Application of Machine Learning in Estimating Ontree Yield of Citrus Fruit

Abstract

Citrus is manually counted to estimate the yield. By using some innovative agricultural techniques yield and production can be increased. Numerous agricultural innovations have been introduced in recent years. Higher agricultural production, prediction, and reliable crop status information are more important than ever due to the expected growth of the human population. Agriculture has always been the foundation of human society. Current study was aimed to develop a reliable and meaningful information-gathering agricultural field based on image processing during 2020. Citrus yield can be increased in the initial stages by counting it with RGB and HSV-based images taken from an Android phone from various angles using machine learning techniques. Fertilizers such as potash, phosphorus, and nitrogen can then be utilized to boost yield. According to the findings, farmers can control and monitor citrus health production more efficiently and effectively by integrating machine learning with agriculture. The citrus calculation using the given technique compared with manually counted citrus, having difference of up to 5 to 10 citruses for a single plant per plot in a field. The proposed method produced excellent results under varying lighting conditions, leaf occlusion, and fruit overlap on photos taken at various distances from the orange trees.