Eldert J. van Henten

Harvesting Robots for High-value Crops: State-of-the-art Review and Challenges Ahead

This review article analyzes state-of-the-art and future perspectives for harvesting robots in high-value crops. The objectives were to characterize the crop environment relevant for robotic harvesting, to perform a literature review on the state-of-the-art of harvesting robots using quantitative measures, and to reflect on the crop environment and literature review to formulate challenges and directions for future research and development. Harvesting robots were reviewed regarding the crop harvested in a production environment, performance indicators, design process techniques used, hardware design decisions, and algorithm characteristics. On average, localization success was 85%, detachment success was 75%, harvest success was 66%, fruit damage was 5%, peduncle damage was 45%, and cycle time was 33 s. A kiwi harvesting robot achieved the shortest cycle time of 1 s. Moreover, the performance of harvesting robots did not improve in the past three decades, and none of these 50 robots was commercialized. Four future challenges with R&D directions were identified to realize a positive trend in performance and to successfully implement harvesting robots in practice: 1 simplifying the task, 2 enhancing the robot, 3 defining requirements and measuring performance, and 4 considering additional requirements for successful implementation. This review article may provide new directions for future automation projects in high-value crops.

Sensing Soil Properties in the Laboratory, In Situ, and On-Line

Abstract Since both the spatial and vertical heterogeneities in soil properties have an impact on crop growth and yield, accurate characterization of soil properties at high sampling resolution is a preliminary step in successful management of soil-water-plant system. Conventional soil sampling and analyses have shown mixed economical returns due to the high costs associated with labor-intensive sampling and analysis procedures, which might be accompanied with map uncertainties. Therefore, the conventional laboratory methods are being replaced or complemented with the analytical soil sensing techniques. The objective of this chapter is to review different soil sensing methods used to characterize key soil properties for management of soil-water-plant system. This will cover laboratory, in situ in the field, and on-line measurement methods. This review chapter is furnished with an overview of background information about a sensing concept, basic principle and brief theory, various factors affecting the output of the sensor, and justification of why specific soil properties can be related with its output. The literature review is succeeded with an integration and analysis of findings in view of application in the precision agriculture domain. Potentials and limitations of current sensor technologies are discussed and compared with commonly used state-of-the-art laboratory techniques. As sensing is commonly addressed as a very technical discipline, the match between the information currently collected with sensors and those required for site-specific application of different inputs, and crop growth and development is discussed, highlighting the most accurate method to measure a soil property for a given application.

Robotics in agriculture and forestry

Robotics for agriculture and forestry (A&F ) represents the ultimate application of one of our society’s latest and most advanced innovations to its most ancient and important industries. Over the course of history, mechanization and automation increased crop output several orders of magnitude, enabling a geometric growth in population and an increase in quality of life across the globe. Rapid population growth and rising incomes in developing countries, however, require ever larger amounts of A&F output. This chapter addresses robotics for A&F in the form of case studies where robotics is being successfully applied to solve well-identified problems. With respect to plant crops, the focus is on the in-field or in-farm tasks necessary to guarantee a quality crop and, generally speaking, end at harvest time. In the livestock domain, the focus is on breeding and nurturing, exploiting, harvesting, and slaughtering and processing. The chapter is organized in four main sections. The first one explains the scope, in particular, what aspects of robotics for A&F are dealt with in the chapter. The second one discusses the challenges and opportunities associated with the application of robotics to A&F. The third section is the core of the chapter, presenting twenty case studies that showcase (mostly) mature applications of robotics in various agricultural and forestry domains. The case studies are not meant to be comprehensive but instead to give the reader a general overview of how robotics has been applied to A&F in the last 10 years. The fourth section concludes the chapter with a discussion on specific improvements to current technology and paths to commercialization.

Induced plant volatiles allow sensitive monitoring of plant health status in greenhouses.

This paper provides a synthesis of our research on the use of induced plant volatiles for sensitive monitoring of plant health status in greenhouses. The main research objective of this research was to investigate whether plant-emitted volatiles can be used to detect a Botrytis cinerea infection in a large-scale greenhouse. The pathogenic fungus B. cinerea and the plant species tomato (Lycopersicon esculentum) were selected as model organisms. Based on this choice, three main research questions were formulated: (1) What is the effect of a B. cinerea infection on the emission of volatiles from tomato? (2) Are B. cinerea induced emissions of tomato specific for the infection with this pathogen? (3) Are B. cinerea induced concentrations of volatiles detectable in large-scale greenhouses?

Optimal control of greenhouse cultivation.

Features Discusses economic optimization of greenhouse control through mathematical modeling Examines 30 years of scientific research to present a unified framework for efficient decision-making Presents modern methods of control and optimization including classical rule-based and multivariable feedback controllers Utilizes real and experimental examples and novel case discussions such as solar greenhouses Concludes with a discussion of open issues to stimulate new areas of research and development Summary Greenhouse control system manufacturers produce equipment and software with hundreds of settings and, while they hold training courses on how to adjust these settings, there is as yet no integrated instruction on when or why. Despite rapid growth in the greenhouse industry, growers are still faced with a multitude of variables and no unifying framework from which to choose the best option. Consolidating 30 years of research in greenhouse climate control, Optimal Control of Greenhouse Cultivation utilizes mathmatical models to incorporate the wealth of scientific knowledge into a feasible optimal control methodology for greenhouse crop cultivation. Discussing several different paradigms on greenhouse climate control, it integrates the current research into physical modeling of the greenhouse climate in response to heating, ventilation, and other control variables with the biological modeling of variables such as plant evapo-transpiration and growth. Key topics include state-space greenhouse and crop modeling needed for the design of integrated optimal controllers that exploit rather than mitigate outside weather conditions, especially sunlight, given widely different time scales. The book reviews classical rule-based and multivariable feedback controllers in comparison with the optimal hierarchical control paradigm. It considers real and hypothetical examples including lettuce, tomato, and solar greenhouses and examines experimental results of greenhouse climate control using optimal control software. The book concludes with a discussion of open issues as well as future perspectives and challenges. Providing a tool to automatically determine the most economical controls and settings for their operation, this much-needed book relieves growers of unnecessary control tasks, and allows them to achieve the best possible trade-off between short term savings and optimal harvest yield.

