Identification and classification of plant diseases using a deep learning approach: A survey

Higher Polytechnic School (ESP) / Cheikh Anta Diop University (UCAD), Dakar, Senegal

Abstract

The loss of agricultural yields is often attributed to plant diseases that affect crops.Early detection and effective control of these diseases remain a major challenge for farmers, leading to substantial yield losses that pose a threat to a growing population. This study aims to identify gaps in the use of deep learning for early detection and classification of plant diseases. Deep learning techniques can help farmers quickly identify diseases, thereby increasing agricultural productivity. Better early detection can help developing countries mitigate food insecurity and economic challenges. The results show that despite promising progress in plant disease identification, several challenges and limitations persist in the literature over the past five years and deserve to be addressed. A critical analysis was conducted to identify the limitations and challenges of existing solutions to serve as a basis for future research on the improvement, early identification and classification crop diseases.