The central role of water access for agriculture is a clear challenge anywhere in the world and particularly in areas with significant seasonal variation in rainfall such as in Eastern and Central Africa. The combination of modern sensor technologies, the Internet, and advanced irrigation equipment combined in an Internet of Things (IoT) approach allow a relatively precise control of agricultural irrigation and creating the opportunity for high efficiency of water use for agricultural demands. This IoT approach can thereby increase the resilience of agricultural systems in the face of complex demands for water use. Most previous works on agricultural IoT systems are in the context of countries with higher levels of economic development. However, in Rwanda, with a low level of economic development, the advantages of efficient water use from the application of IoT technology requires overcoming constraints such as lack of irrigation control for individual farmers, lack of access to equipment, and low reliability of power and Internet access. In this work, we describe an approach for adapting previous studies to the Rwandan context for rice (Oryza sativa) farming with irrigation. The proposed low cost system would automatically provide irrigation control according to seasonal and daily irrigational needs when the system sensors and communications are operating correctly. In cases of system component failure, the system switches to an alternative prediction mode and messages farmers with information about the faults and realistic irrigation options until the failure is corrected. We use simulations to demonstrate, for the Muvumba Rice Irrigation Project in Northeast Rwanda, how the system would respond to growth stage, effective rainfall, and evapotranspiration for both correct operation and failure scenarios.

Crop damage attributed to pest birds is an important problem, particularly in low-income countries. This paper describes a prototype system for pest bird detection using a Conv1D neural network model followed by scaring actions to reduce the presence of pest birds on farms. Acoustic recorders were deployed on farms for data collection, supplemented by acoustic libraries. The sounds of pest bird species were identified and labeled. The labeled data were used in Edge Impulse to train a tinyML Conv1D model to detect birds of interest. The model was deployed on Arduino Nano 33 BLE Sense (nodes) and XIAO (Base station) microcontrollers to detect the pest birds, and based on the detection, scaring sounds were played to deter the birds. The model achieved an accuracy of 96.1% during training and 92.99% during testing. The testing F1 score was 0.94, and the ROC score was 0.99, signifying a good discriminatory ability of the model. The prototype was able to make inferences in 53 ms using only 14.8 k of peak RAM and only 43.8 K of flash memory to store the model. Results from the prototype deployment in the field demonstrated successful detection and triggering actions and SMS messaging notifications. Further development of this novel integrated and sustainable solution will add another tool for dealing with pest birds.

The impact of COVID-19 has been felt across all sectors, from transportation, education, and public works to the daily operations of businesses like selling, retailing, and so forth. The business sector is among those badly affected, especially micro, small, and medium enterprises. The understanding of ground prevailing conditions is key in driving informed policies that would have meaningful impact on society with regard to overcoming the effects of the virus. Hence, this work is an attempt to report the real ground statistics and necessity of technological support with the goal of submitting a report of recommended policies to the concerned authorities. In this direction, this work presents the outcome of a survey conducted to assess the impact of COVID-19 on operations of micro, small, and medium enterprises and also to find out the interventions put in place around business environments so as to enforce adherence to COVID-19 health safety measures. The survey was part of a study to develop automated IoT-powered technological solutions that would help to enforce proper mask wearing in indoor environments and also observance of social distance requirements within business premises. A customized questionnaire was designed to capture data on various aspects central to the focus of the study. The study was carried out in the month of May 2021, in the Huye district of Rwanda. According to the survey findings, the major challenges faced by businesses due to COVID-19 include failure by clients to settle bills, reduced ability to expand investment, difficulty in accessing inputs domestically, lower domestic sales to consumers, and lower domestic sales to businesses. The results also reveal some positive points that most businesses were found to have: hand washing points, hand sanitizer dispensers, and mechanisms to enforce social distance between customer and customer and also customer and front desk worker. In a nutshell, this work is unique in terms of (1) the customized questionnaire about Rwanda’s needs, (2) field visit-based data collection for accurate data, and (3) including an assessment of the importance of technological intervention for better handling of public safety, especially in the MSME business sector.

Rice yields in Rwanda seldom reach values expected for irrigated production, in part due to poor fertilization management. To adapt well-known best practices to local conditions, two fuzzy fertilization algorithms were designed for conditions of rice production in Rwanda. Fuzzy algorithms have a linguistic rule base of 183 IF THEN statements linking measurable field conditions to crop yield. Interviews with government agricultural experts and published knowledge of site conditions were used to create the rule base which incorporates the known nutrient requirements of the different growth stages of rice. Fuzzy algorithms are intended for use in an Internet of Things (IoT) system. To validate the algorithms, historical weather and field data are used to drive yield simulations for different plots during the first growing season of 2020 at sites in northeast Rwanda. Predicted yields are compared to measured yields for scenarios with different irrigation levels and fertilization amounts and with and without full Internet connectivity. Insights are provided on the potential of these fuzzy methods to improve rice nutrient management for increased yields in low-income countries are discussed.

Monitoring the farm acoustic environment can provide important information about processes affecting crop production. This paper assesses farmers’ knowledge and attitudes toward acoustic technology for farm monitoring in Rwanda. Stratified random sampling was used to select 430 farmers from 3 sectors. Demographic, farm, and technical knowledge data were collected from farmers with a survey. Cross-tabulation results show farmers with secondary and university education are more interested in acoustic technology than those with a primary education and farmers engaged in commercial farming are more interested in technology for monitoring acoustic sources than subsistence farmers. However, nearly all farmers are willing to deploy a listening technology to monitor the acoustic environment. Farmers have a clear priority to listen to animals on the farm, whether they are pests (specifically, birds) or livestock. A chi-square test of independence indicates a significant relationship between farmers’ consideration of technology for farm monitoring and their attitudes toward sound monitoring. Farmers want a technological solution, but demand that the solution be low-cost and provide a simple alert. The results inform system requirements for an Internet of Things acoustic network that can deliver this information to the farmer.

This study presents the results of a needs assessment done with small‐scale rice farmers in the Muvumba Valley irrigation scheme in Eastern Rwanda. The goal of the assessment was to learn about how farmers currently farm and what they need to help create a system that can help with rice production by telling them when to water and fertilize their crops. The research employed a mixed methodology. Data were collected from 325 of 2084 smallholder rice farmers in the Muvumba Valley. We made a structured questionnaire and used Kobo Toolbox to give it out. The survey asked farmers about their farming methods, how they monitor field conditions, and whether they would be open to using technology‐based solutions in agriculture. We used SPSS to analyze the quantitative data and create descriptive statistics. We also used content analysis to understand farmers’ experiences and feelings better. The assessment examined various aspects of rice farming, including farmers’ knowledge of soil types and rice varieties, common farming methods, access to farming supplies, watering and fertilizing practices, and how weather changes affect rice production. All participants indicated that rainfall patterns have changed in recent years, making it harder to get water and affecting how they water their plants. The findings revealed that while rice farmers in the Muvumba Valley face several production challenges, there is also a strong interest in technologies that can help improve irrigation and fertilization management, where results show that 99.40% of the farmers indicated willingness to learn and use smart farming systems.