Harvest chronological planning using a method based on satellite-derived vegetation indices and artificial neural networks

Aim of study: Wheat appropriate harvest date (WAHD) is an important factor in farm monitoring and harvest campaign schedule. Satellite remote sensing provides the possibility of continuous monitoring of large areas. In this study, we aimed to investigate the strength of vegetation indices (VIs) derived from Landsat-8 for generating the harvest schedule regional (HSR) map using Artificial Neural Network (ANN), a robust prediction tool in the agriculture sector. Area of study: Qorveh plain, Iran. Material and methods: During 2015 and 2016, a total of 100 plots was selected. WAHD was determined by sampling of plots and specifying wheat maximum yield for each plot. The strength of eight Landsat-8 derived spectral VIs (NDVI, SAVI, GreenNDVI, NDWI, EVI, EVI2, CVI and CIgreen) was investigated during wheat growth stages using correlation coefficients between these VIs and observed WAHD. The derived VIs from the required images were used as inputs of ANNs and WAHD was considered as output. Several ANN models were designed by combining various VIs data. Main results: The temporal stage in agreement with dough development stage had the highest correlation with WAHD. The optimum model for predicting WAHD was a Multi-Layer Perceptron model including one hidden layer with ten neurons in it when the inputs were NDVI, NDWI, and EVI2. To evaluate the difference between measured and predicted values of ANNs, MAE, RMSE, and R-2 were calculated. For the 3-10-1 topology, the value of R-2 was estimated 0.925. A HSR map was generated with RMSE of 0.86 days. Research highlights: Integrated satellite-derived VIs and ANNs is a novel and remarkable methodology to predict WAHD, optimize harvest campaign scheduling and farm management.