Yonca (Medicago sativa L.) Bitkisinde Yaygın Hastalıkların Makine Öğrenmesi Yöntemi ile Teşhisi ve Mobil Uygulama Geliştirilmesi

Öz

Yüksek verimi ve yüksek besin değeri ile bilinen Yonca (Medicago sativa L.), yaygın olarak yetiştirilen çok yıllık bir yem bitkisidir. Yonca Mozaik Virüsü (AMV), Mildiyö ve Yaprak Lekesi gibi çeşitli hastalıklara maruz kalmaktadır. Bu hastalıkların zamanında ve doğru şekilde teşhis edilmesi, bitki sağlığının korunması, verimliliğin artırılması ve kimyasal kullanımının en aza indirilmesi açısından büyük önem taşımaktadır. Bu çalışmada, yaygın yonca hastalıklarının tespiti amacıyla makine öğrenmesi tekniklerine dayalı bir mobil uygulama geliştirilmesi hedeflenmiştir. AMV, Mildiyö, Yaprak Lekesi ve sağlıklı yapraklardan oluşan dört kategoriye ait toplam 557 görüntü içeren açık erişimli bir veri seti kullanılarak Google’ın Teachable Machine platformunda derin öğrenme modeli oluşturulmuştur. Bu model, MIT App Inventor 2 ile geliştirilen bir mobil uygulamaya entegre edilmiştir. Model, TensorFlow Lite ile mobil cihazlara uygun hale getirilmiş Konvolüsyonel Sinir Ağı (CNN) mimarisi kullanmaktadır. Uygulama, Türkçe dil desteği sunan kullanıcı dostu bir arayüz aracılığıyla, mobil telefon kamerası kullanılarak gerçek zamanlı hastalık teşhisi imkânı sağlamaktadır. Ayrıca, kullanıcı bilgileri, zaman ve GPS konumu gibi meta verilerle birlikte görüntülerin Google Drive ve Google E-Tablolar üzerinde bulut tabanlı olarak kaydedilmesine olanak tanımaktadır. Eğitilmiş model, test verisi üzerinde %85 doğrulukla sınıflandırma yapmıştır. Geliştirilen uygulama, sürdürülebilir tarım uygulamalarını destekleyen, ekonomik ve erişilebilir bir hastalık teşhis aracı sunmaktadır. Gelecekteki çalışmalarla, uygulamanın farklı bitki türleri ve hastalıkları kapsayacak şekilde genişletilmesi önerilmektedir. Bu çalışma, yapay zekâ ve mobil teknolojinin entegrasyonuyla çiftçilere yerinde karar desteği sağlayabilecek yenilikçi bir çözüm ortaya koymaktadır.

Anahtar Kelimeler

• Image processing
• Clover diseases
• Machine learning
• Mobile applications
• Smart agriculture

Diagnosis of Common Diseases in Alfalfa (Medicago sativa L.) Plant Using Machine Learning Method and Development of a Mobile Application

Öz

Alfalfa (Medicago sativa L.), known for its high yield and nutritional value, is a widely cultivated perennial legume subject to various diseases including Alfalfa Mosaic Virus (AMV), Downy Mildew, and Leaf Spot. Timely and accurate identification of these diseases is highly important to maintain crop health, improve productivity, and minimize the use of chemicals. In this study it was aimed to develop a mobile application-based machine learning technique for the detection of major alfalfa diseases. Open-access image dataset of 557 images for four categories—AMV, Downy Mildew, Leaf Spot, and healthy leaves, a deep learning model was used in Google’s Teachable Machine platform. The model then integrated into a mobile application developed with MIT App Inventor 2. The model employs a Convolutional Neural Network (CNN) architecture optimized for mobile deployment via TensorFlow Lite. The application provides a user-friendly interface in Turkish and allows real-time disease classification through mobile phone’s camera. Furthermore, it incorporates cloud-based storage using Google Drive and Google Sheets to log images with metadata including user input, time, and GPS location. The trained model achieved 85% classification accuracy on the test set. The resulting application offers a cost-effective, accessible tool for disease diagnosis in alfalfa cultivation, supporting sustainable agricultural practices. Future studies could expand the application to include a broader range of crops and diseases. The study highlights the potential of integrating artificial intelligence and mobile technology to empower farmers with on-the-spot decision support tools.

Anahtar Kelimeler

• Image processing
• Clover diseases
• Machine learning
• Mobile applications
• Smart agriculture

Kaynakça

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