The Importance of Addressing Plant Diseases in the Agricultural Sector
The agricultural sector is crucial to the economic growth of a country, and in the Indian context, it is especially significant as it provides livelihoods for millions of people in rural areas. However, plant diseases pose a significant threat to this sector and can have far-reaching implications on the economy.
There are various factors that contribute to the occurrence of plant diseases, including the use of synthetic fertilizers, outdated agricultural practices, and environmental conditions. These diseases can lead to a decrease in farm yield, making it difficult for farmers to sustain their livelihoods.
Exploring AI and Machine Learning for Disease Detection
To tackle the issue of plant diseases, researchers have turned to AI and machine learning techniques. These technologies have shown great potential in detecting and diagnosing plant diseases accurately and efficiently.
Traditional techniques for disease detection often rely on visual inspection of plants by experts. However, these methods can be time-consuming, subjective, and may not always yield accurate results. In contrast, AI and machine learning algorithms can analyze large volumes of data quickly and identify patterns that may not be evident to the human eye.
Deep learning techniques, in particular, have emerged as a powerful tool in disease detection. By training deep neural networks on large datasets of plant images, these models can learn to classify and identify different diseases accurately.
The Significance of Datasets and Explainable AI
Availability of high-quality datasets is crucial for training accurate machine learning models. This survey recognizes the importance of datasets in disease detection and provides a summary of available datasets for researchers, allowing them to benchmark their models against existing data.
Additionally, the survey explores the concept of Explainable AI (XAI) in the context of plant disease detection. XAI aims to enhance the interpretability and transparency of deep learning models’ decisions, making them more accessible and understandable to end-users.
By leveraging XAI techniques, researchers can provide insights into how these models arrive at their conclusions, enabling farmers and other stakeholders in the agricultural sector to make more informed decisions. This transparency fosters trust in the AI technology and facilitates the adoption of efficient and sustainable agricultural practices.
Conclusion
Addressing plant diseases is crucial for the sustainable development of the agricultural sector. The integration of AI and machine learning techniques, along with the availability of high-quality datasets, offers immense potential for detecting and combatting plant diseases effectively.
This research survey consolidates knowledge on common plant leaf diseases, evaluates traditional and deep learning techniques for disease detection, and provides a summary of available datasets. Furthermore, it explores the concept of Explainable AI to enhance the interpretability of deep learning models’ decisions.
By bridging the gap between researchers, practitioners, and stakeholders in the agricultural sector, this survey aims to foster the development of efficient and transparent solutions for combating plant diseases and promoting sustainable agricultural practices.
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