Development of an artificial neural network-based model for estimating the active iron content in grape leaves

Iron is an essential element in the growth process of plants and plays a crucial role in chlorophyll production. Iron deficiency is a serious limitation in vineyards that can significantly affect both the yield and the quality of the crop. The use of modern methods such as digital image processing not only increases precision but also reduces the need for costly and time-consuming laboratory testing, thereby lowering costs and speeding up data-driven decision-making processes in orchard management. The aim of this study is to develop a system based on image processing and neural networks to estimate the active iron content in grape leaves. For this purpose, 55 leaf samples with different levels of iron deficiency were collected and analyzed from vineyards around Urmia. The total and active iron content in the samples was measured using atomic absorption spectroscopy and the leaves were photographed and processed under controlled light conditions. Statistical features were extracted from the images and their correlation with active and total iron content was analyzed. Finally, the best features were used to predict iron content using a multilayer artificial neural network. The results of the linear regression show that active iron correlates with the R, G, H, and S color channels with coefficients of 0.64, 0.58, 0.54, and 0.45, respectively, and that total iron does not correlate with the changes in leaf color. The neural network with an optimized structure of 8-9-1 was able to predict the data from the atomic absorption device with an accuracy of 0.83, 0.88, and 0.84 for training, test, and all data, respectively. In summary, image processing can be effectively and reliably used as a tool for optimal plant nutrition management and rapid diagnosis of iron deficiency.