High-Performance Deep learning to Detection and Tracking Tomato Plant Leaf Predict Disease and Expert Systems

• Azhee Wria Muhamad ^[1] ; Yaser A. Jasim ^[2] ; Mostafa Ismael Shukri Windi ^[3] ; Mustafa Ghanem Saeed ^[4] ; Sadeeer Dhyaa AbdulAmeer ^[4]
  1. [1] Computer Science Department, College of Basic Education, University of Sulaimani, Iraq
  2. [2] Accounting Department, College of Administrative and Financial Science, Cihan University-Erbil, Kurdistan Region, Iraq
  3. [3] Software Engineering Department, College of Computer Sciences and Mathematics, Mosul University, Mosul,Iraq
  4. [4] Computer science Department, College of Science, Cihan University, Slimani, Iraq, Computer science Department, College of Science, Cihan University, Slimani, Iraq

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• Localización: ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, ISSN-e 2255-2863, Vol. 10, Nº. 2, 2021, págs. 97-122
• Idioma: inglés
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  • Nowadays, technology and computer science are rapidly developing many tools and algorithms, especially in the field of artificial intelligence. Machine learning is involved in the development of new methodologies and models that have become a novel machine learning area of applications for artificial intelligence. In addition to the architectures of conventional neural network methodologies, deep learning refers to the use of artificial neural network architectures which include multiple processing layers.In this paper, Convolutional neural network models were designed to detect and diagnose plant disorders by applying samples of healthy and unhealthy plant images analyzed by means of methods of deep learning. The models were trained using an open data set containing 18,000 images of ten different plants, including healthy plants. Several model architectures have been trained to achieve performance of 97 percent when the respectively [plant, disease] paired are detected. This is a very useful information or early warning technique and a method that can be further improved with the substantially high-performance rate to support an automated plant disease detection system to work in actual farm conditions.