Araştırma Makalesi
Enhancing Pest Detection: Assessing Tuta absoluta (Lepidoptera: Gelechiidae) Damage Intensity in Field Images through Advanced Machine Learning
Öz
The tomato (Solanum lycopersicum (Solanaceae)) is particularly susceptible to Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae), a pest that directly and profoundly influences tomato yields. Consequently, the early detection of T. absoluta damage intensity on leaves using machine learning or artificial intelligence-based algorithms is crucial for effective pest control. In this ground-breaking study, the galleries generated by T. absoluta were examined via field images using the Decision Trees (DTs) algorithm, a machine learning method. The unique advantage of DTs over other algorithms is their inherent capacity to identify complex and vague shapes without the necessity of feature extraction, providing a more streamlined and effective approach. The DTs algorithm was meticulously trained using pixel values from the leaf images, leading to the classification of pixels within regions with and without galleries on the leaves. Accordingly, the gallery intensity was determined to be 9.09% and 35.77% in the test pictures. The performance of the DTs algorithm, as evidenced by a high precision and an accuracy rate of 0.98 and 0.99 respectively, testifies to its robust predictive and classification abilities. This pioneering study has far-reaching implications for the future of precision agriculture, potentially informing the development of advanced algorithms that can be integrated into autonomous vehicles. The integration of DTs in such applications, due to their unique ability to handle complex and indistinct shapes without the need for feature extraction, sets the stage for a new era of efficient and effective pest control strategies.
Anahtar Kelimeler
Convolutional neural networks Decision trees Image processing Pest management Precision agriculture
Kaynakça
- Adi K, Pujiyanto S, Dwi Nurhayati O & Pamungkas A (2017). Beef quality identification using thresholding method and decision tree classification based on android smartphone. Journal of Food Quality 9: 1-10. https://doi.org/10.1155/2017/1674718
- Aliakbarpour H & Rawi C S M (2011). Evaluation of yellow sticky traps for monitoring the population of thrips (Thysanoptera) in a mango orchard. Environmental Entomology 40(4): 873-879. https://doi.org/10.1603/EN10201
- Bhatia A, Chug A & Singh A P (2020). Plant disease detection for high dimensional imbalanced dataset using an enhanced decision tree approach. International Journal of Future Generation Communication and Networking 13(4): 71-78
- Biondi A, Guedes R N C, Wan F H & Desneux N (2018). Ecology, worldwide spread, and management of the invasive south American tomato pinworm, Tuta absoluta: past, present, and future. Annual Review of Entomology 63: 239-258. https://doi.org/10.1146/annurev-ento-031616-034933
- Cely P L, Cantor F & Rodríguez D (2010). Determination of levels of damage caused by different densities of Tuta absoluta populations (Lepidoptera: Gelechiidae) under greenhouse conditions. Agronomía Colombiana 28(3): 392-402
- Collado Jr M C & Tumibay G M (2023). Forecasting onion armyworm using tree-based machine learning models. Global Journal of Engineering and Technology Advances 15(3): 001-007. https://doi.org/10.30574/gjeta.2023.15.3.0095
- Daniya T, Geetha M & Kumar K S (2020). Classification and regression trees with gini index. Advances in Mathematics: Scientific Journal 9(10): 8237-8247. https://doi.org/10.37418/amsj.9.10.53
- Erdoğan H, Bütüner A K & Şahin Y S (2023). Detection of Cucurbit Powdery Mildew, Sphaerotheca fuliginea (Schlech.) Polacci by Thermal Imaging in Field Conditions. Scientific Papers Series Management, Economic Engineering in Agriculture and Rural Development 23(1): 189-192
- Finger R, Swinton S M, El Benni N & Walter A (2019). Precision farming at the nexus of agricultural production and the environment. Annual Review of Resource Economics 11(1): 313-335. https://doi.org/10.1146/annurev-resource-100518-093929
