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 R2 were calculated. For the 3-10-1 topology, the value of R2 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.

Barley has an important role in livestock feed. Therefore, an accurate estimation of harvesting time is necessary to minimize the loss in barley farming. The aim of this study is to determine barley harvest time using satellite images accurately. Field data were sampled from the farms in the Dezaj region of the west of Iran. In addition, satellite remote sensing technique was applied during barley growing season in 2019 using Landsat 8 images. The vegetation indexes were used as input in the prediction model in this study. The results showed that satellite imaging has enough potential to predict the harvesting time of barley accurately. R-squared and RMSE values of the best-structured stepwise regression model in this study were 0.791 as well, and 1.34 respectively. This method can be beneficially employed by farm managers to have an accurate estimation of the most appropriate harvesting time and be able to manage the process, which is an important challenge for them.

The process of Nokhodchi (roasted chickpea) production includes raw chickpea preparation, First Heat Treatment (FHT), Second Heat Treatment (SHT), Moisture Treatment (MT) and Dehulling and Roasting Treatment (DRT) , respectively. In this study; Time-dependent mechanical behavior of chickpeas under pressure load has been studied based on rheological theories. According to the results of the research, it was determined that the modulus and time of stress relaxation increased from raw chickpea phase to FHT, but decreased from FHT to SHT. It was determined that the module and time of stress relaxation, increased from MT phase to Nokhodchi phase. The volume of Nokhodchi increased by 20% compared to the raw chickpea and showed a lower resistance. The results showed that during the roasting process the crude fat increased, resulting in an increase in the nutritional quality of Nokhodchi.