The Effect of Harvesting Time in Moghan on Corn Losses and its Prediction using Fuzzy Logic

Abstract

Introduction Corn harvest losses are imposed by several factors, the most important of which is harvesting-time. Since the harvesting-time is coincident with the rainy season, it is necessary to appropriately estimate the corn harvest time to avoid harvesting losses and losing the next cultivation. Accordingly, in the current research, the effect of harvesting-time on corn losses during the month and the day has been into consideration. An expert fuzzy system was designed to predict the best harvest time as it operates based on the losses amounts which are measured in processing and collection units into the combine, and losses due to the humidity percentage. Materials and Methods In this paper, corn harvest losses in a John Deere Combine, Model 1165, was studied in a different climatic circumstance in Moghan region. Moreover, a split plot experiment in a completely randomized block design was conducted with three replications. The losses data were collected from the processing and collection units of the combine harvester on the November 5th, 8th and 11th, 2017, in three different daily times of 8-10, 11-13 and 14-16 with three replications. The Mamdani fuzzy inference system with singleton fuzzifire and center average defuzzifire‎ was used to develop a fuzzy expert system. In the designed expert system, the losses percentage in the processing and collection units and ‎the humidity percentage were considered as system inputs and optimal harvesting time was used as the system output. "Low, Very low, high and very ‎high" and "‏Best, Suitable, Unfit, and Worst" were four groups of linguistic variables for input and output parameters, respectively. These variables follow the triangular and trapezoidal membership functions. The number of 64 fuzzy rules were considered and introduced into the fuzzy system by experts, experienced farmers, and combiners. Furthermore, the same field data (measured data) were applied to evaluate the designed system, so that the predicted value was accounted as the system output. Results and Discussion Analysis of variance showed that there was a significant difference between the harvesting dates at the 0.05 probability level and significant difference between the harvesting times of a day at the 0.01 probability level. It can be concluded that the harvest dates and harvest times of a day were very effective in the number of corn losses, but the interaction effects were not significant. The results appeared that the lowest losses were 10.05% on November 8th, 2017, at 14-16 p.m., and the highest losses were 12.88% on November 11th, 2017, at 8-10 a.m.‎ The amount of losses was increased due to the higher air humidity and lower temperature. In the fuzzy simulation model, the suitable harvesting-time can be predicted based on the losses quantities in the processing and collection units and the humidity percentage. The results showed that the predicted values for harvesting-times, by a designed fuzzy system, were completely matched with measured values in this study. The coefficient of determination (R2) was 0.980 between measured and predicted harvesting times. This coefficient demonstrated that the developed fuzzy logic system was suitable for prediction of harvesting time in the studied area. Conclusion The experimental observations in the field and data analysis showed that in the corn harvesting in the Moghan region, the humidity level, date, and harvesting-time were the most effective factors in the harvesting losses. In this paper, based on measured data from a small farm and implementation of the expert fuzzy system, the most suitable harvest date was set on November 8th at 14-16 p.m, at 21-24°C and relative humidity of 44%-53% to have 10.5% losses which has been confirmed by the lowest losses observed in the corn plan (10%). Moreover, the high value of the determination coefficient demonstrates a high correlation between measured and predicted data.

Keywords

• corn
• fuzzy expert system
• harvest losses
• prediction of harvest time