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Farming is important because it plays a vital role in the economy of our country. For better crop production, choosing a suitable crop is very important. This can be decided based on many factors, such as soil type, temperature, water availability, market rate of the crop, crop storage capabilities, etc. The soil minerals play a vital role in finding suitable crops for cultivation. At present, sensors, drones, cameras, Wi-Fi, GPS, and many other advanced tools have been implemented for smart irrigation. All these are not affordable for common farmers, and many times they need technical experts. A model has been proposed that implements a few low-cost sensors to collect the soil minerals periodically and stores them in a low-cost cloud memory. Further, by implementing suitable machine learning and deep learning methods, the suitable crops are listed out. A smart phone application can be used to interact with cloud memory and analyse the data using learning methods. Furthermore, the same data can be used on a regular basis to determine soil mineral deficiency. A keen focus has been given to balancing the cost and expertise of implementing this model. [ABSTRACT FROM AUTHOR]

Image Segmentation refers to the process of partitioning a digital image into multiple segments. The goal of segmentation is to simplify and change the representation of an image into something that is more meaningful and easier to analyze. A Cellular Automata (CA) is a computing model of complex system using simple rule. It divides the problem space into number of cells and each cell can be in one or several final states. Cells are affected by its neighbor's to the simple rule. Learning Cellular Automata (LCA) is a variant of automata that interact with random environment having as goal to improve its behavior. This paper proposes an image segmentation technique based on LCA using soft computing approach. This proposed method works in two steps, the first step is called as soft segmentation where the input image(s) is/are analyzed through LCA and the second step is called as soft computing approach where the analyzed image is segmented through fuzzy C-means algorithm. [ABSTRACT FROM AUTHOR]