- Gallardo-Romero D J, Apolo-Apolo O E, Martínez-Guanter J & Pérez-Ruiz M (2023). Multilayer Data and Artificial Intelligence for the Delineation of Homogeneous Management Zones in Maize Cultivation. Remote Sensing 15(12): 3131-3148. https://doi.org/10.3390/rs15123131
- Gerdan D, Koç C & Vatandaş M (2023). Diagnosis of Tomato Plant Diseases Using Pre-trained Architectures and A Proposed Convolutional Neural Network Model. Journal of Agricultural Sciences 29(2): 618-629. https://doi.org/10.15832/ankutbd.957265
- Goncalves J P, Pinto F A, Queiroz D M, Villar F M, Barbedo J G & Del Ponte E M (2021). Deep learning architectures for semantic segmentation and automatic estimation of severity of foliar symptoms caused by diseases or pests. Biosystems engineering 210: 129-142. https://doi.org/10.1016/j.biosystemseng.2021.08.011
- González-Cabrera J, Mollá O, Montón H & Urbaneja A (2011). Efficacy of Bacillus thuringiensis (Berliner) in controlling the tomato borer, Tuta absoluta (Meyrick)(Lepidoptera: Gelechiidae). BioControl 56: 71-80. https://doi.org/10.1007/s10526-010-9310-1
- Hamdini R, Diffellah N & Namane A (2021). Color Based Object Categorization Using Histograms of Oriented Hue and Saturation. Traitement du Signal 38(5): 1293-1307. https://doi.org/10.18280/ts.380504
- He K, Zhang X, Ren S & Sun J (2016). Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition pp. 770-778
- Kiobia D O, Mwitta C J, Fue K G, Schmidt J M, Riley D G & Rains G C (2023). A Review of Successes and Impeding Challenges of IoT-Based Insect Pest Detection Systems for Estimating Agroecosystem Health and Productivity of Cotton. Sensors 23(8): 4127-4147. https://doi.org/10.3390/s23084127
- Li W, Wang D, Li M, Gao Y, Wu J & Yang X (2021). Field detection of tiny pests from sticky trap images using deep learning in agricultural greenhouse. Computers and Electronics in Agriculture 183: 106048. https://doi.org/10.1016/j.compag.2021.106048
- Li W, Zhu T, Li X, Dong J & Liu J (2022). Recommending Advanced Deep Learning Models for Efficient Insect Pest Detection. Agriculture 12(7): 1065. https://doi.org/10.3390/agriculture12071065
- Lietti M M, Botto E & Alzogaray R A (2005). Insecticide resistance in argentine populations of Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae). Neotropical Entomology 34: 113-119. https://doi.org/10.1590/S1519-566X2005000100016
- Lin S, Xiu Y, Kong J, Yang C & Zhao C (2023). An Effective Pyramid Neural Network Based on Graph-Related Attentions Structure for Fine-Grained Disease and Pest Identification in Intelligent Agriculture. Agriculture 13(3): 567-587. https://doi.org/10.3390/agriculture13030567
- Liu Y, Zhang Y, Jiang D, Zhang Z & Chang Q (2023). Quantitative Assessment of Apple Mosaic Disease Severity Based on Hyperspectral Images and Chlorophyll Content. Remote Sensing 15(8): 2202-2020. https://doi.org/10.3390/rs15082202
- Nayana B P & Kalleshwaraswamy C M (2015). Biology and external morphology of invasive tomato leaf miner, Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae). Pest Management in Horticultural Ecosystems 21(2): 169-174
- Otsu N (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 9(1): 62-66
- Ozguven M M & Adem K (2019): Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms. Physica A: Statistical Mechanics and its Applications 535: 122537. https://doi.org/10.1016/j.physa.2019.122537
- Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weis R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M & Duchesnay E (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12(2011): 2825-2830
- Sabrol H & Kumar S (2016). Intensity based feature extraction for tomato plant disease recognition by classification using decision tree. International Journal of Computer Network and Information Security 14(9): 622
- Singh A, Ganapathysubramanian B, Singh A K & Sarkar S (2016). Machine learning for high-throughput stress phenotyping in plants. Trends in Plant Science 21(2): 110-124. https://doi.org/10.1016/j.tplants.2015.10.015
