- Email: [email protected]
Expert system for pests, diseases and weeds identification in olive crops
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Expert Systems with Applications Expert Systems with Applications 36 (2009) 3278–3283 www.elsevier.com/locate/eswa
Expert system for pests, diseases and weeds identification in olive crops J.L. Gonzalez-Andujar * Instituto de Agricultura Sostenible-CSIC, Alameda del Obispo, Apdo. 4084, 14080 Cordoba, Spain
Abstract An expert system was developed with the aim of improving decision-making by olive oil growers. Knowledge was obtained from the literature and from experts. The knowledge was then represented in the knowledge base of the expert system in a series of IF–THEN rules. The system is supported by a data base containing information for the identification of 9 weeds, 14 insects and 14 diseases. The system is enhanced with 150 photos and drawings that assist the used in the identification process. The expert system was evaluated following the conventional expert system evaluation methodologies. According to the validation results the system was considered very satisfactory. The program can be used as an identification tool for farmers and technicians and for educational purposes. Ó 2008 Elsevier Ltd. All rights reserved. Keywords: Production rules; Olive oil; Knowledge base; Evaluation; Validation; Decision support system
1. Introduction Although the olive tree originated in Asia, it has been cultivated for over 3000 years in Mediterranean countries, where much of the olive crop is used to make olive oil. The olive is an important crop in terms of both its commercial value and the role it plays in the rural economy of the Mediterranean region, with its millions of producers. Spain is the world’s leading producer of quality olive oil; it is estimated that there are over 215 million olive trees in Spain, covering over 2.300,000 ha. This amounts to over 27% of the world’s olive production extension. Spain has an average annual production of over 800.000 tons of olive oil. Olive crop production is hampered by pests, weeds and diseases (thereafter harmful organisms) which reduce production and quality of olive oil. Although harmful organisms management information is available from different sources, theirs identification is, in many cases, difficult and often requires consultation with a specialist. Expert systems have been developed for many kinds of applications in agriculture, involving diagnosis, predictions, *
Fax: +34 957499252. E-mail address: [email protected]
0957-4174/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2008.01.007
consultation, control, etc. (Carrascal & Pau, 1992; EdwardJones, 1992; Gonzalez-Andujar, Fernandez-Quintanilla, Izquierdo, & Urbano, 2006; Gonzalez-Andujar & Recio, 1996; Kaloudis, Anastopoulos, Yialouris, Lorentzos, & Sideridis, 2005; Mahaman, Passam, Sideridis, & Yialouris 2003). Only a few expert systems have been reported for olive production, mainly for olive oil quality. SEXIA has been developed for the authentication of extra virgin olive oils from different regions of Spain, Italy and Portugal, by means of their fatty acids, alcohols, sterols, methyl sterols and hydrocarbons content (Aparicio & Alonso, 1994). However, none expert system have been developed for the identification of harmful organism in olive crops. A System Expert is proposed in this paper to provide farmers and technicians with information for an early identification of harmful organisms commonly found in olive crops in Spain. Moreover, the system can be useful for training as well as for educational purposes. 2. Methods 2.1. The knowledge base A critical aspect of building an expert system is formulating the scope of the problem and gleaning from the
J.L. Gonzalez-Andujar / Expert Systems with Applications 36 (2009) 3278–3283
source expert the domain information needed to solve the problem. The reliability of an Expert System depends on the quality of knowledge contained in the knowledge base (Plant & Stone, 1991). In the present work, knowledge has been obtained from two sources. We acquired textual information from literature such as extension booklets, reports, papers, etc. The printed material allowed familiarization with the subject and a more effective communication with the experts. Most knowledge was acquired from the experts using conventional interviewing techniques (Scott, Clayton, & Gibson, 1991). The interview methods allowed us to obtain the heuristic knowledge that was not present in the printed material. This knowledge was provided by three experts on crop protection (one plant pathologists, one entomologist and one weed expert). Unstructured and structured interviews were used. The unstructured interviews were used to define the familiar tasks involved in the process of identification, to obtain an initial understanding of the range of complications involved, and to define specific problems (e.g., similarity between some species, number of species to be considered, etc.) for later discussion. The questions were more or less spontaneous and notes were taken on discussion. These methods were complemented with structured interviews. In the structured interviews, we revised and discussed in depth familiar tasks to clarify questions. 