INTEGRATING PLANT CHEMICAL ECOLOGY ...

In: Tomatoes: Agriculture Procedures, Pathogen… Editor: Eric D. Aube and Frederick H. Poole.

ISBN: 978-1-60876-869-1 © 2009 Nova Science Publishers, Inc.

Chapter 5

INTEGRATING PLANT CHEMICAL ECOLOGY, SENSORS AND ARTIFICIAL INTELLIGENCE FOR ACCURATE PEST MONITORING Saber Miresmailli 1 , Dan Badulescu 1 , Maryam Mahdaviani 2 , Ruben H. Zamar 3 and Murray B. Isman 1 1

  

Center for Plant Research, Faculty of Land and Food Systems, the University of British    Columbia, Vancouver, Canada 2 Department of Computer Science, Faculty of Science, the University of British   Columbia, Vancouver, Canada 3 Department of Statistics, Faculty of Science, the University of British Columbia, Vancouver, Canada

ABSTRACT New strategies and tools for pest management are needed especially for decisionmaking processes based on pest monitoring. We suggest a new approach to pest monitoring by using plant-driven information for assessing crop health in addition to detecting pests and damage symptoms. Herbivore-induced plant volatiles (HIPVs) can be considered as indicators of pest presence. The phenomenon is very well documented. We want to use this wealth of knowledge and put it into practice. New sensory technologies allow us to accurately measure these volatile chemicals. Environmental factors play a critical role on the emission of HIPVs. New pattern recognition and machine learning methods allow us to develop robust models that can account for all these factors.

Keywords: Integrated Pest Management, Pest Monitoring, Herbivore-Induced Plant Volatiles, Chemo-Sensors, Intelligent Systems, Pattern Recognition

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Saber Miresmailli, Dan Badulescu, Maryam Mahdaviani, et al.

INTRODUCTION With the exclusion of serendipitous and accidental discoveries, science is the collective work of numerous researchers and scientists who generate small bits of information and put them together to improve our understanding of certain issues. Epiphanies are very rare in science. Everything must follow a very well established set of scientific rules and reasoning. We must ask appropriate questions, develop hypotheses based on those questions and prior knowledge, design experiments and choose proper statistical methods that enable us to test those hypotheses. Although this seems like a solid path that provides us with some degree of certainty, one should not forget that scientists are limited to their tools and instruments. Our knowledge goes as far as our instruments can take us. New tools and instruments can fundamentally change our understanding of certain issues. Microscopes, telescopes, fine analytical tools and tracing and tracking devices are a few examples of instruments that changed our way of seeing the world and understanding how it works. On the other hand knowledge snowballing is frequent; there have been many occasions where a scientist developed a tool or a theory or introduced a concept for a specific situation or reason and other scientists realized over time that they could use this information or tool for purposes other than that originally intended. When President Eisenhower created the Advanced Research Projects Agency (ARPA) in 1958 in response to the launching of the Soviet Union’s Sputnik , the main goal was to promote research that would ensure that the USA would never again be beaten in any technological race. One of the ARPA’s offices was the Information Processing Technologies Office (IPTO) in which the first ideas of a Galactic Network were developed by J. C. R. Licklider of the Massachusetts Institute of Technologies (MIT) in 1962. That was the origin of what we know today as the Internet and it was meant to create a man-computer symbiosis [1]. Another example is GPS (Global Positioning Systems) that was also developed as a military project for similar reasons [2]. Now we carry cell phones in our pocket that provide us both Internet access and GPS functionality.

Figure 1. Accumulation of knowledge and collective work of scientists

Integrating Plant Chemical Ecology, Sensors and Artificial Intelligence…

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In medical sciences, we have Viagra that was initially developed as a treatment for high blood pressure and angina pectoris but now is used as a cure for erectile dysfunction [3]. Botox was first developed to treat crossed eyes and uncontrollable blinking but now is widely used for cosmetic surgeries [4]. Interdisciplinary projects are now quite common in many universities and research centers. We see scientists, researchers and experts from completely different disciplines working together to solve a mystery or develop a new tool. A bizarre example of such collaboration happened in Britain's largest children's hospital, the Great Ormond Street Hospital. They changed their patient handoff techniques by copying the choreographed pit stops of Italy's Ferrari Formula One racing team [5]. The concept in this chapter falls into the same category. Here we suggest an integration of chemical ecology, sensory systems, artificial intelligence and integrated pest management (IPM) for developing a new tool for pest monitoring in tomato greenhouses.