- Sriwastwa A, Prakash S, Swarit S, Kumari K & Sahu S S (2018). Detection of pests using color based image segmentation. Second Internatiol Conference on Inventive Communication and Computational Technologies (ICICCT), 20-21 April, Coimbatore, Indiai. https://doi.org/10.1109/ICICCT.2018.8473166
- Şahin Y S, Erdinç A, Bütüner A K & Erdoğan H (2023). Detection of Tuta absoluta larvae and their damages in tomatoes with deep learning-based algorithm. International Journal of Next-Generation Computing 14(3): 555-565. https://doi.org/10.47164/ijngc.v14i3.1287
- Urbaneja A, González‐Cabrera J, Arno J & Gabarra R (2012). Prospects for the biological control of Tuta absoluta in tomatoes of the Mediterranean basin. Pest Management Science 68(9): 1215-1222. https://doi.org/10.1002/ps.3344
- Veres A, Wyckhuys G A K, Kiss J, Tóth F, Burgio G, Pons X, Avilla C, Vidal S, Razinger J, Bazok R, Matyjaszczyk E, Milosavljević I, Vi Le X, Zhou W, Zhu R Z, Tarno H, Hadi B, Lundgren J, Bonmatin M J, van Lexmond B M, Aebi A, Rauf A & Furlan L (2020). An update of the worldwide integrated assessment (WIA) on systemic pesticides. Part 4: Alternatives in major cropping systems. Environmental Science and Pollution Research 27(24): 29867-29899. https://doi.org/10.1007/s11356-020-09279-x
- Vibhute A & Bodhe S K (2012). Applications of image processing in agriculture: a survey. International Journal of Computer Application 52(2): 34-40. https://doi.org/10.5120/8176-1495
- Viggiani G, Filella F, Delrio G, Ramassini W & Foxi C (2009). Tuta absoluta, nuovo lepidottero segnalato anche in Italia. L'Informatore Agrario 65(2): 66-68
- Vishnoi VK, Kumar K & Kumar B (2021). Plant disease detection using computational intelligence and image processing. Journal of Plant Diseases and Protection 128(1): 19-53. https://doi.org/10.1007/s41348-020-00368-0
- Weersink A, Fraser E, Pannell D, Duncan E & Rotz S (2018). Opportunities and challenges for big data in agricultural and environmental analysis. Annual Review of Resource Economics 10(1): 19-37. https://doi.org/10.1146/annurev-resource-100516-053654
- Wolfert S, Ge L, Verdouw C & Bogaardt M J (2017). Big data in smart farming–a review. Agricultural Systems 153: 69-80. https://doi.org/10.1016/j.agsy.2017.01.023
- Yan B, Fan P, Lei X, Liu Z & Yang F (2021). A real-time apple targets detection method for picking robot based on improved YOLOv5. Remote Sensing 13(9): 1619. https://doi.org/10.3390/rs13091619
- Zou K, Ge L, Zhou H, Zhang C & Li W (2021). Broccoli seedling pest damage degree evaluation based on machine learning combined with color and shape features. Information Processing in Agriculture 8(4): 505-514. https://doi.org/10.1016/j.inpa.2020.12.003
Yıl 2024,
Cilt: 30 Sayı: 1, 99 - 107, 09.01.2024
Öz
Kaynakça
- Adi K, Pujiyanto S, Dwi Nurhayati O & Pamungkas A (2017). Beef quality identification using thresholding method and decision tree classification based on android smartphone. Journal of Food Quality 9: 1-10. https://doi.org/10.1155/2017/1674718
- Aliakbarpour H & Rawi C S M (2011). Evaluation of yellow sticky traps for monitoring the population of thrips (Thysanoptera) in a mango orchard. Environmental Entomology 40(4): 873-879. https://doi.org/10.1603/EN10201
- Bhatia A, Chug A & Singh A P (2020). Plant disease detection for high dimensional imbalanced dataset using an enhanced decision tree approach. International Journal of Future Generation Communication and Networking 13(4): 71-78
- Biondi A, Guedes R N C, Wan F H & Desneux N (2018). Ecology, worldwide spread, and management of the invasive south American tomato pinworm, Tuta absoluta: past, present, and future. Annual Review of Entomology 63: 239-258. https://doi.org/10.1146/annurev-ento-031616-034933
- Cely P L, Cantor F & Rodríguez D (2010). Determination of levels of damage caused by different densities of Tuta absoluta populations (Lepidoptera: Gelechiidae) under greenhouse conditions. Agronomía Colombiana 28(3): 392-402
