Dry matter yield of corn (Zea mays L.) and Italian ryegrass (Lolium multiflorum Lam.) with double cropping was compared from May 2015 to September 2017 using two types of reduced tillage system (RTS) and the conventional tillage (plowing) system (CTS). The RTS used a vertical-axis type harrow and vacuum seeder (RTS1) or no-tillage seeder (RTS2) for seeding corn, and a disk harrow or vertical-axis type harrow for seeding Italian ryegrass. In RTS1, the average dry matter yield of corn for the three years was 1929kg/10a, while that of Italian ryegrass for two years was 1131kg/10a. In the CTS, the average dry matter yield of corn was 1915 kg/10a, and that of Italian ryegrass was 986kg/10a. The difference in annual dry matter yield between the RTS1 and CTS was not statistically significant. However, with the RTS2, dry matter yield of corn decreased from that in the CTS during the second year of the experiment. Dry matter yield of Italian ryegrass was not significantly different among the tillage systems

Grassland management often makes it easy for invasive weeds to grow without renovation of the grassland. The objective of this study is to develop a software that can automatically detect docks (Rumex spp.) —a major grassland weed— from aerial images captured by unmanned aerial vehicles, by using a convolutional neural network. Images of a grassland (8,240m²) were captured by unmanned aerial vehicles from an altitude of 10m. Patches of docks and patches of background were extracted from the image to establish training and validation data sets. A model for the identification of ducks was created in the convolutional neural network. The software was developed using the Python scripting language. To evaluate the detection accuracy of the developed software, each of the 12 aerial images taken during three different harvesting periods was analyzed. The detection accuracy for each period, compared to manual detection, was 85.4%, 78.6%, and 93.0%.