Model for predicting the nitrogen content of rice at panicle initiation stage using data from airborne hyperspectral remote sensing

Airborne hyperspectral remote sensing was used to provide data for a general-purpose model for predicting the nitrogen content of rice at panicle initiation stage using three years of data. There were significant differences between the vegetation data which were affected by the uptake of nitrogen from the soil depending on weather conditions. Therefore, the reflectance values obtained for one year may exhibit a different trend, due to the lack of vegetation. When the partial least squares regression (PLSR) models were estimated using all combinations of the three-year data, except for the model incorporating the data from 2005, correlation coefficients (r) were greater than 0.758, and the root mean squared error (RMSE) of prediction of the full-cross validation was less than 0.876 g m-2. The accuracy of the 2003-2004-2005 model was determined using five latent variables (PCs), with r = 0.938 and RMSEP = 0.774 g m-2. There were two different patterns for the regression coefficients associated with the NIR or red-edge regions. When the 2003-2004 model was validated using the data from 2005, the prediction error of the PLSR model was 1.050 g m-2. This became 2.378 g m-2 for the 2003-2005 model using the data from 2004 and 5.061 g m-2 for the 2004-2005 model with the data from 2003. There were similarities and differences for each latent variable between the 2003-2004 model and the 2003-2004-2005 model. The 2003-2004-2005 model might be more suitable for use as a general-purpose model, because it is possible to consider and validate all of the three years data

Multivariate analysis of nitrogen content for rice at the heading stage using reflectance of airborne hyperspectral remote sensing

Airborne hyperspectral remote sensing was adapted to establish a general-purpose model for quantifying nitrogen content of rice plants at the heading stage using three years of data. There was a difference in dry mass and nitrogen concentration due to the difference in the accumulated daily radiation (ADR) and effective cumulative temperature (ECT). Because of these environmental differences, there was also a significant difference in nitrogen content among the three years. In the multiple linear regression (MLR) analysis, the accuracy (coefficient of determination: R2, root mean square of error: RMSE and relative error: RE) of two-year models was better than that of single-year models as shown by R2 ≥ 0.693, RMSE ≤ 1.405 g m−2 and RE ≤ 9.136%. The accuracy of the three-year model was R2 = 0.893, RMSE = 1.092 g m−2 and RE = 8.550% with eight variables. When each model was verified using the other data, the range of RE for two-year models was similar or increased compared with that for single-year models. In the partial least square regression (PLSR) model for the validation, the accuracy of two-year models was also better than that of single-year models as R2 ≥ 0.699, RMSE ≤ 1.611 g m−2 and RE ≤ 13.36%. The accuracy of the three-year model was R2 = 0.837, RMSE = 1.401 g m−2 and RE = 11.23% with four latent variables. When each model was verified, the range of RE for two-year models was similar or decreased compared with that for single-year models. The similarities and differences of loading weights for each latent variable depending on hyperspectral reflectance might have affected the regression coefficients and the accuracy of each prediction model. The accuracy of the single-year MLR models was better than that of the single-year PLSR models. However, accuracy of the multi-year PLSR models was better than that of the multi-year MLR models. Therefore, PLSR model might be more suitable than MLR model to predict the nitrogen contents at the heading stage using the hyperspectral reflectance because PLSR models have more sensitive than MLR models for the inhomogeneous results. Although there were differences in the environmental variables (ADR and ECT), it is possible to establish a general-purpose prediction model for nitrogen content at the heading stage using airborne hyperspectral remote sensing

Using multiple sensors to detect uncut crop edges for autonomous guidance systems of head-feeding combine harvesters

This study proposes a method for detection of uncut crop edges using multiple sensors to provide accurate data for the autonomous guidance systems of head-feeding combine harvesters widely used in the paddy fields of Japan for harvesting rice. The proposed method utilizes navigation sensors, such as a real-time kinematic global positioning system (RTK-GPS), GPS compass, and laser range finder (LRF), to generate a three-dimensional map of the terrain to be harvested at a processing speed of 35 ms and obtain the crop height. Furthermore, it can simultaneously detect the uncut crop edges by RANdom SAmple Consensus (RANSAC). The average of the lateral offset value and crop height of the uncut crop edge detected by the proposed method were 0.154 m and 0.537 m, respectively

