In this article, a means of detecting diseases in cashew plants using digital image processing and a smartphone is presented. It is generally difficult, or even economically viable, for farmers to obtain a correct diagnosis of their plants. Thus, through digital image processing, rapid, easy and cost-effective diagnosis for disease detection can be carried out. And using the Android app makes it even more versatile. Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get the original essay Description: The described method uses leaves for disease detection and identification. Usually, diagnosis is made by observing the plants and identifying the disease (performed by experts). Brown and yellow spots, or early and late blights, as well as fungal, viral and bacterial diseases are very common plant diseases. Image processing uses image segmentation to identify the type of disease a plant has. This is not a first approach in the sense of “plant health monitoring”. The first step is acquiring images of plant leaves (cashew leaves in this case), the next step is leaf feature extraction, then statistical analysis and finally disease classification . Figures 1 and 2 are flowcharts but the blocks used are all rectangular; while a proper flowchart shows the workflow or process and uses appropriate start, input, processing, output and end blocks encapsulating the details. The color image captured using any camera is converted into a device-independent color space transformation. After applying some noise removal filters, image segmentation is performed using K-means clustering, converting the RGB image to HSI model, etc. The algorithm for K-means clustering 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.) subsequently extracted are used for classification. The classifier used in this case is Support Vector Machine (SVM), it is normally used for classification and pattern recognition. The accuracy of SVM increases with the number of samples used in the training dataset (supervised machine learning). Keep in mind: this is just a sample. Get a personalized article from our expert writers now. Get a Personalized Test Detection of diseases in plants is crucial. However, this can be tedious due to the extent of the areas covered by the plantations. And hiring professionals is an attractive but expensive option. The method proposed in this article is simple and easy; Anyone with a smartphone and computer with the appropriate software installed can use digital image processing and monitor large fields/plantations for potential diseases that threaten the produce. This article 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's feature dataset..