This article provides a way to detect diseases in cashew plants using digital image processing and a smartphone. It is usually difficult or not economically viable for farmers to obtain a correct diagnosis of their plants. Therefore, with the help of digital image processing it is possible to carry out a quick, simple and economical diagnosis for disease detection. And using the Android application makes it even more versatile. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essayDescription: The described method uses leaves for disease detection and identification. Usually the diagnosis is made by observing the plants and identifying the disease (made by experts). Brown and yellow spots, early and late burns, and fungal, viral, and bacterial diseases are some very common plant diseases. Image processing uses image segmentation to identify the type of disease a plant has. This is not the first approach in the direction of “monitoring plant health”. The first step is image acquisition of plant leaves (cashew leaves in this case), the next step is feature extraction from leaves, then statistical analysis, and finally disease classification. Figure-1 and Figure-2 are flowcharts but the blocks used are all rectangular; whereas a proper flowchart shows the workflow or process and makes use of proper start, input, process, output and end blocks that encapsulate the details. The color image captured using any camera is converted to a device-independent color space transform. After applying some noise removal filters, image segmentation is performed using K-means clustering, converting RGB image to HSI model etc. The K-means clustering algorithm used in this paper is J=|xn - µj|2 Where xn is a vector representing the nth data point and µj is the geometric centroid of the data points in Sj. The features (such as contrast, energy, correlation etc.) then extracted are used for classification. The classifier used in this case is Support Vector Machine (SVM), normally used for classification and pattern recognition. The accuracy of the SVM increases with the number of samples used in the training dataset (supervised machine learning). Please note: this is just a sample. Get a custom paper from our expert writers now. Get a Custom Essay Detecting diseases in plants is crucial. However, it can be complicated due to the large areas covered by the plantations. And hiring professionals is a viable but expensive option. The method proposed in this article is simple and easy; anyone with a smartphone and a computer with appropriate software installed can use digital image processing and monitor large fields/plantations for potential diseases that threaten produce. This paper talks about cashew plants but in general this method can be used for any other plant as long as the system is properly trained with the intended plant characteristics dataset.