Although agriculture remains the dominant economic activity in many countries around the world, in recent years this sector has continued to be negatively impacted by climate change leading to food insecurities. This is so because extreme weather conditions induced by climate change are detrimental to most crops and affect the expected quantity of agricultural production. Although there is no way to fully mitigate these natural phenomena, it could be much better if there is information known earlier about the future so that farmers can plan accordingly. Early information sharing about expected crop production may support food insecurity risk reduction. In this regard, this work employs data mining techniques to predict future crop (i.e., Irish potatoes and Maize) harvests using weather and yields historical data for Musanze, a district in Rwanda. The study applies machine learning techniques to predict crop harvests based on weather data and communicate the information about production trends. Weather data and crop yields for Irish potatoes and maize were gathered from various sources. The collected data were analyzed through Random Forest, Polynomial Regression, and Support Vector Regressor. Rainfall and temperature were used as predictors. The models were trained and tested. The results indicate that Random Forest is the best model with root mean square error of 510.8 and 129.9 for potato and maize, respectively, whereas R2 was 0.875 and 0.817 for the same crops datasets. The optimum weather conditions for the optimal crop yield were identified for each crop. The results suggests that Random Forest is recommended model for early crop yield prediction. The findings of this study will go a long way to enhance reliance on data for agriculture and climate change related decisions, especially in low-to-middle income countries such as Rwanda.
Many countries worldwide face challenges in controlling building incidence prevention measures for fire disasters. The most critical issues are the localization, identification, detection of the room occupant. Internet of Things (IoT) along with machine learning proved the increase of the smartness of the building by providing real-time data acquisition using sensors and actuators for prediction mechanisms. This paper proposes the implementation of an IoT framework to capture indoor environmental parameters for occupancy multivariate time-series data. The application of the Long Short Term Memory (LSTM) Deep Learning algorithm is used to infer the knowledge of the presence of human beings. An experiment is conducted in an office room using multivariate time-series as predictors in the regression forecasting problem. The results obtained demonstrate that with the developed system it is possible to obtain, process, and store environmental information. The information collected was applied to the LSTM algorithm and compared with other machine learning algorithms. The compared algorithms are Support Vector Machine, Naïve Bayes Network, and Multilayer Perceptron Feed-Forward Network. The outcomes based on the parametric calibrations demonstrate that LSTM performs better in the context of the proposed application.
Image segmentation is the most challenging issue in computer vision applications. And most difficulties for crops management in agriculture are the lack of appropriate methods for detecting the leaf damage for pests' treatment. In this paper we proposed an automatic method for leaf damage detection and severity estimation of coffee leaf by avoiding defoliation. After enhancing the contrast of the original image using LUT based gamma correction, the image is processed to remove the background, and the output leaf is clustered using Fuzzy c-means segmentation in V channel of YUV color space to maximize all leaf damage detection, and finally, the severity of leaf is estimated in terms of ratio for leaf pixel distribution between the normal and the detected leaf damage.The results in each proposed method was compared to the current researches and the accuracy is obvious either in the background removal or damage detection.
Nowadays, building infrastructures are pushed to become smarter in response to desires for the environmental comforts of living. Enhanced safety upgrades have begun taking advantage of new, evolving technologies. Normally, buildings are configured to respond to the safety concerns of the occupants. However, advanced Internet of Things (IoT) techniques, in combination with edge computing with lightweight virtualization technology, is being used to improve users’ comfort in their homes. It improves resource management and service isolation without affecting the deployment of heterogeneous hardware. In this research, a containerized architectural framework for support of multiple concurrent deployed IoT applications for smart buildings was proposed. The prototype developed used sensor networks as well as containerized microservices, centrally featuring the DevOps paradigm. The research proposed an occupant counting algorithm used to check occupants in and out. The proposed framework was tested in different academic buildings for data acquisition over three months. Different deployment architectures were tested to ensure the best cases based on efficiency and resource utilization. The acquired data was used for prediction purposes to aid occupant prediction for safety measures as considered by policymakers.
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