Makine Öğrenimi (Ml) ve Tarımdaki Uygulamaları
Özet
Bu çalışma, artan dünya nüfusunun beslenme ve istihdam ihtiyaçlarını karşılamada kritik bir role sahip olan tarım sektöründe yapay zekâ ve makine öğrenimi (ML) teknolojilerinin kullanımını, yöntemlerini ve sunduğu yenilikçi fırsatları kapsamlı bir şekilde incelemektedir. Dijital ve hassas tarım uygulamaları çerçevesinde, çeşitli sensörler ve IoT teknolojileri aracılığıyla toplanan yüksek hacimli ve karmaşık verilerin işlenmesinde veriye dayalı ML algoritmalarından yararlanılmaktadır. Çalışmada boyut azaltma analizi, regresyon, kümeleme, Bayes modelleri, örnek tabanlı modeller, karar ağaçları, yapay sinir ağları ve destek vektör makineleri gibi temel öğrenme modelleri teorik esaslarıyla ele alınmaktadır. Makine öğrenimi tekniklerinin tarımsal üretimdeki temel uygulama alanları; uygun tür seçimi, morfolojik özniteliklere dayalı bitki tanıma ve sınıflandırma, iklim ve toprak parametreleriyle verim tahmini, hiperspektral görüntüleme sistemleriyle mahsul kalitesi ve olgunluk tespiti, kimyasal kullanımını optimize eden hastalık ve yabancı ot tespiti, hassas hayvancılıkta hayvan sağlığı ile davranışlarının gerçek zamanlı takibi ve evapotranspirasyon tahminine dayalı akıllı su yönetimini kapsamaktadır. Sonuç olarak, büyük veri analitiği ile entegre edilen ML modelleri, çevresel olumsuz etkileri en aza indirgeyerek tarımsal verimliliği, sürdürülebilirliği ve finansal getiriyi artırmada son derece yüksek bir potansiyel sunmaktadır.
This study comprehensively examines the use, methods, and innovative opportunities of artificial intelligence and machine learning (ML) technologies in the agricultural sector, which plays a critical role in meeting the nutritional and employment needs of the growing world population. Within the framework of digital and precision agriculture applications, data-driven ML algorithms are utilized to process complex and high-volume data collected through various sensors and IoT technologies. The study discusses fundamental learning models, including dimensionality reduction analysis, regression, clustering, Bayesian models, instance-based models, decision trees, artificial neural networks, and support vector machines, along with their theoretical foundations. Main application areas of machine learning techniques in agricultural production encompass suitable species selection, plant recognition and classification based on morphological features, yield prediction using climate and soil parameters, crop quality and maturity detection through hyperspectral imaging systems, disease and weed detection that optimizes chemical usage, real-time monitoring of animal health and behavior in precision livestock farming, and smart water management based on evapotranspiration estimation. Consequently, ML models integrated with big data analytics offer immense potential to increase agricultural productivity, sustainability, and financial returns while minimizing adverse environmental impacts.
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