Fruit Detectability Analysis for Different Camera Positions in Sweet-Pepper †

For robotic harvesting of sweet-pepper fruits in greenhouses a sensor system is required to detect and localize the fruits on the plants. Due to the complex structure of the plant, most fruits are (partially) occluded when an image is taken from one viewpoint only. In this research the effect of multiple camera positions and viewing angles on fruit visibility and detectability was investigated. A recording device was built which allowed to place the camera under different azimuth and zenith angles and to move the camera horizontally along the crop row. Fourteen camera positions were chosen and the fruit visibility in the recorded images was manually determined for each position. For images taken from one position only with the criterion of maximum 50% occlusion per fruit, the fruit detectability (FD) was in no case higher than 69%. The best single positions were the front views and looking with a zenith angle of 60° upwards. The FD increased when a combination was made of multiple viewpoint positions. With a combination of five favourite positions the maximum FD was 90%.

Automated signal processing applied to volatile-based inspection of greenhouse crops.

Gas chromatograph–mass spectrometers (GC-MS) have been used and shown utility for volatile-based inspection of greenhouse crops. However, a widely recognized difficulty associated with GC-MS application is the large and complex data generated by this instrument. As a consequence, experienced analysts are often required to process this data in order to determine the concentrations of the volatile organic compounds (VOCs) of interest. Manual processing is time-consuming, labour intensive and may be subject to errors due to fatigue. The objective of this study was to assess whether or not GC-MS data can also be automatically processed in order to determine the concentrations of crop health associated VOCs in a greenhouse. An experimental dataset that consisted of twelve data files was processed both manually and automatically to address this question. Manual processing was based on simple peak integration while the automatic processing relied on the algorithms implemented in the MetAlign™ software package. The results of automatic processing of the experimental dataset resulted in concentrations similar to that after manual processing. These results demonstrate that GC-MS data can be automatically processed in order to accurately determine the concentrations of crop health associated VOCs in a greenhouse. When processing GC-MS data automatically, noise reduction, alignment, baseline correction and normalisation are required.

Proximal Gamma-Ray Spectroscopy to Predict Soil Properties Using Windows and Full-Spectrum Analysis Methods

Fine-scale spatial information on soil properties is needed to successfully implement precision agriculture. Proximal gamma-ray spectroscopy has recently emerged as a promising tool to collect fine-scale soil information. The objective of this study was to evaluate a proximal gamma-ray spectrometer to predict several soil properties using energy-windows and full-spectrum analysis methods in two differently managed sandy loam fields: conventional and organic. In the conventional field, both methods predicted clay, pH and total nitrogen with a good accuracy (R2 ≥ 0.56) in the top 0–15 cm soil depth, whereas in the organic field, only clay content was predicted with such accuracy. The highest prediction accuracy was found for total nitrogen (R2 = 0.75) in the conventional field in the energy-windows method. Predictions were better in the top 0–15 cm soil depths than in the 15–30 cm soil depths for individual and combined fields. This implies that gamma-ray spectroscopy can generally benefit soil characterisation for annual crops where the condition of the seedbed is important. Small differences in soil structure (conventional vs. organic) cannot be determined. As for the methodology, we conclude that the energy-windows method can establish relations between radionuclide data and soil properties as accurate as the full-spectrum analysis method.

IEEE Robotics and Automation Society Technical Committee on Agricultural Robotics and Automation [TC Spotlight]

The IEEE Robotics and Automation Society Technical Committee (TC) on Agricultural Robotics and Automation was launched in 2012 with the goal of bringing together researchers and practitioners, academic and industrial, in an informal setting to increase knowledge dissemination in the field. The goal for 2013 is to hold at least eight Webinars and for 2014 to hold one every month. The TC members are rewriting the Handbook of Robotics chapter on Agricultural and Forestry Robotics and wrote an invited chapter on Agricultural Robotics on an SAE book on autonomous vehicles. Beyond endowing machines and vehicles with higher levels of intelligence, two long-term challenges, such as humanlike manipulation of crops and harvesting robots, must be addressed before R&A makes a full incursion into agriculture. Membership in the TC is open to all interested in contributing to the exciting field of agricultural robotics and automation.

Performance Evaluation of a Harvesting Robot for Sweet Pepper

This paper evaluates a robot developed for autonomous harvesting of sweet peppers in a commercial greenhouse. Objectives were to assess robot performance under unmodified and simplified crop conditions, using two types of end effectors (Fin Ray; Lip type), and to evaluate the performance contribution of stem-dependent determination of the grasp pose. We describe and discuss the performance of hardware and software components developed for fruit harvesting in a complex environment that includes lighting variation, occlusions, and densely spaced obstacles. After simplifying the crop, harvest success significantly improved from 6% to 26% (Fin Ray) and from 2% to 33% (Lip type). We observed a decrease in stem damage and an increase in grasp success after enabling stem-dependent determination of the grasp pose. Generally, the robot had difficulty in successfully picking sweet peppers and we discuss possible causes. The robots novel capability of perceiving the stem of a plant may serve as useful functionality for future robots.