Water stress is one of the most important growth-limiting factors in crop production around the world, water in plants is required to permit vital processes such as nutrient uptake, photosynthesis, and respiration. Drought stress in plants causes major production losses in the agricultural industry worldwide. There is no sensor commercially available for real-time assessment of health conditions in beans. Currently, there are several methods to evaluate the effect of water stress on plants and commonly practiced method over the years for stress detection is to use information provided by remote sensing. Studies exist which determined the effect of water stress in plants grown under the different watering regime, while other studies explore the performance of the artificial neural network techniques to estimate plant yield using spectral vegetation indices. This review recognizes the need for developing a rapid cost-effective, and reliable health monitoring sensor that would facilitate advancements in agriculture
Inclusion of Learners with disabilities continues to extensively rely on digital and Artificial Intelligence (AI) enabled Assistive Technologies (AT) as enablers for teaching and learning. However, the provision of ATs to meet the unique needs of PWDs continues to be a challenge. Moreover, such AI enabled ATs exist within areas of innovations, learning and working environments, hence the need for ease of learning, usage and cost effective acquisition and implementation. This paper introduces a systematic approach that matches the unique needs of PwD learners and abilities of innovators using AI-ATs. The research approach applies Design Thinking (DM), participatory elements enhanced with online collaborative tools. The study was conducted in 3 physically challenged pilot schools and an AT Centre at Meru University of science and technology. The objective of the study was to create better understanding of learners with physical disabilities and innovators with a view of enabling accurate identification, evaluation and choice of appropriate AI-ATs so as to develop learning and innovation spaces that enable the creation, introduction and testing of AI-ATs for e-inclusion. The expected outcome of this research is socio-economic inclusivity for livelihood empowerment as well as de-stigmatization of PwDs.
The inclusion of Learners with disabilities continues to extensively rely on digital and Artificial Intelligence (AI) enabled Assistive Technologies (AT) as enablers for Persons Living with Disabilities (PWD). However, the provision of ATs to meet the unique needs of PWDs continues to be a challenge. Moreover, such AI enabled ATs exist within areas of innovations, learning and working environments, hence the need for ease of learning, usage and cost effective acquisition and implementation. This paper introduces a systematic approach that matches the unique needs and abilities of innovators and learners in areas of innovation and special schools with AI-ATs that supports innovation and learning of PWDs. This approach applies Design Thinking (DM) approaches, participatory elements enhanced with online collaborative tools in three special schools and one area of innovation through two training cycles. The objective is to be able to better understand the target group of learners and innovators with physical disabilities, to enable accurate identification, evaluation and choice of appropriate AI-ATs so as to develop learning and innovation spaces that enable the creation, introduction and testing of AI-ATs for eInclusion. The approach was developed for an area of innovation that focuses on ATs and Special Schools for adoption in diverse settings with PWDs.
Plant Stress detection is a vital farming activity for enhanced productivity of crops and food security. Convolution Neural Networks (CNN) focuses on the complex relationships on input and output layers of neural networks for prediction. This task further helps in detecting the behavior of crops in response to biotic and abiotic stressors in reducing food losses. The enhancement of crop productivity for food security depends on accurate stress detection. This paper proposes and investigates the application of deep neural network to the tomato pests and disease stress detection. The images captured over a period of six months are treated as historical dataset to train and detect the plant stresses. The network structure is implemented using Google’s machine learning Tensor-flow platform. A number of activation functions were tested to achieve a better accuracy. The Rectifier linear unit (ReLU) function was tested. The preliminary results show increased accuracy over other activation functions.
This chapter introduces the service delivery challenges experienced by users of enterprise resource planning systems (ERP) by discussing the user perceptions. The authors administered questionnaires to users of ERP systems and user perception of ERPs was found to affect them in service delivery. Software complexity, software usability, and user resistance were found out as challenges contributing the challenge of service delivery. Attribution theory, diffusion of innovation theory, and compatibility maturity model are discussed; existing theories are discussed in the chapter. Findings are outlined and conclusion made based on the questionnaires addressed to the respondents.
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