• S.BALAJI
• Dr.G.M.Karpura deepan

This Agriculture has facing challenges due to receding water levels, climate change, shrinking amount of arable land and an increase in population and upward social mobility. Hence it is very difficult in traditional agriculture system to provide a fresh, clean food and to meet the need of the fast-growing population. Increasing agricultural production using modern innovative urban smart vertical farming techniques such as aeroponics and hydroponics system is top most solution to face these challenges. The soil-less cultivation in hydroponic and Aeroponics system aimed to improve the agriculture production efficiently and an environmental impact. In recent years an intelligent sensor techniques, The Internet of things (IoT) plays a big important role in agricultural industry to plan the several activities and missions properly by utilizing limited resources with minor human interference in order to provide a support to farmers such as growth monitoring system of temperature, humidity and water supply, and also early disease monitoring and detection system. In Aeroponics system, the plant diseases can affect the leaf any time between sowing and harvesting which leads to huge loss on the production of crop and economical value of market. Therefore, leaf disease detection plays a vital role and it requires huge manpower, more processing time and extensive knowledge about plant diseases. Hence detection and diagnosis of disease at the apparent time is crucial to the cultivator. Farmers find it cumbersome to analyze whether a plant is diseased or not manually as it requires a lot of time, labour and cost. Thus, it is always required to automate a system to identify if a plant is diseased or not earlier to evolve. This research work actualizes a machine learning system to recognize the type of malady on various plant species where phases include dataset acquisition, feature extraction, training and classification. The datasets of both healthy and diseased leaves are trained using various machine learning classifiers to detect diseases in plant leaves as it analyzes the data from different aspects, and classifies it into one of the predefined set of classes. The properties such as color, shape, intensity and dimensions of the plant leaves are taken into consideration for classification. In this paper we proposes a novel hybrid machine learning algorithm based on multi support vector machine for IOT based intelligent aeroponics system provides significant knowledge about early fault detection and diagnosis in which the farmer could monitor several parameters and they could control the entire system remotely and also to detect the various types of plant diseases and different classification techniques that are used for identifying diseases in different plant leaves

Support vector machine, IOT, Machine learning, nutrient solution, Aeroponics

"A Novel Machine Learning Algorithm for Detecting Leaf Diseases in IOT based Smart Vertical Aeroponics Farming", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.1036-1043, June-2019, Available :http://www.jetir.org/papers/JETIR1907U24.pdf

"A Novel Machine Learning Algorithm for Detecting Leaf Diseases in IOT based Smart Vertical Aeroponics Farming", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp1036-1043, June-2019, Available at : http://www.jetir.org/papers/JETIR1907U24.pdf