Efficient searching for grain storage container by combine robot

Partly presented at the 6th International Symposium on Machinery and Mechatronics for Agricultural and Biosystems Engineering ISMAB 2012.In this study, a combine robot was equipped with an autonomous grain container searching function. In order to realize automated grain unloading, the combine robot has to search and identify the grain storage container in an outdoor environment. A planar board was attached to the container. The marker was searched for using a camera mounted on the unloading auger of the combine. An efficient marker searching procedure was proposed on the basis of a numerical analysis of the camera's field of view and was verified experimentally. The results showed that the combine robot efficiently searched for and detected the marker and positioned its spout at the target point over the container to unload the grain

Integrating remote sensing and GIS for prediction of rice protein contents

In this study, protein content (PC) of brown rice before harvest was established by remote sensing (RS) and analyzed to select the key management factors that cause variation of PC using a GIS database. The possibility of finding out the key management factors using GreenNDVI was tested by combining RS and a GIS database. The study site was located at Yagi basin (Japan) and PC for seven districts (85 fields) in 2006 and nine districts (73 fields) in 2007 was investigated by a rice grain taste analyzer. There was spatial variability between districts and temporal variability within the same fields. PC was predicted by the average of GreenNDVI at sampling points (Point GreenNDVI) and in the field (Field GreenNDVI). The accuracy of the Point GreenNDVI model (r 2 > 0.424, RMSE 0.250, RMSE < 0.298%). A general-purpose model (r 2 = 0.392, RMSE = 0.255%) was established using 2 years data. In the GIS database, PC was separated into two parts to compare the difference in PC between the upper (mean + 0.5SD) and lower (mean − 0.5SD) parts. Differences in PC were significant depending on the effective cumulative temperature (ECT) from transplanting to harvest (Factor 4) in 2007 but not in 2006. Because of the difference in ECT depending on vegetation term (from transplanting to sampling), PC was separated into two groups based on the mean value of ECT as the upper (UMECT) and lower (LMECT) groups. In 2007, there were significant differences in PC at LMECT group between upper and lower parts depending on the ECT from transplanting to last top-dressing (Factor 2), the amount of nitrogen fertilizer at top-dressing (Factor 3) and Factor 4. When the farmers would have changed their field management, it would have been possible to decrease protein contents. Using the combination of RS and GIS in 2006, it was possible to select the key management factor by the difference in the Field GreenNDVI

Vision-based uncut crop edge detection for automated guidance of head-feeding combine

This study proposes a vision-based uncut crop edge detection method to be utilized as a part of an automated guidance system for a head-feeding combine harvester, which is widely used in Japan for the harvesting of rice and wheat. The proposed method removes the perspective effects of the acquired images by inverse perspective mapping and recovers the crop rows to their actual parallel states. Then, the uncut crop edges are detected by applying color transformation and the edge detection method. The proposed method has shown outstanding detection performance on the images acquired under various conditions of the paddy field with an average accuracy of 97% and a processing speed of 33 ms per frame

Implementation of deep-learning algorithm for obstacle detection and collision avoidance for robotic harvester

Convolutional neural networks (CNNs) are the current state of the art systems in image semantic segmentation (SS). However, because it requires a large computational cost, it is not suitable for running on embedded devices, such as on rice combine harvesters. In order to detect and identify the surrounding environment for a rice combine harvester in real time, a neural network using Network Slimming to reduce the network model size, which takes wide neural networks as the input model, yielding a compact model (hereafter referred to as “pruned model”) with comparable accuracy, was applied based on an image cascade network (ICNet). Network Slimming performs channel-level sparsity of convolutional layers in the ICNet by imposing L1 regularization on channel scaling factors with the corresponding batch normalization layer, which removes less informative feature channels in the convolutional layers to obtain a more compact model. Then each of the pruned models were evaluated by mean intersection over union (IoU) on the test set. When the compaction ratio is 80%, it gives a 97.4% reduction of model volume size, running 1.33 times faster with comparable accuracy as the original model. The results showed that when the compaction ratio is less than 80%, a more efficient (less computational cost) model with a slightly reduced accuracy in comparison to the original model was achieved. Field tests were conducted with the pruned model (80% compaction ratio) to verify the performance of obstacle detection. Results showed that the average success rate of collision avoidance was 96.6% at an average processing speed of 32.2 FPS (31.1 ms per frame) with an image size of 640 × 480 pixels on a Jetson Xavier. It shows that the pruned model can be used for obstacle detection and collision avoidance in robotic harvesters