- Collado Jr M C & Tumibay G M (2023). Forecasting onion armyworm using tree-based machine learning models. Global Journal of Engineering and Technology Advances 15(3): 001-007. https://doi.org/10.30574/gjeta.2023.15.3.0095
- Daniya T, Geetha M & Kumar K S (2020). Classification and regression trees with gini index. Advances in Mathematics: Scientific Journal 9(10): 8237-8247. https://doi.org/10.37418/amsj.9.10.53
- Erdoğan H, Bütüner A K & Şahin Y S (2023). Detection of Cucurbit Powdery Mildew, Sphaerotheca fuliginea (Schlech.) Polacci by Thermal Imaging in Field Conditions. Scientific Papers Series Management, Economic Engineering in Agriculture and Rural Development 23(1): 189-192
- Finger R, Swinton S M, El Benni N & Walter A (2019). Precision farming at the nexus of agricultural production and the environment. Annual Review of Resource Economics 11(1): 313-335. https://doi.org/10.1146/annurev-resource-100518-093929
- Gallardo-Romero D J, Apolo-Apolo O E, Martínez-Guanter J & Pérez-Ruiz M (2023). Multilayer Data and Artificial Intelligence for the Delineation of Homogeneous Management Zones in Maize Cultivation. Remote Sensing 15(12): 3131-3148. https://doi.org/10.3390/rs15123131
- Gerdan D, Koç C & Vatandaş M (2023). Diagnosis of Tomato Plant Diseases Using Pre-trained Architectures and A Proposed Convolutional Neural Network Model. Journal of Agricultural Sciences 29(2): 618-629. https://doi.org/10.15832/ankutbd.957265
- Goncalves J P, Pinto F A, Queiroz D M, Villar F M, Barbedo J G & Del Ponte E M (2021). Deep learning architectures for semantic segmentation and automatic estimation of severity of foliar symptoms caused by diseases or pests. Biosystems engineering 210: 129-142. https://doi.org/10.1016/j.biosystemseng.2021.08.011
- González-Cabrera J, Mollá O, Montón H & Urbaneja A (2011). Efficacy of Bacillus thuringiensis (Berliner) in controlling the tomato borer, Tuta absoluta (Meyrick)(Lepidoptera: Gelechiidae). BioControl 56: 71-80. https://doi.org/10.1007/s10526-010-9310-1
- Hamdini R, Diffellah N & Namane A (2021). Color Based Object Categorization Using Histograms of Oriented Hue and Saturation. Traitement du Signal 38(5): 1293-1307. https://doi.org/10.18280/ts.380504
- He K, Zhang X, Ren S & Sun J (2016). Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition pp. 770-778
- Kiobia D O, Mwitta C J, Fue K G, Schmidt J M, Riley D G & Rains G C (2023). A Review of Successes and Impeding Challenges of IoT-Based Insect Pest Detection Systems for Estimating Agroecosystem Health and Productivity of Cotton. Sensors 23(8): 4127-4147. https://doi.org/10.3390/s23084127
- Li W, Wang D, Li M, Gao Y, Wu J & Yang X (2021). Field detection of tiny pests from sticky trap images using deep learning in agricultural greenhouse. Computers and Electronics in Agriculture 183: 106048. https://doi.org/10.1016/j.compag.2021.106048
- Li W, Zhu T, Li X, Dong J & Liu J (2022). Recommending Advanced Deep Learning Models for Efficient Insect Pest Detection. Agriculture 12(7): 1065. https://doi.org/10.3390/agriculture12071065
- Lietti M M, Botto E & Alzogaray R A (2005). Insecticide resistance in argentine populations of Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae). Neotropical Entomology 34: 113-119. https://doi.org/10.1590/S1519-566X2005000100016
- Lin S, Xiu Y, Kong J, Yang C & Zhao C (2023). An Effective Pyramid Neural Network Based on Graph-Related Attentions Structure for Fine-Grained Disease and Pest Identification in Intelligent Agriculture. Agriculture 13(3): 567-587. https://doi.org/10.3390/agriculture13030567
- Liu Y, Zhang Y, Jiang D, Zhang Z & Chang Q (2023). Quantitative Assessment of Apple Mosaic Disease Severity Based on Hyperspectral Images and Chlorophyll Content. Remote Sensing 15(8): 2202-2020. https://doi.org/10.3390/rs15082202
- Nayana B P & Kalleshwaraswamy C M (2015). Biology and external morphology of invasive tomato leaf miner, Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae). Pest Management in Horticultural Ecosystems 21(2): 169-174
