Abstract : Background: Vegetable agriculture is very important to human continued existence and remains a key driver of many economies worldwide, especially in underdeveloped and developing economies. Objectives: There is an increasing demand for food and cash crops, due to the increasing in world population and the challenges enforced by climate modifications, there is an urgent need to increase plant production while reducing costs. Methods: In this paper, Lemon classification approach is presented with a dataset that contains approximately 2,000 images (...) belong to 3 species at a few developing phases. Convolutional Neural Network (CNN) algorithms, a deep learning technique extensively applied to image recognition was used, for this task. The results: found that CNN-driven lemon classification applications when used in farming automation have the latent to enhance crop harvest and improve output and productivity when designed properly. The trained model achieved an accuracy of 99.48% on a held-out test set, demonstrating the feasibility of this approach. (shrink)

Theory without concepts is blind. Concept without ideas is empty. Ideas lead a researcher to find some phenomenon. Certaintly, phenomenon isn’t come partially or separately. Every act, habit, mindset, and human behavior in everyday activity rise up categorization. The task of researcher is to find out that categorization. Behind that situation and absolutely when we involve with participant, sometimes we can’t avoid from our contruct of ideas or concepts. Sometimes between real context and research perspectives comes differing. So, the important (...) thing is how our theory can describe phenomenon and has clarity of definition in matter of terminology. (shrink)