2.2. Knowledge representation Amongst the different methods for representing the knowledge production rules are the most frequently used for diagnostic expert systems (Ellison, Ash, & McDonald, 1998; Gonzalez-Andu´jar, Garcia de Ceca, & Fereres, 1993; Mahaman et al., 2002; Plant & Stone, 1991). A rule is composed of a list of IF conditions and a list of THEN and ELSE statements about the appropriate solution to the problem. Rules IF/THEN were used in developing the identification system. The knowledge base contains information for the identification of 9 weed species, 14 insect species and 14 diseases (Table 1) and 150 digital photos and drawings. All of these species are frequently found in olive crops in Spain (De Andres F., 2001). 3. Interface One of the most important design considerations behind the system was that the resulting system should be as userfriendly as possible. The system was divided in three subsystems, namely: insects, diseases and weeds. Each subsystem has its own database. This modularity ensures that only some of the rules are active at any one time. The user operates the system through screens of a graphical user interface. At the beginning of the each diagnosis session the user is prompted to select the corresponding subsystem in the start-up screen (Fig. 1). In the insect identification subsys-
3279
Table 1 Weeds, pests and diseases included in the expert system Weeds Cynodon dactylon Lolium rigidum Allium spp. Conium macalatum Amaranthus spp. Malva parviflora Conyza ssp. Sinapsis arvensis Ecballium elaterium Pests Bractrocera oleae Prays oleae Saissetia oleae Liothrips oleae Coenorrhinus cribripennis Euphyllura olivita Phloeotribus scarabaeoides Hylesinus oleiperda Parlatoria oleae Lepidosaphes ulmi Palpita unionalis Aceria oleae Reseliella oleisuga Melolontha papposa Diseases Spilocea oleagina Fomes, spp. Polyporus spp. Stereum birsutum Gloesporium olivarum Cescorpora cladosporioides Alternaria tenuis Capnodium olaeophilu Verticilium dahliae Pseudomonas savastanoi Sictis panizzei Camarosporium (=Sphaeropsis) dalmaticum Armillaria mellea Meloidogyne spp.
tem, the user can identify the insect by its feeding habitat (leaves, fruits, etc.) (Fig. 1a). When electing the feeding habitat, the user interacts with the expert system by providing a yes-no answer to the identification questions (Fig. 2b). Finally, the system shows the insect that has been identified. Each identified insect is accompanied by thumbnail sized (a small version of the photograph) photographs that include the insect life cycle and the damages produced. These photographs can be expanded to a larger image by clicking on the thumbnail images (Fig. 3a). These fullscreen images assist the user in comparing the case evaluated with the identification result. In the disease subsystem, the diagnosis is based on the parts of the olive tree where the symptoms appear (leaves, fruits, roots, etc.) (Fig. 1b). The process of identification is similar to the insect identification by providing a yes-no answers until the disease identification. Each identified disease is accompanied by thumbnail sized photographs.
3280
J.L. Gonzalez-Andujar / Expert Systems with Applications 36 (2009) 3278–3283
Fig. 1. Interface screens used to select: (a) pests; (b) diseases and (c) weeds.
In the weed subsystem, the identification process starts by determining whether a grass or broadleaf weed plant is going to be classified (Fig. 1c). The user interacts with the expert system by providing a yes-no answer as well (Fig. 2a). Finally, the system shows the weed species that
has been identified. Again each weed is accompanied by thumbnail sized photographs (Fig. 3b) about the weed life cycle. The system records the decision made at each level and also allows for jumping back to previous decision level.
J.L. Gonzalez-Andujar / Expert Systems with Applications 36 (2009) 3278–3283
3281
Fig. 2. An example of an interface screen used to identify: (a) a broadleaf weed specie and (b) an insect specie.
