Vasavi et al. (2022) comprehensively reviewed plant leaf disease detection and classification using image processing, machine learning, and deep learning. They analyzed various methodologies such as datasets, algorithms used, convolutional neural networks (CNN) models used, and overall performance achieved. They also suggested algorithms suitable for use in standard systems, mobile systems, embedded systems, robots, and unmanned aerial vehicles (UAVs). The results include development recommendations focused on extending the automatic and real-time plant leaf disease detection system.
Further to this development, what are the real-world applications of plant leaf disease detection?
It was previously discussed that machine learning algorithms can be used to analyze images of plant leaves and identify the presence of diseases. This can help farmers to detect diseases early to prevent the spread of diseases, thereby minimizing crop losses. Some real-world applications of machine learning for plant leaf disease detection include:
- Automated disease detection system: Utilization of drones or UAVs to monitor crops and detect diseases automatically and in real-time.
- High-tech agriculture: The development of precision farming systems can monitor crops and detect diseases early. This can help farmers to take appropriate action to prevent the spread of diseases and minimize crop losses.
- Disease diagnosis: Machine learning algorithms can be used to diagnose plant foliar diseases based on visual symptoms. This can help farmers to identify the disease and find the right solution to deal with it.
- Crop yield prediction: Monitoring crop development can help farmers plan the harvest and optimize yields.
Overall, machine learning algorithms have the potential to revolutionize plant foliar disease detection and help farmers to sustainably increase agricultural yields.
A Quick and precise crop leaf disease detection is important to increasing agricultural yield in a sustainable manner. We present a comprehensive overview of recent research in the field of crop leaf disease prediction using image processing (IP), machine learning (ML) and deep learning (DL) techniques in this paper. Using these techniques, crop leaf disease prediction made it possible to get notable accuracies. This article presents a survey of research papers that presented the various methodologies, analyzes them in terms of the dataset, number of images, number of classes, algorithms used, convolutional neural networks (CNN) models employed, and overall performance achieved. Then, suggestions are prepared on the most appropriate algorithms to deploy in standard, mobile/embedded systems, Drones, Robots and unmanned aerial vehicles (UAV). We discussed the performance measures used and listed some of the limitations and future works that requires to be focus on, to extend real time automated crop leaf disease detection system.
Crop leaf disease detection and classification using machine learning and deep learning algorithms by visual symptoms: a review
Pallepati Vasavi, Arumugam Punitha, T. Venkat Narayana Rao
By: I. Busthomi