GREENHOUSE TOMATO PRODUCTION The greenhouse vegetable industry is an important and growing segment of Canadian agriculture. Canada has a short summer, which limits the quantity of field vegetables grown. Through greenhouse production, Canadian greenhouse tomatoes are available from March to December with peak production in May. There is a move toward trying to provide a year round supply, however, the economics of producing a crop when light levels and temperatures are at their lowest will increase costs and limit supplies from December to February. Almost all greenhouse vegetable production uses some form of hydroponics. The most common systems use rock-wool slabs as the growing medium. Computerized production facilities and new varieties have increased the diversity of products, improved quality and improved efficiencies. Most greenhouses in Ontario and British Columbia, the two leading provinces, are heated with natural gas, usually purchased through producerowned cooperatives. In warmer climates cooling becomes a significant cost. Greenhouse crops, because of the controlled and enclosed environment, are much less susceptible to outbreaks of diseases and insects. The estimated value of the greenhouse vegetable industry was $80 M in 1988, reaching $600 M in 2000. According to Statistics Canada (2006), the main greenhouse vegetable crops in Canada are tomatoes (468 ha), cucumbers (190 ha), sweet peppers (144 ha) and lettuce (21 ha) [6]. During the 1990s, the total area under glass and plastic more than doubled to nearly 1500 hectares and by 2003, it had reached nearly 1900 hectares. In 2003, revenues from greenhouse sales reached a record high of almost $2.1 billion, nearly double that of six years earlier. Flowers accounted for about 70% of sales and vegetables the remaining 30%. In the early 1990s, revenues from the comparable greenhouse and field vegetables were roughly the same. However, since 1996, revenues from greenhouse vegetables have increased at a much more rapid pace than field vegetables. For example, in 2003, the farm gate value of the four main vegetable crops produced under glass or plastic (tomatoes, cucumbers, lettuce and peppers) amounted to $605.8 million. This was more than three times higher than the value of $171.7 million for the same four vegetable crops produced in the field. Farmers grow more tomatoes than any other vegetable crop, whether it’s in the greenhouse or in the field.

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Tomatoes alone, account for over one-half of revenues from the sale of greenhouse vegetables. According to Statistics Canada, the main greenhouse vegetable producing provinces in 2007 were Ontario with farm cash receipts (FCR) of $418.2 million and British Columbia with FCR of $ 201.6 million. The 2007 export value of the three major greenhouse crops (tomatoes, cucumbers and peppers) to the U.S. was down by 11 percent from the previous year to $510.7 million. This is mostly due to the higher value of the Canadian dollar versus the U.S currency. Tomato exports showed a 21% decrease in volume in 2007, due to a significant increase in Mexico’s exports to the U.S. at reportedly lower prices. The estimated greenhouse tomato area in Mexico in 2008 was 850 hectares which was substantially more than what it was in 2003 (182 ha) while in Canada it went down from 444 hectares in 2003 to 432 hectares in 2008 [7]. Due to the current global economic crisis and its significant effects on the greenhouse tomato market, it would make more sense to concentrate on protecting our crops and reducing the crop loss caused by pests and diseases.

PEST MONITORING AND IPM For centuries, humans have been battling to protect crops from pests and diseases. Despite recent progress in crop protection tools and techniques, we still experience 26% to 40% crop losses in production and storage [8]. Before the advent of synthetic pesticides, most pest control depended on ecological knowledge of the pest and how its host environment could be altered to reduce damage [9]. After the Second World War, pest management became synonymous with chemical pesticides worldwide. The publication of Silent Spring by Rachel Carson in 1962 raised awareness of the potentially deleterious effects of pesticides on the environment and health, launching a global environmental movement. Since then, integrated pest management or IPM, has become the alternative paradigm for crop protection. IPM stresses the interaction of multiple tools aimed at maintaining pest populations below the level where economic damage occurs. Pest management could benefit from a model based on systematic and automated tools and incorporating newer technologies for decision-making. Integrated pest management can be considered as an important part of a broader concept of integrated plant management that looks into the effect of the major elements of an agroecosystem on the crop[10]. These elements include climate, nutrition, soil, water and finally pests and diseases. Over the past few decades, several instruments have been developed for measuring different agro-ecosystem elements and provide information that enable us to develop decision-making models. As Alan Thomson described in his paper on indicator-based knowledge management, the three major components of participatory decision-making systems are knowledge, communication and reporting [11]. We now have access to sophisticated tools that can measure the majority of environmental factors including temperature, relative humidity, air flow and light intensity. There are instruments that can measure the level of major nutrients in soil or assess the level of photosynthesis and gas exchange in plants. We have a variety of quality control systems for water and soil quality so we can easily obtain a lot of information about the cropping system [12].


