Kentaro Nishiwaki

Estimate Crop Position Using Template Matching in Rice Production

To recognize crop positions by machine vision system is a basic technology to achieve a non-herbicidal, mechanical weeding method. But in fields, where lighting conditions change kaleidoscopically, it is very difficult to recognize the position stably. We propose a method to estimate crop position using a template matching. A template that scans matching area was designed including a crop model, intra- and inter-row spacings. A cross-correlation image was calculated between the template and a field image captured by a CCD camera. Local maximums in the cross correlation image were regarded as crop positions. Our method had high robustness to field conditions and ran stably with various images in rice fields.

A Robot System for Paddy Field Farming in Japan

Abstract The objective of this research is to realize fully robot-operated farming from tillage to harvest in large-scale agriculture such as cultivation of rice, wheat and soybean in Japan. For this purpose, three types of robot have been developed; the first is robot tractor, the second are rice transplanter robots, the third are combine harvester robots. RTK-GPS and inertia measurement unit (IMU), or GPS compass, are used for navigation system. Robots have Controller Area Network (CAN) bus that all sensors and PC can be connected in common among other robots such as tractors, rice transplanters, and combine harvesters. These robots could be done autonomous operation in paddy field. In addition, moving between the fields for effective operations and safe guideline for robot system were discussed.

A Global Positioning System Guided Automated Rice Transplanter

Abstract We have developed a new Global Positioning System (GPS) guided rice transplanter. We developed GPS and IMU guided rice transplanter in previous researches. Those were guided with GPS position data and inertia measurement unit (IMU) direction data. New one is guided with GPS position data with tilt correction during straight driving and guided with IMU direction data during turning at the headland. The GPS antenna is set to 1.5m height and 0.4m front offset from vehicle front axle. The actuator control command and data communication protocols comply with through the controller area network (CAN) bus. Steering and transmission are controlled by electrical actuators according to the location in a field. We report results of experiment.

Development of Robot Combine Harvester for Beans Using CAN Bus Network

Abstract A robot combine harvester for beans was developed using CAN Bus network. Two sensors are connected to the CAN Bus; a RTK-GPS to locate the position, and a GPS Compass to detect the heading direction. This robot is programmed to perform autonomous operation such as moving forward, backward, and turning in right angle. When the robot was tested in harvesting soybeans on a 100m × 30m rectangular field, the deviations between the target path and the robots traveling path was approximately 0.07m RMS, and the yaw angle error was approximately 1.82° RMS along the long side of the field. Therefore this robot can work autonomously along the target path without overlapping and shortening. In addition, this robot can unload harvested grain to adjacent transport truck during harvesting operation, which improved harvesting capacity to approximately 10%.

Development of Experimental Setup for Distinction of Disease Plant

The target of our research is to develop a technology for detecting vegetable infected by a disease at an early stage. We developed the experimental setup to capture hyper-spectral images of lettuce and attempted to detect the disease-infected area before the stage that human can easily recognize it. This experimental setup was composed of a hyper-spectral camera, a linear slider, a personal computer (OS:federa core 3), a signal conversion processor (PIC16f873), and sixteen Halogen lamps. This system makes it possible to make 2D spectral image, at regular intervals of about 8nm of wavelength in the range from 339nm to 1014nm. We found it possible to distinguish between healthy and dead part of lettuce plants where human couldn’t find it as a dead part.

Vision-Based Speed and Yaw Angle Measurement System

We propose a system that is able to detect vehicle speed and yaw angle based on vision sensors. In rice fields in Japan, rice is transplanted in an ordered grid pattern. We have already developed a vision system able to estimate plant positions with high precision based on this lattice layout. In this study, the inclination to the crop rows of vehicles (Yaw angle) was calculated from the crop position information acquired using our vision system. Next, two time lag images were compared and the speed of the vehicles was computed by tracing the motion of the same plant. Furthermore, the tracks of vehicles were calculated using these two sets of data. As a result of comparing the computed tracks with the position information obtained by a RTKGPS, when it was assumed that the RTK-GPS’s error was 0, the vehicle’s position was detectable within 30mm error.

Autonomous rice field operation project in NARO

We are engaged in research about autonomous rice field operation. We have developed an automated tractor, a rice transplanter and a combine harvester. These automated agricultural machineries are guided by a global positioning system (GPS) and a direction sensor such as inertia measurement unit (IMU) using the controller area network (CAN) bus. Because each machine is used for a limited period of time in a year, we plan to share a main control computer and navigation sensors among automated machineries. We expect to deliver autonomous operation in paddy field at lower cost and to shorten the development period of each machine. The actuator control command and data communication protocols comply with the ISO 11783. In this paper, we report about project outline and explain about a rice transplanter as an example of automated agricultural machinery.

Development of High-Throughput Field Phenotyping System Using Imagery from Unmanned Aerial Vehicle

Abstract. Phenomics research, which involves the determination of the relationship between phenotypes and genotypes, is constrained by the difficulties in phenotyping. Typically, the traits or characteristics of plants are manually measured or visually evaluated by humans for purposes such as, breeding and crop science. Therefore, general phenotyping is laborious, expensive, and subjective, whereas genotyping is readily conducted via DNA-sequencing techniques. The objective of this study was to develop a high-throughput phenotyping system that used field imagery from an unmanned aerial vehicle (UAV). The UAV was a multi-rotor drone that flew along a path preset by a global positioning system (GPS)-based navigation system. During the flight, a camera mounted on the UAV captured images of the field every 2 s. Because diseases on the leaves could be detected by image processing, disease severity was assessed via the aerial images of a potato field for breeding purposes. The areas under disease progress curves (AUDPCs) from the aerial images and conventional visual assessment were found to have a correlation; the coefficient of determination was R 2 = 0.77. In the large test field for crop studies, the aerial images were assembled, and a composite image of the entire field was generated. Furthermore, a three-dimensional (3D) model of the field was generated as a point cloud with 3D reconstruction techniques called structure from motion and semi-global dense matching. With these techniques, the physical size of the plants in the field, which is one of the growth parameters, was retrievable as digital information.

Standardized Control and Communication Data Network for a Small Range Agricultural Machinery

Abstract In this paper, development and standardization of on-vehicle control and communication data network for small size farm machinery is described. There already exist an international standard and many compliant products in the western countries. But, they are too huge and complicated for the average Japanese style of farming, which is represented by the words of small tractors and small paddy fields. In order to achieve an advancement and cost reduction in this field, enough and sufficient low cost simple electronic devices and related software were developed. The communication method adopted basically depends on the ISO 11783 standard technology, and some new features needed for the domestic agricultural conditions are considered. At the same time, a new domestic simple standard for data exchange between ECUs on tractors and attached implements are discussed. From some experiments, it became clear that the developed hardware and software together are strong tools to realize the domestic standardized devices. It can be said that standardization of data communication for lower range of farm machinery just has been started.