4. System evaluation The evaluation process was carried out in two steps: verification and validation (Harrison, 1991). In the verification step, it was determined the possible errors in the expert system and ensured that the expert system performed as intended. Verification consisted of tracing all pathways to determine their correctness. This was accomplished by running the program many times, giving all the combinations of possible answers. The result of each consultation was verified by a different specialist working in the area of olive crop protection. The second step of the evaluation was validation. In this step, we used the methodology validation by the end users or live testing (Mosqueira-Rey & Monet-Bonillo, 2000).
The validation process was conducted by two groups. The first group was formed 20 technicians and the second group consisted of 20 students from agricultural courses. The three groups were asked to mark in a table-like questionnaire the following criteria (Kaloudis, Anastopoulos, Yialouris, Lorentzos, & Sideridis, 2005): usefulness, user friendliness, easiness to learn, and educational relevance. The evaluated mark these criteria on a continuum 1–10 scale, corresponding to the following responses: 1 unsatisfactory and 10 extremely satisfactory. According to the validation results the system was considered very satisfactory with an average rank of 9.28 by technicians and of 9.13 by students with a statistic mode ranking 10 in all the cases (Fig. 4 and 5). The expert system was found in general more satisfactory by the technicians than the students. This is because the system was consid-
3282
J.L. Gonzalez-Andujar / Expert Systems with Applications 36 (2009) 3278–3283
Fig. 3. An example of an interface screen showing the specific conclusion of: (a) identification of an insect specie and (b) identification of a weed specie. Both images present thumbnail-sized images which can be expanded to larger images by clicking on the image.
ered as a professional tool and there was able to provide consultation in a rapid way. The usefulness of the system was considered very satisfactory (Fig. 4a). Student ranked an average of 9.15 and technicians ranked 9.25. It was really satisfactory the response of the technicians because this question is evaluating the system as a professional tool. System friendliness
was also considered very satisfactory, especially by the technicians who ranked an average of 9.55. Student ranked an average of 8.95 (Fig. 4b). The expert system was found very satisfactory in relationship with educational relevance, especially for students which ranked 9.30. The technicians ranked 9.05 (Fig 5). This result seems logical because the students are more
J.L. Gonzalez-Andujar / Expert Systems with Applications 36 (2009) 3278–3283
was a determining factor to help in the identification process and the acceptance of the system. At present, the system is stand-alone; in the future we are planning to make it web-based. This modification would make the system accessible to everyone with a computer and Internet connection. Further work includes the extension of the system by the inclusion of control measures.
15 Students Technicians
Frecuency
3283
10
5
Acknowledgements 0 1
2
3
4
5
6
7
8
9
I would like to thank Miguel Angel Ayuso for his help with the program development.
10
Rank 12
References Students
Frecuency
10
Technicians
8 6 4 2 0 1
2
3
4
5
6
7
8
9
10
Rank Fig. 4. Results of evaluation for (a) usefulness and (b) user friendliness.
12 Students
10
Frecuency
Technicians
8 6 4 2 0 1
2
3
4
5
6
7
8
9
10
Rank Fig. 5. Results of evaluation for educational relevance.