- Otsu N (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 9(1): 62-66
- Ozguven M M & Adem K (2019): Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms. Physica A: Statistical Mechanics and its Applications 535: 122537. https://doi.org/10.1016/j.physa.2019.122537
- Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weis R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M & Duchesnay E (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12(2011): 2825-2830
- Sabrol H & Kumar S (2016). Intensity based feature extraction for tomato plant disease recognition by classification using decision tree. International Journal of Computer Network and Information Security 14(9): 622
- Singh A, Ganapathysubramanian B, Singh A K & Sarkar S (2016). Machine learning for high-throughput stress phenotyping in plants. Trends in Plant Science 21(2): 110-124. https://doi.org/10.1016/j.tplants.2015.10.015
- Sriwastwa A, Prakash S, Swarit S, Kumari K & Sahu S S (2018). Detection of pests using color based image segmentation. Second Internatiol Conference on Inventive Communication and Computational Technologies (ICICCT), 20-21 April, Coimbatore, Indiai. https://doi.org/10.1109/ICICCT.2018.8473166
- Şahin Y S, Erdinç A, Bütüner A K & Erdoğan H (2023). Detection of Tuta absoluta larvae and their damages in tomatoes with deep learning-based algorithm. International Journal of Next-Generation Computing 14(3): 555-565. https://doi.org/10.47164/ijngc.v14i3.1287
- Urbaneja A, González‐Cabrera J, Arno J & Gabarra R (2012). Prospects for the biological control of Tuta absoluta in tomatoes of the Mediterranean basin. Pest Management Science 68(9): 1215-1222. https://doi.org/10.1002/ps.3344
- Veres A, Wyckhuys G A K, Kiss J, Tóth F, Burgio G, Pons X, Avilla C, Vidal S, Razinger J, Bazok R, Matyjaszczyk E, Milosavljević I, Vi Le X, Zhou W, Zhu R Z, Tarno H, Hadi B, Lundgren J, Bonmatin M J, van Lexmond B M, Aebi A, Rauf A & Furlan L (2020). An update of the worldwide integrated assessment (WIA) on systemic pesticides. Part 4: Alternatives in major cropping systems. Environmental Science and Pollution Research 27(24): 29867-29899. https://doi.org/10.1007/s11356-020-09279-x
- Vibhute A & Bodhe S K (2012). Applications of image processing in agriculture: a survey. International Journal of Computer Application 52(2): 34-40. https://doi.org/10.5120/8176-1495
- Viggiani G, Filella F, Delrio G, Ramassini W & Foxi C (2009). Tuta absoluta, nuovo lepidottero segnalato anche in Italia. L'Informatore Agrario 65(2): 66-68
- Vishnoi VK, Kumar K & Kumar B (2021). Plant disease detection using computational intelligence and image processing. Journal of Plant Diseases and Protection 128(1): 19-53. https://doi.org/10.1007/s41348-020-00368-0
- Weersink A, Fraser E, Pannell D, Duncan E & Rotz S (2018). Opportunities and challenges for big data in agricultural and environmental analysis. Annual Review of Resource Economics 10(1): 19-37. https://doi.org/10.1146/annurev-resource-100516-053654
- Wolfert S, Ge L, Verdouw C & Bogaardt M J (2017). Big data in smart farming–a review. Agricultural Systems 153: 69-80. https://doi.org/10.1016/j.agsy.2017.01.023
- Yan B, Fan P, Lei X, Liu Z & Yang F (2021). A real-time apple targets detection method for picking robot based on improved YOLOv5. Remote Sensing 13(9): 1619. https://doi.org/10.3390/rs13091619
- Zou K, Ge L, Zhou H, Zhang C & Li W (2021). Broccoli seedling pest damage degree evaluation based on machine learning combined with color and shape features. Information Processing in Agriculture 8(4): 505-514. https://doi.org/10.1016/j.inpa.2020.12.003
Toplam 38 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Mühendislik |
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 9 Ocak 2024 |
| Gönderilme Tarihi | 1 Haziran 2023 |
| Kabul Tarihi | 19 Ağustos 2023 |
| Yayımlandığı Sayı | Yıl 2024 Cilt: 30 Sayı: 1 |
Kaynak Göster
Journal of Agricultural Sciences is published open access journal. All articles are published under the terms of the Creative Commons Attribution License (CC BY).