Information Processing in Agriculture最新文献

Sugarcane planting is an important and growing activity in Brazil. Thereupon, several techniques have been developed over the years to maximize crop productivity and profit, amongst them, processing of sugarcane field images. In this sense, this research aims to identify and analyze crop rows and measure their gaps from aerial images of sugarcane fields. For this, a small Remotely Piloted Aircraft captured the images, generating orthomosaics of the areas for analysis. Then, each orthomosaic is classified with the K-Nearest Neighbor algorithm to segment regions of interest. Planting row orientation is estimated using the RGB gradient filter. Morphological operations and computational geometry models are then used to detect and map rows and gaps along the planting row segment. To evaluate the results, crop rows are mapped and compared to manually taken measurements. Our technique obtained an error smaller than 2% when compared to gap length in crop rows from an orthomosaic with the area of 8.05 ha (ha). The proposed approach can map the positioning of the automatically generated row segments appropriately onto manually created segments. Moreover, our method also achieved similar results when confronted with a manual technique for differing growth stages (40 and 80 days after harvest) of the sugarcane crop. The proposed method presents a great potential to be adopted in sugarcane planting monitoring.

Oil content estimation in palm fruits is a precious property that significantly impacts oil palm production, starting from the upstream and downstream. This content can be used to monitor the progress of the oil palm fresh fruit bunch (FFB) and be applied to identify product profitability. Based on the near-infrared (NIR) signals, this study proposes an empirical mode decomposition (EMD) technique to decompose signals and predict the oil content of palm fruit. First, 350 palm fruits with Tenera varieties (Elaeis guineensis Jacq. var. tenera), at various ages of maturity, were harvested from the Cikabayan Oil Palm Plantation (IPB University, Indonesia). Second, each sample was sent directly to the laboratory for NIR signal measurements and oil content extraction. Then, the EMD analysis and artificial neural network (ANN) were employed to correlate the NIR signals and oil content. Finally, a robust EMD-ANN model is generated by optimizing the lowest possible errors. Based on performance evaluation, the proposed technique can predict oil content with a coefficient of determination (R²) of 0.933 ± 0.015 and a root mean squared error (RMSE) of 1.446 ± 0.208. These results demonstrate that the model has a good predictive capacity and has the potential to predict the oil content of palm fruits directly, without neither solvents nor reagents, which makes it environmentally friendly. Therefore, the proposed technique has a promising potential to be applied in the oil palm industry. Measurements like this will lead to the effective and efficient management of oil palm production.

A large loss occurs in the combine harvesting of rapeseeds due to the fragility of rapeseed pods, and all the more so with the vibration of the combine header and the collision between the header and plants. Seed loss is greatly affected by the biomechanical properties of ready-to-harvest rapeseed plants. To understand the mechanism of pod cracking and seed loss and to propose measures for alleviating them, it is needed to study the biomechanical properties of ready-to-harvest rapeseed plants. To this end, “Huayouza 62”, a widely planted rapeseed variety in central China, was selected to study the biomechanical properties, including pod-cracking resistance, main stem-shearing resistance and resonant frequencies, of whole plants. The results showed that the distribution of pod-cracking resistance forces was 1.333–6.100 N in the mature stage, and the pod width and thickness had a significant influence on the cracking resistance. The main influencing factor of the main stem-shearing resistance was the stem diameter. A thicker main stem resulted in a larger shearing resistance force but a smaller shear stress. The moisture contents of the main stems varied from 47.71% to 76.13%. However, the varying moisture contents did not show a significant impact on the shearing resistance. The resonant frequencies of whole rapeseed plants ready for harvest ranged from 6.5 Hz to 7.5 Hz, which was close to the excitation frequency of the cutter bar on the 4LL-1.5Y harvester. This study lays a foundation for improving the design and construction of harvesting devices for rapeseed plants to reduce seed loss.

The objective of this study is to investigate the effectiveness of a multi-parameter intelligent fault diagnosis method based on a modified random forest algorithm (RFNB algorithm), so as to reduce the impact of blockage fault on the operation of a silage harvester, thus providing a reference for the intelligent control. In brief, the forward speed, cutting speed, engine speed and engine load were selected as the input variables. Then, a random forest (RF) was used to construct a naive Bayes classifier for each node of the decision tree, and finally the RFNB algorithm constituted based on the naive Bayes tree (NBTree). The results revealed that by improving the classification accuracy of a single decision tree, the fault diagnosis accuracy of the entire RF was improved. When the sample data were consistent, the accuracy of the RFNB algorithm was 97.9%, while that of the RF algorithm was only 93.27%. Besides, the performance of RFNB classifiers was significantly better than that of RF classifiers. In conclusion, the RFNB model can accurately identify the fault status of the silage harvester with its good robustness, which provides a new idea for the fault monitoring and early warning of large agricultural rotating machinery in the future.