Prediction of rice harvest results based on aerial image segmentation of texture and color features in Nganggil Purwodadi Grobogan village - Eko Supriyadi; Agus Susilo Nugroho; Arindhya Ratna Hayuningtyas; Puput Rismawati; Zahra Fatma Saniya (Institute of Computer Science (IOCS), 2023)
Rice growth is very unstable in areas with large areas of land. The causes of unstable crop yields are caused by several factors, including natural factors, the type of rice planted, care models and also pests and weeds found in rice fields. Due to the large area of ??agricultural land, farmers cannot monitor the progress of the rice they plant. Monitoring of farmers is mostly only carried out in the edge areas of the rice fields, while those in the middle areas are most likely not included in monitoring. So this research will carry out a broad monitoring system that covers the entire rice field area. This system is carried out by taking pictures through the air using a drone. By using drones, the area coverage becomes wider and the image data obtained will then be processed to estimate the rice production results that will be obtained. In this imaging process, the k mean method is used to group images of agricultural areas. The identification process used is HSV color and texture using the Outsu and Canny algorithms for each part of the image. The default weight factor is the factor used to convert from RGB to HSV. With line selection, Parameters are culled: angle, length, mean, mode, bounding rectangle and standard deviation, min max values. The land area process where in this study there were 624 land images, this grouping produced areas that determined the shape of the rice or non-rice type images. From the table above, look for the average value of weight per kilogram and get predicted results with an average harvest area of ??21,078 quintals or 2.32 tons with an error rate of 0.82%.