Application of Drone Surveillance for Advance Agriculture Monitoring by Android Application Using Convolution Neural Network

• Published in: Agronomy (Basel) Vol. 13; no. 7; p. 1764
• Main Authors: Shah, Sabab Ali, Lakho, Ghulam Mustafa, Keerio, Hareef Ahmed, Sattar, Muhammad Nouman, Hussain, Gulzar, Mehdi, Mujahid, Vistro, Rahim Bux, Mahmoud, Eman A, Elansary, Hosam O
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2023
• Subjects: Agricultural industry; Agricultural production; Agriculture; Artificial intelligence; Artificial neural networks; Cameras; Chemical pest control; Classification; Classification systems; Crop diseases; Crop yield; Crop yields; Crops; Deep learning; drone; Economic development; Economic growth; EfficientNet-B3; Farmers; Food security; Food supply; Image detection; leaf disease product; Leaves; Machine learning; Medical imaging; Medical research; Medicine, Experimental; Methyltestosterone; Neural networks; Optimization; Pesticides; plant disease identification; Plant diseases; Smartphones; Support vector machines; Technology application; Unmanned aerial vehicles; User friendly products; Pakistan

Plant diseases are a significant threat to global food security, impacting crop yields and economic growth. Accurate identification of plant diseases is crucial to minimize crop loses and optimize plant health. Traditionally, plant classification is performed manually, relying on the expertise of th...