concerns about education than technicians who are more concerns about practical applications. Everyone who used the expert system believed that the system had management and educational value. As an educational tool, it can augment the traditional educational methodologies for students and farmers. The use of photos
Aparicio, R., & Alonso, V. (1994). Characterization of vergin olive oils by SEXIA expert system. Progress in Lipid Research, 33, 29–38. Carrascal, M. J., & Pau, L. F. (1992). A survey of expert systems in agriculture and food processing. AI Applications, 6, 27–49. De Andres, F. (2001). Enfermedades y plagas del olivo. Sevilla, Spain: Riquelme y Vargas Ediciones. Edward-Jones, G. (1992). Knowledge-based systems for pest management: An application-based review. Pesticide Science, 36, 143–153. Ellison, P., Ash, G., & McDonald, C. (1998). An expert system for the management of Botrytis cinerea in Australian vineyard. I. Development. Agricultural Systems, 56, 185–207. Gonzalez-Andu´jar, J. L., Garcia de Ceca, J. L., & Fereres, A. (1993). Cereal aphid expert system (CAES): Identification and decision making. Computers & Electronics in Agriculture, 8, 293–300. Gonzalez-Andujar, J. L., & Recio, B. (1996). Aplicacio´n de los Sistemas Expertos en Agricultura. Madrid: Mapa-Mundiprensa. Gonzalez-Andujar, J. L., Fernandez-Quintanilla, C., Izquierdo, J., & Urbano, J. M. (2006). SIMCE: An expert system for seedling weed identification in cereals. Computers & Electronics in Agriculture, 54, 115–123. Harrison, S. R. (1991). Validation of agricultural expert systems. Agricultural Systems, 35, 265–285. Kaloudis, S., Anastopoulos, D., Yialouris, C. P., Lorentzos, N. A., & Sideridis, A. B. (2005). Insect identification expert system for forest protection. Expert Systems with Applications, 28, 445–452. Mahaman, B. D., Harizanis, P., Filis, I., Antonopoulou, E., Yialouris, C. P., & Sideridis, A. B. (2002). A diagnostic expert system for honeybee pests. Computers and Electronics in Agriculture, 36, 17–31. Mahaman, B. D., Passam, A. C., Sideridis, A. B., & Yialouris, C. P. (2003). DIARES-IPM: A diagnostic advisory rule-based expert system for integrated pest management in Solanaceous crop systems. Agricultural Systems, 76, 1119–1135. Mosqueira-Rey, E., & Monet-Bonillo, V. (2000). Validation of intelligent systems: a critical study and a tool. Expert Systems with Applications, 18, 1–16. Plant, R. E., & Stone, N. D. (1991). Knowledge-based systems in agriculture. New York, USA: McGraw-Hill. Scott, A. C., Clayton, J. E., & Gibson, E. L. (1991). A practical guide to knowledge acquisition. Massachusetts, USA: Addison-Wesley.
Expert Systems with Applications Expert Systems with Applications 36 (2009) 3278–3283 www.elsevier.com/locate/eswa
Expert system for pests, diseases and weeds identification in olive crops J.L. Gonzalez-Andujar * Instituto de Agricultura Sostenible-CSIC, Alameda del Obispo, Apdo. 4084, 14080 Cordoba, Spain
Abstract An expert system was developed with the aim of improving decision-making by olive oil growers. Knowledge was obtained from the literature and from experts. The knowledge was then represented in the knowledge base of the expert system in a series of IF–THEN rules. The system is supported by a data base containing information for the identification of 9 weeds, 14 insects and 14 diseases. The system is enhanced with 150 photos and drawings that assist the used in the identification process. The expert system was evaluated following the conventional expert system evaluation methodologies. According to the validation results the system was considered very satisfactory. The program can be used as an identification tool for farmers and technicians and for educational purposes. Ó 2008 Elsevier Ltd. All rights reserved. Keywords: Production rules; Olive oil; Knowledge base; Evaluation; Validation; Decision support system
1. Introduction Although the olive tree originated in Asia, it has been cultivated for over 3000 years in Mediterranean countries, where much of the olive crop is used to make olive oil. The olive is an important crop in terms of both its commercial value and the role it plays in the rural economy of the Mediterranean region, with its millions of producers. Spain is the world’s leading producer of quality olive oil; it is estimated that there are over 215 million olive trees in Spain, covering over 2.300,000 ha. This amounts to over 27% of the world’s olive production extension. Spain has an average annual production of over 800.000 tons of olive oil. Olive crop production is hampered by pests, weeds and diseases (thereafter harmful organisms) which reduce production and quality of olive oil. Although harmful organisms management information is available from different sources, theirs identification is, in many cases, difficult and often requires consultation with a specialist. Expert systems have been developed for many kinds of applications in agriculture, involving diagnosis, predictions, *
