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@article{165049,
        author = {Ashish Dakle and Mayur Bakal and Bhausaheb Shinde and Shantanu Naik},
        title = {Soft Computing Approaches Used in  Agrivision Disease Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {11},
        number = {1},
        pages = {413-416},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=165049},
        abstract = {This research paper explores the application of machine learning techniques in the domain of agriculture for the detection of plant leaf diseases. The study investigates various methodologies, including image processing and deep learning, to develop efficient and accurate disease detection models. The paper discusses the importance of early disease detection in preserving crop health and proposes novel approaches to enhance detection accuracy.
The Study describes plant illnesses in the Barracuda mango (Nam-Dok Mai), one of a significant agricultural export crop about Thailand. However, because Thailand is a tropical nation, its environment gives rise to a variety of plant diseases that have an impact on mango trees’ ability to thrive. Due to an agriculturalist is ignorance of the proper classification of plant diseases, several types of agricultural production are reduced. Additionally, there is no framework for offering recommendations for the best method to avoid or treat the sickness that occurs on their farm. Their therapies for diseased plants suffer greatly as a result. 
Consequently, this technique was created to aid an agriculturalist in diagnosing In this study, we introduce a novel approach to accurately identify plant leaf diseases using machine learning techniques. A diverse dataset of labeled leaf images from different plant species and disease types serves as the foundation for our investigation. After applying preprocessing methods such as image normalization and data augmentation, we employ a convolutional neural network (CNN) architecture for disease classification. The model is trained and evaluated through multiple data splits, providing a comprehensive assessment of its performance. Compared to traditional methods, our approach achieves superior results and offers a promising path for integrating AI into agricultural practices. This work contributes to advancing precision agriculture by enabling early detection and targeted management of plant diseases
},
        keywords = {Smart Agriculture, Convolutional Neural Network, Plant Health Diagnostics, Support Vector Mechanism (SVM)},
        month = {},
        }