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Figure 2. (A) Estimated value of the greenhouse vegetable industry in Canada, (B) main greenhouse vegetable crops in Canada in 2003, (C) farm gate value of the four main vegetable crops produced in 2003 in field versus greenhouse, (D) estimated greenhouse tomato area in Mexico versus Canada. Data from Statistics Canada and Agriculture and Agri-food Canada (6,7).

An attractive feature of these instruments is that they can communicate with each other and provide real-time reports. We have wireless weather stations and crop monitors that can provide detailed reports on the changes in our cropping systems [13]. Many of these instruments have been widely used in greenhouses for more than two decades [14]. Monitoring pest populations is a cornerstone of the IPM philosophy but it has lagged in comparison to the development of other monitoring tools. Current pest monitoring methods and decision-making are built on a common model: detecting pests and/or signs of their presence or damage [15]. Even modern technologies are following the same path and base their monitoring on pest-related indicators [16-20]. The majority of pest monitoring techniques enable the grower to estimate pest populations or predict pest outbreaks; however, we still rely on human scouts to establish the precise location of pests and severity of damage within a field or a greenhouse. Scouts are faced with the challenge of visually scanning a large representative number of plants, plant organs and a variety of pests. Fields and greenhouses pose problems due to limitations of human vision; some pests might be overlooked in their early stages of development. Besides, scouts need extensive training and their performance is never the same due to different levels of experience, individual values and other considerations; thus making consistent decisions a challenge [21]. In this chapter, we suggest creating a new tool that can increase the efficiency and performance of human scouts based on an alternative source of information. For pest monitoring, one should not just look for the pests but also detect the signals generated by plants, as they may be more informative.

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Figure 3. Some examples of field measurement technologies. (A) Watchdog weather station model 2900ET (B) Waterproof EC meter (C) Cardy Twin pH meter (D) Cardy Sodium meter (E) Watchdog wireless crop monitor 3540 (F) AELDScout CM 1000 Chlorophyll meter (G) Watchdog 2000 weather tracker. Spectrum Technologies Inc. Plainfield, Illinois. Pictures obtained from the company website: www. Specmeters.com

PLANTS AS AN ALTERNATIVE SOURCE OF INFORMATION Plants cannot talk and they cannot walk but they can communicate through several types of signals and responses. They can provide us with useful information about their health. Some even believe that we should put plants in charge of their own wellbeing and let them control the optimum conditions for their development and growth [22]. Plants can defend themselves against threats both directly and indirectly and can actively manipulate their environment [23]. Despite several controversial interpretations of plant-generated signals and responses – whether they are intelligent or reflexive- or their evolutionary raison d'être [24, 25], it is an accepted fact that most plants are capable of responding to changes in their surroundings and can convey precise information about their overall health status through those responses [26]. There is a large body of evidence that supports these claims. For example, some plants are capable of showing the footsteps of insects crawling on their foliage [27], while some other


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plants react to pest oviposition [28, 29] or feeding [23, 30, 31]. Plants show various types of induced responses to organisms that range from viruses, bacteria, fungi, nematodes, mites, insects and mammals as part of their defense mechanisms [32]. Some plants also show the ability to alarm neighboring plants via their volatile emissions [33-35]. The ability of plants to sense external stimuli and translate them into signals that are transmitted to distant, non-stimulated organs has been known for a long time [36]. One of the well-documented responses of plants to biotic stressors is the emission of herbivore induced plant volatiles (HIPVs) –also known as info-chemicals due to the fact that they carry some information about the status of the plant [37]. HIPVs can strongly affect the behavior of both predatory and herbivorous arthropods in nature and some plants are under strong selection pressure to release these volatiles [31]. HIPVs are known to be emitted by various parts of plants including leaves [38-40] from both the abaxial and the adaxial side [41], flower buds [42] and roots [43]. HIPV emission is not limited to the site of damage but also occurs systematically throughout the plant even in undamaged parts [44-47]. Recent findings show that plants can recognize the herbivores and assess their threat via a series of chemical and electrical reactions that occur before activation of defensive mechanisms [48]. Considering the vast knowledge of plant behavior and their responses to the environment, it is conceivable to use these plant-generated signals as indicators of herbivore presence in pest monitoring programs in addition to previously used indicators. The questions are: how reliable are these signals and how quickly can they convey information? When plants emit info-chemicals, they have no authority over the receiver of these signals [49, 50] and although they can be found in higher concentrations closer to the emitter [51], it is difficult to relate these volatiles that float in the air to their actual source. Environmental factors such as light intensity, temperature and moisture can profoundly affect the emission of plant volatiles [52]. However, in spite of the complexity of this system, predators can “learn” to associate these chemical signals in addition to other cues to locate their prey[39]. Some of these volatile chemicals are emitted within minutes after tissue damage and can be considered a quick indicator of problems [53], while other chemicals are released later as a complement to other types of defense [54]. A pest monitoring system that is capable of harvesting information from the environment through a series of sensory systems can also learn to associate different signal patterns with pest presence and perform the same task. This would be considered an intelligent pest monitoring system.