Fax: +34 957499252. E-mail address: [email protected]
0957-4174/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2008.01.007
consultation, control, etc. (Carrascal & Pau, 1992; EdwardJones, 1992; Gonzalez-Andujar, Fernandez-Quintanilla, Izquierdo, & Urbano, 2006; Gonzalez-Andujar & Recio, 1996; Kaloudis, Anastopoulos, Yialouris, Lorentzos, & Sideridis, 2005; Mahaman, Passam, Sideridis, & Yialouris 2003). Only a few expert systems have been reported for olive production, mainly for olive oil quality. SEXIA has been developed for the authentication of extra virgin olive oils from different regions of Spain, Italy and Portugal, by means of their fatty acids, alcohols, sterols, methyl sterols and hydrocarbons content (Aparicio & Alonso, 1994). However, none expert system have been developed for the identification of harmful organism in olive crops. A System Expert is proposed in this paper to provide farmers and technicians with information for an early identification of harmful organisms commonly found in olive crops in Spain. Moreover, the system can be useful for training as well as for educational purposes. 2. Methods 2.1. The knowledge base A critical aspect of building an expert system is formulating the scope of the problem and gleaning from the
J.L. Gonzalez-Andujar / Expert Systems with Applications 36 (2009) 3278–3283
source expert the domain information needed to solve the problem. The reliability of an Expert System depends on the quality of knowledge contained in the knowledge base (Plant & Stone, 1991). In the present work, knowledge has been obtained from two sources. We acquired textual information from literature such as extension booklets, reports, papers, etc. The printed material allowed familiarization with the subject and a more effective communication with the experts. Most knowledge was acquired from the experts using conventional interviewing techniques (Scott, Clayton, & Gibson, 1991). The interview methods allowed us to obtain the heuristic knowledge that was not present in the printed material. This knowledge was provided by three experts on crop protection (one plant pathologists, one entomologist and one weed expert). Unstructured and structured interviews were used. The unstructured interviews were used to define the familiar tasks involved in the process of identification, to obtain an initial understanding of the range of complications involved, and to define specific problems (e.g., similarity between some species, number of species to be considered, etc.) for later discussion. The questions were more or less spontaneous and notes were taken on discussion. These methods were complemented with structured interviews. In the structured interviews, we revised and discussed in depth familiar tasks to clarify questions. 2.2. Knowledge representation Amongst the different methods for representing the knowledge production rules are the most frequently used for diagnostic expert systems (Ellison, Ash, & McDonald, 1998; Gonzalez-Andu´jar, Garcia de Ceca, & Fereres, 1993; Mahaman et al., 2002; Plant & Stone, 1991). A rule is composed of a list of IF conditions and a list of THEN and ELSE statements about the appropriate solution to the problem. Rules IF/THEN were used in developing the identification system. The knowledge base contains information for the identification of 9 weed species, 14 insect species and 14 diseases (Table 1) and 150 digital photos and drawings. All of these species are frequently found in olive crops in Spain (De Andres F., 2001). 3. Interface One of the most important design considerations behind the system was that the resulting system should be as userfriendly as possible. The system was divided in three subsystems, namely: insects, diseases and weeds. Each subsystem has its own database. This modularity ensures that only some of the rules are active at any one time. The user operates the system through screens of a graphical user interface. At the beginning of the each diagnosis session the user is prompted to select the corresponding subsystem in the start-up screen (Fig. 1). In the insect identification subsys-
3279
Table 1 Weeds, pests and diseases included in the expert system Weeds Cynodon dactylon Lolium rigidum Allium spp. Conium macalatum Amaranthus spp. Malva parviflora Conyza ssp. Sinapsis arvensis Ecballium elaterium Pests Bractrocera oleae Prays oleae Saissetia oleae Liothrips oleae Coenorrhinus cribripennis Euphyllura olivita Phloeotribus scarabaeoides Hylesinus oleiperda Parlatoria oleae Lepidosaphes ulmi Palpita unionalis Aceria oleae Reseliella oleisuga Melolontha papposa Diseases Spilocea oleagina Fomes, spp. Polyporus spp. Stereum birsutum Gloesporium olivarum Cescorpora cladosporioides Alternaria tenuis Capnodium olaeophilu Verticilium dahliae Pseudomonas savastanoi Sictis panizzei Camarosporium (=Sphaeropsis) dalmaticum Armillaria mellea Meloidogyne spp.
