its composition, including household, food-related waste, and potential microcontaminants such as pharmaceutical and laboratory chemicals. This study evaluated wastewater from eleven academic institutions. Horizontal subsurface flow constructed wetlands (HSFCWs) filled with locally sourced volcanic gravel were tested for treatment performance at a selected site, the University of Rwanda, College of Science and Technology (UR-CST). Six HSFCWs operated continuously for four months: two planted with Cyperus latifolius, two with Juncus effusus, and two unplanted. Academic wastewater showed average concentrations of 715 mg/L COD, 54 mg/L NH₄⁺-N, 9 mg/L NO₃⁻-N, and 32 mg/L PO₄³⁻-P. A 4-day hydraulic retention time was effective for the treatment process. The HSFCWs achieved removal efficiencies of 69–82% (COD), 66–87% (NH₄⁺-N), 69–90% (NO₃⁻-N), and over 90% for TSS and PO₄³⁻-P, emphasizing the strong physical and chemical purification capacity of volcanic gravel as confirmed by XRF analysis. Juncus effusus outperformed Cyperus latifolius in biomass (6667 vs. 4389 g/m²) and nutrient assimilation per unit area, removing 3.75× more TP and 1.46× more TN. The desorption capacity of the substrate enabled potential phosphorus recovery of up to 50%. Volcanic gravel-based CWs offer an effective, low-cost, and sustainable approach to wastewater treatment with potential for nutrient recovery.

Increasing water scarcity and pollution have intensified the need for low-cost wastewater treatment in developing regions. Constructed wetlands (CWs) offer a nature-based solution for pollutant removal but often fail, on their own, to meet discharge and reuse standards. This study evaluated four CW systems planted with Cyperus latifolius, Juncus effusus, Phragmites mauritianus, and Pennisetum purpureum, integrated with banana fibre filtration as a polishing step. The CWs alone achieved ammonium removal of 74–83%, nitrate 78–85%, phosphorus 86–91%, and COD 78–83%. Banana fibre filtration enhanced overall removal efficiencies to 93–96%, 94–96%, 81–88%, and 82–87% for ammonium, phosphorus, nitrate, and COD, respectively. Pennisetum purpureum had the highest aboveground nitrogen accumulation (74.4 g N/m²), with its coupled system achieving the highest nitrate removal, whereas Juncus effusus had the highest phosphorus accumulation (93.1 g P/m²), with its coupled system showing the best overall removal of ammonium, phosphorus, and COD. The integrated system reduced fecal coliforms and Escherichia coli by 6–8 log and eliminated detectable Salmonella and Shigella. Treated effluent met FAO irrigation, Rwanda discharge, and European Union (EU) standards for all evaluated parameters except phosphorus, which remained above the stricter EU limit. Despite bench-scale operation over four months, these findings demonstrate a low-cost, nature-based treatment approach suitable for decentralized wastewater treatment and reuse, with harvested wetland biomass offering additional potential for animal feed, energy, or fibre valorization.