One of the most critical aspects of quality assurance is inspecting products for defects before they are sold or shipped. A good product is more vital than having more of the same item for a customer’s enjoyment. The client has a significant role in determining the quality of a product. Another way to think about quality is as the total of all the characteristics that contribute to the creation of items that the client enjoys. Recently, the application of machine vision and image processing technology to improve the surface quality of fruits and other foods has increased significantly. This is primarily because these technologies make significant advancements in areas where the human eye falls short. This means that, by utilizing computer vision and image processing techniques, time-consuming and subjective industrial quality control processes can be eliminated. This article discusses how to check and assess food using picture segmentation and machine learning. It is capable of classifying fruits and determining whether a piece of fruit is rotten. To begin, Gaussian elimination is used to remove noise from images. Then, photos are subjected to histogram equalization in order to improve their quality. Segmentation of the image is carried out using the K-means clustering technique. Then, fruit photos are classified using machine learning methods such as KNN, SVM, and C4.5. These algorithms determine if a fruit is damaged or not.

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In this paper we present a numerical algorithm for solving fuzzy differential equations based on Seikkala's derivative of a fuzzy process. We discuss in detail a numerical method based on a Runge-Kutta Nystrom method of order three. The algorithm is illustrated by solving some fuzzy differential equations.

In an agricultural country like India a lot of people will work with green thumb in mind. Most of the people loves to grow plants at home, but due to their work schedule they very often take care of plants. The only solution to this problem is smart monitoring of the plant growth by modernizing the current traditional methods of gardening. Hence the proposed system targets at smart way of monitoring the plant growth using automation and IoT technologies. Internet of things (IoT) provides various applications for crop growth and monitoring the growth conditions. Main theme of this paper is to increase the plant growth condition by maintaining the suitable moisture level and temperature with the use of moisture sensor and temperature sensor. This paper works for the crop development at low quantity water consumption by providing an automatic watering system to the user. People often waste lot of time for watering the plants so an efficient management of water should be developed. The proposed system will work based on the information send by the sensors and so proper growth for the plant will be estimated. With the help of the moisture sensor and temperature sensor details like moisture content and the temperature will be obtained and so based on those reading automatic watering will be done. The major advantage of this system is to make a suitable environment for plant growth and also to minimize the water consumption.

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