Plant Disease Detection by means of K-Means Clustering and HSV Algorithm

The productivity of agriculture determines the economy of a country. To boost agricultural productivity and improve product quality, plant diseases must be identified. It is seen that the agricultural production is rapidly growing and various techniques are being used for it. One of the emerging technologies is fogponics. In this technology, plants are grown using fog which is directly exposed to roots instead of water. This methodology has a limitation of being infected with diseases due to a lack of nutrition. Plant leaf patterns can be observed visually to diagnose disease using traditional methods; however, they require the use of human resources. Image classification is a technique to detect the diseases of plants that are already trained to model. If the disease is detected, then acknowledgment has been sent to the user that the plant is affected by the disease. This dataset must be uploaded to carry out the software implementation, and specific operations must be carried out on it to obtain the findings, namely whether the disease is detected. Regarding real-time applications, any model (Raspberry Pi) is used to hold substantial amounts of data. Devoid of any datasets for model training, a few different techniques are applied to identify anomalous leaf conditions. A color feature of the leaf area is used in image-processing algorithms to detect plant infections and diseases.