ANAZLYZING PLANT VOLATILES The literature on HIPVs is vast and continuously growing. In most cases, the researcher does not know the biological activity of the compounds assessed and therefore samples and analyzes a full range of HIPVs. Usually the volatile organic compounds in the headspace of plants that are enclosed in collection chambers are collected using an adsorbing material. Subsequently the collected volatiles are analyzed by gas chromatography (GC) and massspectrometry (MS) or a combination of both (GC/MS) [55, 56]. D’Alessandro and Turlings (2006) looked into the most commonly used HIPV collection methods from 1995 to 2004. They found that adsorbent/solvent desorption was the most popular method among the scientists who studied insect-plant interactions [57]. There are different approaches to volatile

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analysis depending on the physiological state of the plant, whether the plant tissue is intact or if it has been detached from the plant. Choosing the method of analysis that will give the most accurate results takes some careful consideration. Inconsistencies and inherent methodological weaknesses will be unavoidable to some degree, but the investigator must examine certain parameters and try to minimize potential errors. First, the choice between using detached plant material or intact plants must be made. Data from [57] .For plant-herbivore interaction studies, using intact plants is generally considered to be most rigorous and accurate. Experiments that use detached plant parts often show more fluctuations in volatile profile than intact plants and are often verified against intact plant results to compensate for oscillations caused by the mechanical damage from detachment [58]. For studies in chemical ecology, using intact plants is considered the most reliable method but additional considerations must be made to minimize other factors that can affect the accuracy of the results. Some parameters to consider are light intensity, relative humidity (RH), air temperature, and photoperiod [52, 58]. The investigator must try to standardize environmental conditions over the duration of the analysis so as to eliminate as much environmental influence as possible. Because elements such as RH and temperature can induce changes in volatile emission it can be difficult to ascertain if the volatiles are a result of insect damage or of abiotic factors. Recent improvements in analytical tools enable researchers to collect more accurate data about HIPVs and plant systems within their growing environment in short periods of time [59-63], which is a key element for developing an intelligent monitoring system.

SENSORY SYSTEMS A good indicator of pest or disease presence should be (a) plant-generated, (b) caused directly or indirectly by the stressor and (c) measurable. In order to create an intelligent system, one needs to identify plausible indicators, select suitable corresponding sensors and finally develop computational techniques that can interpret the output of sensory systems. Environmental factors like temperature, moisture, light intensity and airflow can also be correlated to the fluctuations of the main indicators to create a robust decision making system.


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Figure 4. The most commonly used HIPV collection methods during the ten year period from 19952004.

A whole array of sensory systems has been developed and improved in the past decade ranging from chemical sensors [64-66] to biological hybrids [67-70], physiological sensors [71] and visual sensors [72-74]. Despite great technological advances in recent years, there are still a number of challenges in developing these sensory systems for outdoor use [75, 76]. There are a few successful cases [66, 77, 78] that have shown some promising results, but there is a great need for further improvement of these sensory technologies. Considering the fast pace of technological advances in this new century, it is not too ambitious to suggest that within a few machine generations, we might have sensors that are accurate enough to be incorporated into an intelligent pest monitoring system.