tem, the user can identify the insect by its feeding habitat (leaves, fruits, etc.) (Fig. 1a). When electing the feeding habitat, the user interacts with the expert system by providing a yes-no answer to the identification questions (Fig. 2b). Finally, the system shows the insect that has been identified. Each identified insect is accompanied by thumbnail sized (a small version of the photograph) photographs that include the insect life cycle and the damages produced. These photographs can be expanded to a larger image by clicking on the thumbnail images (Fig. 3a). These fullscreen images assist the user in comparing the case evaluated with the identification result. In the disease subsystem, the diagnosis is based on the parts of the olive tree where the symptoms appear (leaves, fruits, roots, etc.) (Fig. 1b). The process of identification is similar to the insect identification by providing a yes-no answers until the disease identification. Each identified disease is accompanied by thumbnail sized photographs.
3280
J.L. Gonzalez-Andujar / Expert Systems with Applications 36 (2009) 3278–3283
Fig. 1. Interface screens used to select: (a) pests; (b) diseases and (c) weeds.
In the weed subsystem, the identification process starts by determining whether a grass or broadleaf weed plant is going to be classified (Fig. 1c). The user interacts with the expert system by providing a yes-no answer as well (Fig. 2a). Finally, the system shows the weed species that
has been identified. Again each weed is accompanied by thumbnail sized photographs (Fig. 3b) about the weed life cycle. The system records the decision made at each level and also allows for jumping back to previous decision level.
J.L. Gonzalez-Andujar / Expert Systems with Applications 36 (2009) 3278–3283
3281
Fig. 2. An example of an interface screen used to identify: (a) a broadleaf weed specie and (b) an insect specie.
4. System evaluation The evaluation process was carried out in two steps: verification and validation (Harrison, 1991). In the verification step, it was determined the possible errors in the expert system and ensured that the expert system performed as intended. Verification consisted of tracing all pathways to determine their correctness. This was accomplished by running the program many times, giving all the combinations of possible answers. The result of each consultation was verified by a different specialist working in the area of olive crop protection. The second step of the evaluation was validation. In this step, we used the methodology validation by the end users or live testing (Mosqueira-Rey & Monet-Bonillo, 2000).
The validation process was conducted by two groups. The first group was formed 20 technicians and the second group consisted of 20 students from agricultural courses. The three groups were asked to mark in a table-like questionnaire the following criteria (Kaloudis, Anastopoulos, Yialouris, Lorentzos, & Sideridis, 2005): usefulness, user friendliness, easiness to learn, and educational relevance. The evaluated mark these criteria on a continuum 1–10 scale, corresponding to the following responses: 1 unsatisfactory and 10 extremely satisfactory. According to the validation results the system was considered very satisfactory with an average rank of 9.28 by technicians and of 9.13 by students with a statistic mode ranking 10 in all the cases (Fig. 4 and 5). The expert system was found in general more satisfactory by the technicians than the students. This is because the system was consid-
3282
J.L. Gonzalez-Andujar / Expert Systems with Applications 36 (2009) 3278–3283
Fig. 3. An example of an interface screen showing the specific conclusion of: (a) identification of an insect specie and (b) identification of a weed specie. Both images present thumbnail-sized images which can be expanded to larger images by clicking on the image.
ered as a professional tool and there was able to provide consultation in a rapid way. The usefulness of the system was considered very satisfactory (Fig. 4a). Student ranked an average of 9.15 and technicians ranked 9.25. It was really satisfactory the response of the technicians because this question is evaluating the system as a professional tool. System friendliness
was also considered very satisfactory, especially by the technicians who ranked an average of 9.55. Student ranked an average of 8.95 (Fig. 4b). The expert system was found very satisfactory in relationship with educational relevance, especially for students which ranked 9.30. The technicians ranked 9.05 (Fig 5). This result seems logical because the students are more
J.L. Gonzalez-Andujar / Expert Systems with Applications 36 (2009) 3278–3283
was a determining factor to help in the identification process and the acceptance of the system. At present, the system is stand-alone; in the future we are planning to make it web-based. This modification would make the system accessible to everyone with a computer and Internet connection. Further work includes the extension of the system by the inclusion of control measures.
