Year: 2025
Type: article
Abstract: These is similar to others studies, these studies explored how various machine leaning and deep learning methods can be employed for real life problems like fruit disease detection. The conclusive evidence is the string of successes that CNNs have gotten in their workplace, which is classification of image-based tasks, including plant disease detection. For example, Deep Learning applied to an apple disease detection problem on convolutional neural networks (CNNs) reach an accuracy level over 90% as Smith et al (2018) confirm, demonstrating the success of deep learning in agribusiness. Consequently, Zhang, and Yang (2020) applied the transfer learning methods for grape disease detection in CNNs explaining the them working well in real-life scenarios. In turn the studies point up the capability of CNN to innovate in pest control and crop studies by providing a quick and precise disease identification.
Authors: Md Firoz Kabir -, Md Mizanur Rahman -, Abdulla Al Mamun -, Md Yousuf Ahmad -
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: hybrid
OpenAlex ID: W4407340167