Paddy and Tomato Plants Disease Detection Using Deep Learning and Pest Control Recommendation
Abstract
Introduces a groundbreaking approach for the disease detection along control of leaf diseases and pests in paddy and tomato crops. Utilizing Convolutional Neural Networks (CNNs), the model accurately classifies diverse leaf diseases based on an extensive dataset. Beyond disease recognition, the system integrates intelligent pest control strategies, offering a comprehensive solution for farmers. The proactive nature of the integrated system enables timely interventions, minimizing crop damage and economic losses. Emphasizing precision agriculture, the model facilitates targeted responses to potential threats. The study's significance lies in its contribution to sustainable agriculture by promoting environmentally conscious practices through reduced reliance on conventional treatments. In essence, this research highlights the transformative potential of deep learning in advancing crop health management, ensuring enhanced yield, and fostering sustainable agricultural practices.
Keywords
Leaf disease dataset, CNN algorithms etc.
Citations
IRE Journals:
K. Monika , Mukkamalla Pragna Sree , D. Karthik Srinivas , Nune Sai Charan Teja , Akkamahadevi C
"Paddy and Tomato Plants Disease Detection Using Deep Learning and Pest Control Recommendation" Iconic Research And Engineering Journals Volume 7 Issue 7 2024 Page 235-243
IEEE:
K. Monika , Mukkamalla Pragna Sree , D. Karthik Srinivas , Nune Sai Charan Teja , Akkamahadevi C
"Paddy and Tomato Plants Disease Detection Using Deep Learning and Pest Control Recommendation" Iconic Research And Engineering Journals, 7(7)
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
Important Links
For Authors
Volume 7:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 6:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 5:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 4:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 3:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12