Crop identification and disease classification using traditional machine learning and deep learning approaches
المؤلفون المشاركون
Rangarajan, Aravind Krishnaswamy
Purushothaman, Raja
Prabhakar, Maheswari
Szczepański, Cezary
المصدر
Journal of Engineering Research
العدد
المجلد 11، العدد 1 B (31 مارس/آذار 2023)، ص ص. 228-252، 25ص.
الناشر
جامعة الكويت مجلس النشر العلمي
تاريخ النشر
2023-03-31
دولة النشر
الكويت
عدد الصفحات
25
التخصصات الرئيسية
الملخص EN
Crop and disease classification is one of the important problems in automation of agricultural processes with multicropping method, where the field is cultivated with more than one crop.
In order to solve this classification problem, a study has been carried out in the field cultivating eggplant (Solanum melongena) and tomato (Solanum lycopersicum) using the images obtained from a mobile phone camera.
Textural descriptors, namely, contrast, correlation, energy, and homogeneity, were extracted from the gray-scale converted RGB image for crop identification, that is, tomato or eggplant, and the same descriptors were extracted from the gray-scale converted image from Hue Saturation Value (HSV) for disease classification (due to Cercospora leaf spot disease or two-spotted spider infestation).
Discriminant analysis, Naive Bayes algorithm, support vector machine, and neural network were the classification algorithms used with a resulting best accuracy of 97.61%, 95.62%, 98.01%, and 98.94% for crop identification and 86.09%, 76.52%, 86.96%, and 86.04% for disease classification, respectively.
Similarly, the application of algorithm with 6 histogram-based descriptors for health status detection resulted in an accuracy of 66.67%, 37.04%, 50%, and 72.9%, respectively.
A deep learning algorithm, namely, AlexNet, was also evaluated, which resulted in an accuracy of 100% for crop identification, 89.36% for health status detection, and 81.51% for disease classification.
Among the algorithms, AlexNet resulted in the best average accuracy of 90.29% for the above classification tasks.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Rangarajan, Aravind Krishnaswamy& Purushothaman, Raja& Prabhakar, Maheswari& Szczepański, Cezary. 2023. Crop identification and disease classification using traditional machine learning and deep learning approaches. Journal of Engineering Research،Vol. 11, no. 1 B, pp.228-252.
https://search.emarefa.net/detail/BIM-1495532
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Rangarajan, Aravind Krishnaswamy…[et al.]. Crop identification and disease classification using traditional machine learning and deep learning approaches. Journal of Engineering Research Vol. 11, no. 1 B (Mar. 2023), pp.228-252.
https://search.emarefa.net/detail/BIM-1495532
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Rangarajan, Aravind Krishnaswamy& Purushothaman, Raja& Prabhakar, Maheswari& Szczepański, Cezary. Crop identification and disease classification using traditional machine learning and deep learning approaches. Journal of Engineering Research. 2023. Vol. 11, no. 1 B, pp.228-252.
https://search.emarefa.net/detail/BIM-1495532
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references : p. 249-252
رقم السجل
BIM-1495532
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر