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Possibilities of Using Smart Agricultural Systems in Integrated Pest Management Studies

Yıl 2025, Cilt: 6 Sayı: 2, 81 - 97, 30.12.2025
https://doi.org/10.58728/joinabt.1831254

Öz

Integrated pest management (IPM) is an approach that aims to maintain pest populations in agricultural production at economically and ecologically reasonable levels. IPM aims not only to control pests but also to protect environmental and human health, support natural enemies, and minimize economic losses. Today, the rapid development of digital technologies has enabled the rapid and accurate detection of pests in agricultural areas. Early detection of pests in agricultural areas is crucial for taking the necessary precautions before population density increases, determining the appropriate control method, and preventing yield and quality losses. The Internet of Things (IoT), sensors, drones, image processing techniques, artificial intelligence-based decision support systems, and automation applications enable early detection of pests, monitoring population dynamics, and targeted control. When combined with machine learning and deep learning algorithms, these technologies provide significant benefits in monitoring pest populations, identifying stress symptoms, and optimizing control strategies. Studies using image processing and sensor-based systems have revealed that pest-induced stress in plants can be determined by spectral, thermal, or acoustic changes. Drone-based imaging systems and autonomous robots, in particular, offer rapid data collection and on-site assessment capabilities across large agricultural areas, surpassing traditional observation methods. This approach is strategically important not only for pest control but also for achieving climate change adaptation, resource efficiency, and sustainable agriculture goals. This will contribute to reducing pesticide use and supporting environmentally friendly and economical production processes. Based on this study, the potential use of smart agricultural systems in integrated pest management and compiles research on insect pests.

Etik Beyan

Ethics committee approval is not required for this study.

Destekleyen Kurum

Sakarya University of Applied Sciences Scientific Research Projects Unit

Proje Numarası

158-2023

Teşekkür

We would like to thank SUBU Scientific Research Projects Unit (Project No: 158-2023) for their contributions to this study.

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Entegre Zararlı Yönetimi Çalışmalarında Akıllı Tarım Sistemlerinin Kullanım Olanakları

Yıl 2025, Cilt: 6 Sayı: 2, 81 - 97, 30.12.2025
https://doi.org/10.58728/joinabt.1831254

Öz

Entegre zararlı yönetimi (EZY),tarımsal üretimdeki zararlı popülasyonlarını ekonomik ve ekolojik açıdan makul seviyelerde tutmayı amaçlayan bir yaklaşımdır. EZY, yalnızca zararlılarla mücadele etmeyi değil; çevre ve insan sağlığını korumayı, doğal düşmanları desteklemeyi ve ekonomik kayıpları minimize etmeyi hedeflemektedir. Günümüzde dijital teknolojilerin hızla gelişimi tarımsal alanlarda zararlı böceklerin hızlı ve doğru şekilde tespit edilmesine olanak tanımıştır. Tarım alanlarında zararlıların erken dönemde tespit edilmesi, popülasyon yoğunluğu artmadan gerekli önlemlerin alınması, uygun mücadele yönteminin belirlenmesi, verim ve kalite kayıplarının önlenmesi açısından büyük önem taşımaktadır. Nesnelerin interneti (IoT), sensörler, dronlar, görüntü işleme teknikleri, yapay zekâ destekli karar destek sistemleri ve otomasyon uygulamaları sayesinde zararlıların erken tespiti, popülasyon dinamiklerinin izlenmesi ve hedefe yönelik mücadeleyi mümkün hale getirmektedir. Bu teknolojiler, makine öğrenmesi ve derin öğrenme algoritmalarıyla birleştirildiğinde, zararlı popülasyonlarının izlenmesi, stres belirtilerinin tanımlanması ve mücadele stratejilerinin optimize edilmesi açısından önemli kazanımlar sağlamaktadır. Görüntü işleme ve sensör tabanlı sistemlerle yapılan çalışmalar, bitkilerde zararlı kaynaklı stresin spektral, termal veya akustik değişimlerle belirlenebildiğini ortaya koymaktadır. Özellikle dron tabanlı görüntüleme sistemleri ve otonom robotlar, geniş tarım alanlarında hızlı veri toplama ve yerinde değerlendirme olanağı sunarak klasik gözlem yöntemlerinin ötesine geçmektedir. Bu yaklaşım, yalnızca zararlı kontrolünde değil; aynı zamanda iklim değişikliğine uyum, kaynak verimliliği ve sürdürülebilir tarım hedeflerinin gerçekleştirilmesinde de stratejik bir öneme sahiptir. Bu durum hem pestisit kullanımının azaltılması hem de çevre dostu ve ekonomik bir üretim sürecinin desteklenmesine katkı sağlayacaktır. Bu çalışma kapsamında, entegre zararlı yönetiminde akıllı tarım sistemlerinin kullanım olanakları ele alınmış ve zararlı böceklere yönelik yapılan araştırmalar derlenmiştir.

Etik Beyan

Bu çalışma için etik kurul iznine gerek yoktur.

Destekleyen Kurum

Sakarya Uygulamalı Bilimler Üniversitesi Bilimsel Araştırma Projeleri Birimi

Proje Numarası

158-2023

Teşekkür

Bu çalışmaya katkılarından dolayı SUBÜ Bilimsel Araştırma Projeleri Birimine (Proje No: 158-2023) teşekkür ederiz.

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  • [79]Sanchis, J.G., Guerrero, J.D.M., Olivas, E.S., Sober, M.M., Benedito, R.M., Blasco, J. (2012). Detecting rottenness caused by Penicillium genus fungi in citrus fruits using machine learning techniques. Expert Systems with Applications, 39: 780–785. https://doi.org/10.1016/j.eswa.2011.07.073
  • [80]Barbedo, J.G.A. (2014). Using digital image processing for counting whiteflies on soybean leaves. Journal of Asia Pacific Entomology, 17, 685–694. https://doi.org/10.1016/j.aspen.2014.06.014
  • [81] Wang, D.C., Yang, Y., Qiang, Z.J., Kai, Z.H., Fei, L. (2014). Detection of thrips defect on Green-Peel Citrus using hyperspectral imaging technology combining PCA and Bspline lighting correction method. Journal of Integrative Agriculture, 13(10), 2229-2235. https://doi.org/10.1016/S2095-3119(13)60671-1
  • [82]Yao, Q., Xıan, D., Lıu, Q., Yang, B., Dıao, G., Tang, J. (2014). Automated counting of rice planthoppers in paddy fields based on image processing. Journal of Integrative Agriculture, 13(8), 1736-1745. https://doi.org/10.1016/S2095- 3119(14)60799-1
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Toplam 98 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Tarımda Entomoloji
Bölüm Derleme
Yazarlar

Ceyda Güler 0000-0003-1395-3380

Bahadır Şin 0000-0002-0109-3662

Salih Karabörklü 0000-0003-4737-853X

Proje Numarası 158-2023
Gönderilme Tarihi 27 Kasım 2025
Kabul Tarihi 11 Aralık 2025
Yayımlanma Tarihi 30 Aralık 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 6 Sayı: 2

Kaynak Göster

APA Güler, C., Şin, B., & Karabörklü, S. (2025). Entegre Zararlı Yönetimi Çalışmalarında Akıllı Tarım Sistemlerinin Kullanım Olanakları. Journal of Agricultural Biotechnology, 6(2), 81-97. https://doi.org/10.58728/joinabt.1831254