The agriculture sector plays a big part in the overall economy of any country. The population of the world is increasing day by day, which is also increasing the overall demand of the food. Due to various diseases and nature of the soil, it is difficult to meet the overall demand of the food. The agronomists and farmers also face many problems in the agriculture sector such as disease identification, knowing nature of the soil, pesticide management, etc. The old methods of managing crop do not help in an effective way in meeting the increasing demand for food. The modern methods such IOT, machine, deep learning, and image processing help to identify the crop health and to predict crop yield. This chapter generally discusses various state-of-the-art technologies and methods for predicting crop yield and disease identification.
This study based on survey, by using four higher educational (Universities) websites were selected for the usability testing with the help of response to the experience of eighty students of same age group and investigated to make pre-survey and post-survey based on an eight questionnaire for websites usability. The source for the survey was Laptops of windows 8.1 operation system used. The questionnaires were depends on two factors: one factor contains gender, nationality, respondents and second factor contains strongly agree, Agree, Undecided, Disagree, Strongly Disagree. The factor structure replicated across the study with data collected during usability test respectively in survey. There was evidence of usability with existing questionnaires, including the website usability testing by applying guidelines of webcredible. The overall results were acceptable and more meaningful for future researchers and webdevelopers. The questionnaire can be used to understand of websites quality and how well websites work.
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