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The paper presents a novel method for predictive modelling of chanterelle mushrooms' drying kinetics. Use of computational techniques and mathematical modelling for chanterelle mushrooms were employed in analysing the drying process using heat pump dryer. Mushrooms were sliced into 20 mm × 20 mm × 30 mm cuboids and 20 mm × O40 mm cylinders. Drying air temperatures of 40 °C, 48 °C, and 56 °C were studied. Determination of the best mushrooms' drying process using selected thin layer and computational intelligence models was examined. Drying curves showed a falling rate period while moisture ratio decreased with temperature rise from 40 °C to 56 °C. Mushrooms’ moisture diffusivities rose from 23.709 × 10−8 m2s−1 to 41.035 × 10−8 m2s−1 as temperature rose from 40 °C to 56 °C for the cuboid slices. Similarly, diffusivities for cylindrical mushrooms rose from 9.322 × 10−8 m2s−1 to 12.1585 × 10−8 m2s−1. The activation energy of cuboidal mushrooms was 29.3908 kJmol-1 while that of cylindrical sample was 14.2856 kJmol-1. Statistical parameters used showed that Midilli et al. was the most superior thin-layer drying model in predicting drying kinetics of chanterelle mushrooms. 1–3 ANN architecture was the best architectures in predicting drying process of chanterelle mushrooms. Standardized Pearson universal kernel was the best SVM filter. A k = 5 value was the best kNN values tested in predicting the chanterelle mushrooms drying process. However, tested data for standardized Pearson universal kernel was the most superior among conventional and intelligent models used. Consequently, computational intelligence modelling especially the standardized Pearson universal kernel was recommendable in modelling drying kinetics of chanterelle mushrooms under heat pump dryer.

To achieve a precise seeding and fertilization control on a seed-fertilizer drill machine, it is essential to first realize accurate on-line measurement of the application rate for the machine. This study was focused on creating an on-line solid fertilizer and seed application rate measuring system based on the weighing principle for seed-fertilizer drill machines. The invented device consisted of a spring set and a weighing sensor, with its weighing mechanism, accurately measures the application rate of the seed-fertilizer drill machine. A real-time signal processing algorithm based on the low-pass filter, mean filter and adaptive Kalman filter was formulated based on analyzing field test results under various conditions. The developed device (with the on-line signal processing algorithm) was integrated to a commercial seed-fertilizer drill machine widely used on Jiangsu region of China and tested in field operations. The parameters being collected for assessing the performance of this developed system included the cumulative and the dynamic application rates in real time. In the cumulative rate measurement tests, this system was operated at three different seeding and fertilizing rates under two operating speeds of the machine. Obtained results showed that the maximum relative error of the cumulative application rate was 4.88%, and the average error of 2.62% with a standard deviation of 1.7% over about 2.5 ha of operation area. In the dynamic rate measurement performance tests, the device was operated at four combinations of seeding and fertilizing rates under two operation speeds. Under the condition of no seeding and fertilization, the average error of the dynamic application rate measurement was 3.15% with a standard deviation of 1.64% over about 2.2 ha of operation area. The seed-fertilizer drill machine was also tested on a wheat seeding operation, the obtained performance results showed that the application rate measuring system can provide the dynamic and cumulative performance information of the seed-fertilizer drill machine. The fluctuation degree of dynamic application rate can illustrate the dynamic regulation performance of the machine. Obtained results verified that the developed solid fertilizer application rate measuring system could not only used to precisely measure application rate, but also used to provide a needed feedback information for accurately control the application via a closed-loop controller to the machine to achieve a variable rate application.

The traditional method based on passive light source for measuring vegetation indices (VIs) has poor adaptability to the light environment and is inconvenient to use. In this paper, utilizing the characteristics of constant proportion of solar radiation energy in each component band, the standard whiteboard response ratio CW with constant value was introduced to construct a new method for calculating VIs. The measurement system composing of two narrow-band cameras was used to determine CW for many times in 23 days under natural light conditions. The result of determination of CW was applied to measure the Normalized Difference Vegetation Index (NDVI) of the wheat canopy in a box planting area. The measured values of both CW and NDVI were stable and showed a strong adaptability to natural light intensity and measurement heights during tests. The coefficient of variation (CV) of CW and NDVI were 3.72% and 1.95%, respectively. The measured NDVI data were compared with those by GreenSeekerRT200, and the maximum, average and standard deviation of the absolute deviation rate were 5.65%, 1.49% and 1.06%, respectively. This showed that the new measurement method was effective and provided a new idea for measuring VIs.