Agriculture has a major role in the economic development of our country. Productive growth and high yield production of fruits is essential and required for the agricultural industry. Due to the lack of skilled workers, 30–35% of the harvested fruits is wasted. also, because of human perception subjectivity identification, classification and grading of fruits not done precisely. So, it is required to impose the automation system in the fruit industry. The machine learning techniques with adequate concepts of image processing have a great scope to provide intelligence for designing an automation system to differentiate the fruits according to its type, variety, matureness and intactness. Application of image processing has helped agriculture to improve yield estimation, disease detection, fruit sorting, irrigation and maturity grading. Image processing techniques can be used to reduce the time consumption and has made it cost efficient. In this paper, an automatic system is reviewed to identify the ripening stages of fruit from images. Various feature extraction is performed using different algorithm to get the low to high level features automatically and later classification is carried out using various machine learning algorithm to get ripening stages of fruit as predicted output.

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Banana is one of the most consumed fruits globally. It contributes about 16% of the world’s fruit production according to FAO. Maturity stage of fresh banana fruit is a principal factor that affects the fruit quality during ripening and marketability after ripening. The machine learning techniques with adequate concepts of image processing have a great scope to provide intelligence for designing an automation system to differentiate the fruits according to its type, variety, matureness and intactness. Application of image processing has helped agriculture to improve yield estimation, disease detection, fruit sorting, irrigation and maturity grading. In this paper, an automatic system is implemented to identify the ripening stages of banana from images. The feature extraction is performed using pre-trained deep convolution neural network i.e. Inception V3 to get the low to high level features automatically and later classification is carried out using various support vector machine learning algorithm to get ripening stages of fruit as predicted output.

Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene. Face detection can be regarded as a specific case of object-class detection. In object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given. As per the various face detection system seen various work done onto the detection with various way. In existing this are get evaluate with the HOG with SVM, which will help us to get the exact value so that it is necessary to implement the system which will more effective and advance. As per the face detection seen there are various face detection systems are implemented. Determining face is easy but recognition is quite typical so that we are proposed machine learning based face recognition with SVM which helps to determine and detect the faces So the proposed system will get integrated with highly efficient and effective SVM model for face recognition. The proposed methodology will help us to implement the face based security implementation in any security system like door lock, mobile screen lock etc.

A remote online carbon dioxide (CO2) concentration monitoring system is developed, based on the technologies of wireless sensor networks, in allusion to the gas leakage monitoring requirement for CO2 capture and storage. The remote online CO2 monitoring system consists of monitoring equipment, a data center server, and the clients. The monitoring equipment is composed of a central processing unit (CPU), air environment sensors array, global positioning system (GPS) receiver module, secure digital memory card (SD) storage module, liquid crystal display (LCD) module, and general packet radio service (GPRS) wireless transmission module. The sensors array of CO2, temperature, humidity, and light intensity are used to collect data and the GPS receiver module is adopted to collect location and time information. The CPU automatically stores the collected data in the server and displays them on the LCD display module in real-time. Afterwards, the GPRS module continuously wirelessly transmits the collected information to the data center server. The online monitoring Web GIS clients are developed using a PHP programming language, which runs on the Apache web server. MySQL is utilized as the database because of its speed and reliability, and the stunning cross browser web maps are created, optimized, and deployed with the Open Layers JavaScript web-mapping library.