An automated mechanical intra-row weed removal system for vegetable crops

This was the original project description: The goal of this project is to develop a practical mechanical intra-row weed control solution for automatically removing weeds from vegetable crops for small and mid-scale Iowa growers. Investigators will explore an optical sensing system and a mechanism to remove weeds with minimal soil disturbance, crop damage and energy input. The project also will demonstrate the effectiveness and economic viability of the system

Plant Identification in Mosaicked Crop Row Images for Automatic Emerged Corn Plant Spacing Measurement

Image processing algorithms for individual corn plant and plant stem center identification were developed. These algorithms were applied to mosaicked crop row image for automatically measuring corn plant spacing at early growth stages. These algorithms utilized multiple sources of information for corn plant detection and plant center location estimation including plant color, plant morphological features, and the crop row centerline. The algorithm was tested over two 41 m (134.5 ft) long corn rows using video acquired two times in both directions. The system had a mean plant misidentification ratio of 3.7%. When compared with manual plant spacing measurements, the system achieved an overall spacing error (RMSE) of 1.7 cm and an overall R2 of 0.96 between manual plant spacing measurement and the system estimates. The developed image processing algorithms were effective in automated corn plant spacing measurement at early growth stages. Interplant spacing errors were mainly due to crop damage and sampling platform vibration that caused mosaicking errors

Optimal Coverage Path Planning for Arable Farming on 2D Surfaces

With the rapid adoption of automatic guidance systems in agriculture, automated path planning has great potential to further optimize field operations. Field operations should be done in a manner that minimizes time and travel over field surfaces and should be coordinated with specific field operation requirements, machine characteristics, and topographical features of arable lands. To reach this goal, an intelligent coverage path planning algorithm is the key. To determine the full coverage pattern of a given field by using boustrophedon paths, it is necessary to know whether to and how to decompose a field into sub-regions and how to determine the travel direction within each sub-region. A geometric model was developed to represent this coverage path planning problem, and a path planning algorithm was developed based on this geometric model. The search mechanism of the algorithm was guided by a customized cost function resulting from the analysis of different headland turning types and implemented with a divide-and-conquer strategy. The complexity of the algorithm was analyzed, and methods for reducing the computational time are discussed. Field examples with complexity ranging from a simple convex shape to an irregular polygonal shape that has multiple obstacles within its interior were tested with this algorithm. The results were compared with other reported approaches or farmers\u27 recorded patterns. These results indicate that the proposed algorithm was effective in producing optimal field decomposition and coverage path direction in each sub-region

Real-Time Crop Row Image Reconstruction for Automatic Emerged Corn Plant Spacing Measurement

In-field variations in corn plant spacing and population can lead to significant yield differences. To minimize these variations, seeds should be placed at a uniform spacing during planting. Since the ability to achieve this uniformity is directly related to planter performance, intensive field evaluations are vitally important prior to design of new planters and currently the designers have to rely on manually collected data that is very time consuming and subject to human errors. A machine vision-based emerged crop sensing system (ECSS) was developed to automate corn plant spacing measurement at early growth stages for planter design and testing engineers. This article documents the first part of the ECSS development, which was the real-time video frame mosaicking for crop row image reconstruction. Specifically, the mosaicking algorithm was based on a normalized correlation measure and was optimized to reduce the computational time and enhance the frame connection accuracy. This mosaicking algorithm was capable of reconstructing crop row images in real-time while the sampling platform was traveling at a velocity up to 1.21 m s-1 (2.73 mph). The mosaicking accuracy of the ECSS was evaluated over three 40 to 50 m long crop rows. The ECSS achieved a mean distance measurement error ratio of -0.11% with a standard deviation of 0.74%

Field evaluation and system improvement of a semi-automated mechanical intra-row weeder for vegetable crops

This was the original project description: This is an expansion of a previous Leopold Center competitive grant (M2009-23), which supported the development of a basic semi-automated mechanical intra-row weed removal system for vegetable crops. The investigators will conduct field trials to evaluate and improve the prototype

Field-based Robotic Phenotyping for Sorghum Biomass Yield Component Traits Characterization Using Stereo Vision

Sorghum is known as a major potential feedstock for biofuel production. Being able to efficiently discover genetic control of many traits over a large number of genotypes, genome-wide association study (GWAS) has become a powerful tool for studying sorghum biomass yield components. However, automated high-throughput field-based plant phenotyping is now the bottleneck for scaling up such experiments. This paper presents an auto-guidance enabled utility tractor which navigates itself between crop rows with a predefined path while collecting stereo images of sorghum samples from both sides of the vehicle. Three levels of stereo camera heads were instrumented to capture images of plants up to 3 meters tall. The stereo images were processed offline to reconstruct 3D point clouds using Semi-Global Block Matching. A semi-automated software interface was developed to measure stem diameter due to the strict sampling strategy and the complexity of high-density crop canopy. An automated hedge-based feature extraction pipeline was proposed to quantify other variations in plant architecture traits such as plant height, leaf area index (LAI) and vegetation volume index (VVI). The stem diameter measured using the semiautomatic method showed high correlation (0.958) to hand measurement