15 Students Technicians
Frecuency
3283
10
5
Acknowledgements 0 1
2
3
4
5
6
7
8
9
I would like to thank Miguel Angel Ayuso for his help with the program development.
10
Rank 12
References Students
Frecuency
10
Technicians
8 6 4 2 0 1
2
3
4
5
6
7
8
9
10
Rank Fig. 4. Results of evaluation for (a) usefulness and (b) user friendliness.
12 Students
10
Frecuency
Technicians
8 6 4 2 0 1
2
3
4
5
6
7
8
9
10
Rank Fig. 5. Results of evaluation for educational relevance.
concerns about education than technicians who are more concerns about practical applications. Everyone who used the expert system believed that the system had management and educational value. As an educational tool, it can augment the traditional educational methodologies for students and farmers. The use of photos
Aparicio, R., & Alonso, V. (1994). Characterization of vergin olive oils by SEXIA expert system. Progress in Lipid Research, 33, 29–38. Carrascal, M. J., & Pau, L. F. (1992). A survey of expert systems in agriculture and food processing. AI Applications, 6, 27–49. De Andres, F. (2001). Enfermedades y plagas del olivo. Sevilla, Spain: Riquelme y Vargas Ediciones. Edward-Jones, G. (1992). Knowledge-based systems for pest management: An application-based review. Pesticide Science, 36, 143–153. Ellison, P., Ash, G., & McDonald, C. (1998). An expert system for the management of Botrytis cinerea in Australian vineyard. I. Development. Agricultural Systems, 56, 185–207. Gonzalez-Andu´jar, J. L., Garcia de Ceca, J. L., & Fereres, A. (1993). Cereal aphid expert system (CAES): Identification and decision making. Computers & Electronics in Agriculture, 8, 293–300. Gonzalez-Andujar, J. L., & Recio, B. (1996). Aplicacio´n de los Sistemas Expertos en Agricultura. Madrid: Mapa-Mundiprensa. Gonzalez-Andujar, J. L., Fernandez-Quintanilla, C., Izquierdo, J., & Urbano, J. M. (2006). SIMCE: An expert system for seedling weed identification in cereals. Computers & Electronics in Agriculture, 54, 115–123. Harrison, S. R. (1991). Validation of agricultural expert systems. Agricultural Systems, 35, 265–285. Kaloudis, S., Anastopoulos, D., Yialouris, C. P., Lorentzos, N. A., & Sideridis, A. B. (2005). Insect identification expert system for forest protection. Expert Systems with Applications, 28, 445–452. Mahaman, B. D., Harizanis, P., Filis, I., Antonopoulou, E., Yialouris, C. P., & Sideridis, A. B. (2002). A diagnostic expert system for honeybee pests. Computers and Electronics in Agriculture, 36, 17–31. Mahaman, B. D., Passam, A. C., Sideridis, A. B., & Yialouris, C. P. (2003). DIARES-IPM: A diagnostic advisory rule-based expert system for integrated pest management in Solanaceous crop systems. Agricultural Systems, 76, 1119–1135. Mosqueira-Rey, E., & Monet-Bonillo, V. (2000). Validation of intelligent systems: a critical study and a tool. Expert Systems with Applications, 18, 1–16. Plant, R. E., & Stone, N. D. (1991). Knowledge-based systems in agriculture. New York, USA: McGraw-Hill. Scott, A. C., Clayton, J. E., & Gibson, E. L. (1991). A practical guide to knowledge acquisition. Massachusetts, USA: Addison-Wesley.