A Review of Plant Disease Detection Based on Deep Learning Models - Hamijah Mohd Rahman; Arbaiy, Nureize; Ruslan, Rusma Anieza (Journal of Applied Science, Technology and Computing, 2024)
Maintaining the agricultural sector is a huge requirement due to the global food supply crisis. The increasing global population, extreme climate change and disease are major problems that happen across the world. It is challenging as traditional farming methods are not enough to sustain the demand for agricultural production. Thus, the automation method of artificial technology, such as deep learning, was introduced to increase production and prevent loss in agriculture. This study researches deep learning approaches in real-time agricultural applications to avoid plant disease. The research focused on publications between 2018 and 2023, and the study discussed the effectiveness of using this method in improving the efficiency of plant disease detection. This study will be a reference for agricultural experts and academicians to understand better the applicability of deep learning in decision-making and problem-solving applications.