Agricultural plant diseases identification: From traditional approach to deep learning

Plant disease computerization in agriculture areas an important for every country, as the population rate increases the demand for food supply also increases. Today, the significant adaption of modern techniques and tools increases the accuracy of detection the plant disease. Identifying plant diseases in an early stage can reduce their spread. Early identifying is a beginning stage to fight against disease spreading. For plenty of years, researchers have researched how to tackle the common disease effects amongst humans, animals, and plants. However, there are still many gaps are remaining to identify and explore. In recent years, there have been many researchers using Deep Learning (DL) and Transfer Learning (TL) technologies to detect agricultural diseases based on Machine Learning (ML) algorithms that were developed with the development of Artificial Intelligence (AI) technology. Many, DL architectures are carried out together with numerous diverse visualization strategies to perceive and label the features of plant diseases. Our take a look at additionally makes a specialty of how ML strategies had been moved from conventional ML to DL and additionally numerous overall performance metrics (F1-score, sensitivity, accuracy, etc) are used for the assessment of the architecture/strategies. Some challenges are figure out while identifying the plant disease detection.