This project presents a novel idea of detection and classification of leaf diseases and soil Moisture Level. It is difficult for human eyes to identify the exact type of leaf disease which occurs on the leaf of plant. Thus, in order to identify the leaf diseases accurately, the use of image processing and machine learning techniques can be helpful. The images used for this work were acquired from the field using digital camera. In pre-processing step, background removal technique is applied on the image in order to remove background from the image. Then, the background removed images are further processed for image segmentation using Otsu thresholding technique. Different segmented images will be used for extracting the features such as color, shape and texture from the images. At last, these extracted features will be used as inputs of classifier. Leaf diseases cause significant damage and economic losses in crops. Subsequently, reduction in leaf diseases by early diagnosis results in substantial improvement in quality of the product. Erroneous diagnosis of disease and its severity leads to inappropriate use of pesticides. The goal of proposed work is to diagnose the disease using image processing of plant leaf. In the proposed system, leaf image with complex background is taken as input. Thresholding is deployed to mask green pixels and image is processed to remove noise using anisotropic diffusion. Then leaf disease segmentation is done. The diseased portion from segmented images is identified. The purpose of this project is to developed our irrigation system, to make it automated and smart irrigation system. There are many countries where economy is depends on agriculture and the climatic conditions lead to lack of rains. The farmers working in the farm lands are dependent on the rains and bore wells. Even if the farm land has a water-pump, manual involvement by farmers is required to turn the pump on/off when on earth needed. The main purpose of this project is to measuring the moisture of agricultural soils by real-time method and to minimize this manual involvement by the farmer, which is why we are using a micro-controller. The sensor senses the amount of moisture present in the soil and presents an output in the form of resistance .If resistance is high then water is present in soil. Also we used another sensor to measure water level of well.
Image Processing, Internet of Things, Sensors
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