Advancement in Soil Analysis for Tailored Tomato Cultivation Using Deep Learning & IoT

Abstract

Abstract: In the agriculture sector, one of the major problems in the plants is its seed diseases. The seed diseases can be caused by various factors such as viruses, bacteria, fungus etc. Most of the farmers are unaware of such diseases. That's why the detection of various diseases of seeds is very essential to prevent the damages that it can make to the plants itself as well as to the farmers and the whole agriculture ecosystem. Regarding this practical issues, this research aimed to classify and detect the plant's diseases automatically especially for the tomato plant. As per the hardware requirement, PC Sis the major computing unit. Image processing is the key process of the project which includes image acquisition, adjusting image ROI, feature extraction and convolution neural network (CNN) based classification. Here, Python programming language, OPENCV library is used to manipulate raw input image. To train on CNN architecture and creating a machine learning model that can predict the type of diseases, image data is collected from the authenticated online source. On providing the soil nutrient values obtained using NPK sensor, the recommendation of the suitable crop is displayed. The proposed system takes nitrogen, phosphorus, potassium values from the soil and recommends crop that is best suitable for the soil.