J.W. Hofstee

Detection of diseased plants by analysis of volatile organic compound emission.

This review focuses on the detection of diseased plants by analysis of volatile organic compound (VOC) emissions. It includes an overview of studies that report on the impact of infectious and noninfectious diseases on these emissions and discusses the specificity of disease-induced emissions. The review also provides an overview of processes that affect the gas balance of plant volatiles, including their loss processes. These processes are considered as important because they contribute to the time-dynamic concentration profiles of plant-emitted volatiles. In addition, we describe the most popular techniques currently in use to measure volatiles emitted from plants, with emphasis on agricultural application. Dynamic sampling coupled with gas chromatography and followed by an appropriate detector is considered as the most appropriate method for application in agriculture. It is recommended to evaluate the state-of-the-art in the fields concerned with this method and to explore the development of a new instrument based on the specific needs for application in agricultural practice. However, to apply such an instrument in agriculture remains a challenge, mainly due to high costs.

Colour based detection of volunteer potatoes as weeds in sugar beet fields using machine vision

The possible spread of late blight from volunteer potato plants requires the removal of these plants from arable fields. Because of high labour, energy, and chemical demands, a method of automatic detection and removal is needed. The development and comparison of two colour-based machine vision algorithms for in-field volunteer potato plant detection in two sugar beet fields are discussed. Evaluation of the results showed that both methods gave closely matched results within fields, although large differences exist between the fields. At plant level, in one field up to 97% of the volunteer potato plants were correctly classified. In another field, only 49% of the volunteer plants were correctly identified. The differences between the fields were higher than the differences between the methods used for plant classification.

TESTING AN ONLINE SPREAD PATTERN DETERMINATION SENSOR ON A BROADCAST FERTILIZER SPREADER

An alternative method for fertilizer spread pattern determination was developed based on predicting where individual fertilizer particles land on the ground, in contrast to the traditional method of collecting the particles in bins (ASAE Standard S341.2). A small broadcast granular fertilizer spreader (Lowery 300) was equipped with an optical sensor designed to measure the velocity and diameter of individual fertilizer particles shortly after they leave the impeller disc. The measured velocity and diameter of individual particles were input into a ballistic model that predicted where particles land on the ground. A total of over 1000 landing spots revealed the spread pattern. The results have shown that the optical sensor is capable of automatically determining the spread pattern of a fertilizer spreader on the fly. The sensor could be a key component in the development of uniformity-controlled fertilizer application systems.

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?

AERODYNAMIC PROPERTIES OF INDIVIDUAL FERTILIZER PARTICLES

Predictability of granular fertilizer spreading patterns is of interest from the environmental as well as the economic point of view. To ensure a constant level of uniformity of spreading patterns in the field, the Dutch government has announced their intention to require periodic testing of spreader equipment. Testing of fertilizer spreaders is traditionally carried out in large halls where spread patterns are derived from measuring fertilizer mass in collecting bins. Hofstee (1994) has developed an alternative system which measures three-dimensional velocity vectors within a cylindrical sampling zone behind the spreader. It also simultaneously estimates individual particle diameters. These measured quantities serve as initial conditions in a trajectory model that predicts landing spots for individual particles. After a test run the complete set of landing spots represents a spread pattern. The trajectory model uses prediction equations based on the aerodynamic behavior of perfectly spherical particles. However, since fertilizer particles are in general not spherical, a method to compensate for this has been developed. This method uses the ratio between measured and modeled fall times, and is expressed in a parameter, the diameter coefficient. Once this parameter is assessed for a specific material, it can be used as a correction factor in the trajectory model. In this research a fall test is used as a robust and simple method for collecting data about the fall time of individual fertilizer particles, falling from a constant height. The materials used in this research were Calcium Ammonium Nitrate (CAN 27 N), Nitrate Phosphorous Potassium (NPK 12-10-18) and Potassium 60. They were chosen for their wide-spread use and different shape characteristics. The diameter range of particles used in the research was 1 to 4.75 mm.

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.

DETERMINING EFFECTS OF FERTILIZER PARTICLE SHAPE ON AERODYNAMIC PROPERTIES

A method was investigated for determining the extent to which aerodynamic properties of fertilizer particles can be explained by a combination of turbulent airflow theory and a response surface involving geometric shape and mass of particles for a sample of specific fertilizer material. Fall tests were conducted, where particles were dropped and fall times were described by a mathematical model using turbulent airflow theory. Secondly, a measure of particle shape was determined to explain the difference between theoretical and measured fall times. Various dimensions of particles were measured using digital image processing. Absolute radius deviations from a preassumed best-fit circular shape were recorded and combined from two perpendicular particle images and designated “shape factor”. For a sample of calcium ammonium nitrate (CAN) particles, the shape factor ranged from 11.8 to 73.0 (perfect spheres are zero). Over that range, the difference between theoretical and measured fall times was satisfactorily explained (R2 = 0.82 ) by a function of shape factor and particle mass. A new approach to characterize a bulk of fertilizer material and its spreading properties was proposed.

Field Applications of Automated Weed Control: Northwest Europe

In Northwest Europe there is high need for advanced weed control methods. The use of crop protection chemicals has become stricter, and integrated pest management is required by regulations from the European Union. This need has resulted in the development of several advanced weed control principles based on a combination of proven technologies in combination with decision systems. A major problem with full-field-based methods is that the required settings depend very much on the specific conditions. Use of decision systems helps to improve these methods. Emerging new technologies as machine vision and GPS enabled more precise methods focused on the interrow and intrarow zone and on the plant itself. Some of the methods have already achieved a high level of development and resulted in commercially available weed control equipment with sensors and actuators for precise control. This chapter discusses the advancements achieved in NW Europe on mechanical weed control (full field, interrow and intrarow), physical weed control (steaming and flaming) and chemical weed control (full field, spot and plant oriented).

Plant Volatiles: Useful Signals to Monitor Crop Health Status in Greenhouses

This chapter focuses on the monitoring of crop health status via the measurement of volatile organic compounds (VOCs) emitted from the plants. It includes the most important factors that affect the emission of these VOCs from crops grown in greenhouses. Since both stressors as well as nonstressors have an effect on the emission, they are covered separately. The chapter provides an overview of processes that affect the gas balance of plant VOCs in the greenhouse including the loss processes. These processes are considered as important since they contribute to the time-dynamic concentration profiles of plant-emitted VOCs. In addition, we describe the most popular techniques currently in use to measure volatiles emitted from plants, with emphasis on greenhouse application. Dynamic sampling in combination with gas chromatography coupled to mass spectrometry is considered as the most appropriate method for application at greenhouse scale. It is recommended to evaluate the state of the art in the fields concerned with this method and explore the development of a new instrument based on the specific needs for application in greenhouse practice. However, to apply such an instrument at greenhouse-scale remains a challenge, mainly due to the high costs associated with it.