STATISTICAL AND COMPUTATIONAL METHODS To recognize patterns in the sensory data and predict whether a plant or a particular area is infested, a computational model needs to be developed. Pest-plant-environment systems are very complex; therefore we need a mechanism that learns cause and effect dependencies as well as the underlying noise in the environment (i.e. variability in temperature, RH%, light intensity, other sources of volatile chemicals). A model could be created by incorporating prior knowledge as well as on-site collected training data sets. After training the computational model with large amounts of sensory data, this system should be capable of decision-making based on the model and new observations. In recent years, certain statistical and computational methods have been applied to overcome challenges in modeling biological systems. In particular, machine learning and pattern recognition methods have been extensively used in various application domains such as medical diagnosis, bio-informatics and chemo-informatics [79, 80]. Several statistical and machine learning methods can be adapted for intelligent pest monitoring. These methods range from simple linear discriminant analysis [81-83] to principal component analysis [78, 84-86] , neural networks [87-90], support vector machines [91], logistic regression [92] and more complicated techniques such as bayesian networks [93-95]. From the statistical point of view, pest-related sensory data presents problems similar to other sensor-based applications, although their domain-specific challenges are different. In this chapter we focus on Bayesian networks as an example. Also known as belief networks, Bayesian networks are graphical structures used to represent knowledge in uncertain domains. They incorporate a probabilistic model of all dependencies. In these networks, the goal is to learn the network parameters and the topology of the model using a set of training samples. These training instances consist of the sensory data and the true diagnosis. Figure 5 shows an example of a Bayesian network for a pest management system. In reality, we would encode as many variables as possible to include all factors in decision making. The more varied and realistic the training samples are, the more accurate the probabilistic model becomes. Therefore, training probabilistic models is often considered time-consuming and expensive. To alleviate the cost, active and adaptive learning techniques are proposed for efficient training of probabilistic models [96]. After initial off-site learning, these techniques enable the system to become adaptable to new environments without extensive on-site

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training. With a fixed initial cost, sensors can collect a large number of samples from different locations. Only in cases where the system is indecisive about the condition of the plant, will the human expert be asked to examine the plant. By acquiring more information when the system has low confidence, the system becomes tuned to the new environment and soon fully automated and independent of human expertise. When probabilistic models are trained by active-learning methods, observations are analyzed and most informative samples can be selected for diagnosis. By these means, not only is the pest monitoring system efficiently trained, but it also becomes adaptive to new environments. Once the computational model is trained for pest monitoring, diagnosis becomes automated, as the collected sensory data is processed and fed to the model. The probabilistic network computes the probability of having an infested plant given the evidence. If the confidence is low the system requires more thorough sampling and expert feedback is needed to improve the model.

Figure 5. An example of a Bayesian network, modeling some of the causal relationships of in a pest monitoring system based on plant volatiles.


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CONCLUSION Pest management programs in tomato and other valuable vegetable greenhouses will benefit from an interdisciplinary boost provided by developing a precise method for early detection of pests and locating them in the field, an important prerequisite for full implementation of IPM recommendations. In this chapter we have suggested a new approach towards crop protection and pest monitoring by adding the idea of “monitoring plant-driven signals “ to the “pest and damage detection” mindset. In the past few decades, our knowledge of plant chemical ecology, artificial intelligence and sensory systems has increased considerably. We believe now is the time for us to use this wealth of knowledge and put it into practice. Like any novel concept, there are numerous complexities and challenges for this proposed approach, which requires the close attention of the scientific community and the active collaboration of different disciplines. We might not immediately be able to precisely pinpoint the location of pests within a field or greenhouse by using these tools but we will be able to locate areas in the field with higher probability of pest presence. Therefore, we can use our current resources more effectively. In our own research we have identified unique HIPVs in tomato in response to specific pests and demonstrated the proof of concept in a commercial tomato greenhouse by detecting these volatiles and relating them to real pest infestations. We hope that this brief introduction translates into several research projects in different disciplines to improve crop protection practices.

Figure 6. Comparing conventional pest monitoring with a proposed intelligent pest monitoring system: in addition to individual differences among pest scouts , they also have limited resources that reduce their productivity and prevent them from providing consistent and precise results. Even those who develop expertise over years of practice are unable to pass their full practical knowledge to other scouts. On the other hand, with intelligent pest monitoring, we are not limited to human senses and can incorporate exotic sensory systems and consider a wide range of variables in the decision making process. Feedback from human experts will always remain part of the system and will improve the system’s precision and performance.

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ACKNOWWLEDGMENT This work has been sponsored by grants and scholarships from the Natural Sciences and Engineering Research Council of Canada (NSERC), the British Columbia Greenhouse Growers Association (BCGGA) and the British Columbia Innovation Council (BCIC).

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