Development of an Autonomous Indoor Phenotyping Robot

In order to fully understand the interaction between phenotype and genotype x environment to improve crop performance, a large amount of phenotypic data is needed. Studying plants of a given strain under multiple environments can greatly help to reveal their interactions. To collect the labor-intensive data required to perform experiments in this area, an indoor rover has been developed, which can accurately and autonomously move between and inside growth chambers. The system uses mecanum wheels, magnetic tape guidance, a Universal Robots UR 10 robot manipulator, and a Microsoft Kinect v2 3D sensor to position various sensors in this constrained environment. Integration of the motor controllers, robot arm, and a Microsoft Kinect (v2) 3D sensor was achieved in a customized C++ program. Detecting and segmenting plants in a multi-plant environment is a challenging task, which can be aided by integration of depth data into these algorithms. Image-processing functions were implemented to filter the depth image to minimize noise and remove undesired surfaces, reducing the memory requirement and allowing the plant to be reconstructed at a higher resolution in real-time. Three-dimensional meshes representing plants inside the chamber were reconstructed using the Kinect SDK’s KinectFusion. After transforming user-selected points in camera coordinates to robot-arm coordinates, the robot arm is used in conjunction with the rover to probe desired leaves, simulating the future use of sensors such as a fluorimeter and Raman spectrometer. This paper shows the system architecture and some preliminary results of the system, as tested using a life-sized growth chamber mock-up. A comparison of using raw camera coordinates data and using KinectFusion data is presented. The results suggest that the KinectFusion pose estimation is fairly accurate, only decreasing accuracy by a few millimeters at distances of roughly 0.8 meter

Within-row spacing sensing of maize plants using 3D computer vision

Within-row plant spacing plays an important role in uniform distribution of water and nutrients among plants which affects the final crop yield. While manual in-field measurements of within-row plant spacing is time and labour intensive, little work has been done on an alternative automated process. We have attempted to develop an automatic system making use of a state-of-the-art 3D vision sensor that accurately measures within-row maize plant spacing. Misidentification of plants caused by low hanging canopies and doubles were reduced by processing multiple consecutive images at a time and selecting the best inter-plant distance calculated. Based on several small scale experiments in real fields, our system has been proven to measure the within-row maize plant spacing with a mean and standard deviation error of 1.60 cm and 2.19 cm, and a root mean squared error of 2.54 cm, respectively

Design and Experiment of Differential Speed Snapping Rollers for Horizontal Roller Corn Harvester

This paper analyzed some conditions which cause grain breakage and grain loss during harvesting process of horizontal roller corn harvester. If the transmission rate between two snapping rollers can be changed, the grain breakage and grain loss can be solved at a certain level. So a pair of differential speed snapping rollers for horizontal roller corn harvester was designed, and the work effects between the constant speed snapping rollers and the differential speed snapping rollers were compared by using software CATIA and ADAMS, and found that the rolling speed of inner snapping roller is 500r/min, and the outer snapping roller is 460r/min are the best transmission rate. Finally, a physical experiment was conducted to certificate the simulation effect. In or-der to avoid some disadvantages of field experiment and soil bin experiment in laboratory, half laboratory and half field environment were used in this physical experiment. As a result, the physical experiment confirmed that the analyses above and the simulation by ADAMS were valid. In conclusion, it is an effective method to reduce grain breakage and grain loss by using differential speed snapping rollers for horizontal roller corn harvester

3D Perception-based Collision-Free Robotic Leaf Probing for Automated Indoor Plant Phenotyping

Various instrumentation devices for plant physiology study such as spectrometer, chlorophyll fluorimeter, and Raman spectroscopy sensor require accurate placement of their sensor probes toward the leaf surface to meet specific requirements of probe-to-target distance and orientation. In this work, a Kinect V2 sensor, a high-precision 2D laser profilometer, and a six-axis robotic manipulator were used to automate the leaf probing task. The relatively wide field of view and high resolution of Kinect V2 allowed rapid capture of the full 3D environment in front of the robot. The location and size of each plant were estimated by k-means clustering where “k” was the user-defined number of plants. A real-time collision-free motion planning framework based on Probabilistic Roadmaps was adapted to maneuver the robotic manipulator without colliding with the plants. Each plant was scanned from the top with the short-range profilometer to obtain high-precision 3D point cloud data. Potential leaf clusters were extracted by a 3D region growing segmentation scheme. Each leaf segment was further partitioned into small patches by a Voxel Cloud Connectivity Segmentation method. Only the patches with low root mean square errors of plane fitting were used to compute leaf probing poses of the robot. Experiments conducted inside a growth chamber mock-up showed that the developed robotic leaf probing system achieved an average motion planning time of 0.4 seconds with an average end-effector travel distance of 1.0 meter. To examine the probing accuracy, a square surface was scanned at different angles, and its centroid was probed perpendicularly. The average absolute probing errors of distance and angle were 1.5 mm and 0.84 degrees, respectively. These results demonstrate the utility of the proposed robotic leaf probing system for automated non-contact deployment of spectroscopic sensor probes for indoor plant phenotyping under controlled environmental conditions