Machine Learning Approach for Plant Disease Detection And Suggestion

Machine Learning Approach for Plant Disease Detection And Suggestion

Yash Kamthe 1, Dnyaneshwari Garule 2,

Aishwarya Kudapane 3,  Akash Khalekar 4

Mr. Jitendra R. Chavan 5

1 Yash Kamthe Information Technology & MMCOE

2Dnyaneshwari Garule Information Technology & MMCOE

3Aishwarya Kudapane Information Technology &MMCOE

4Akash Khalekar Information Technology & MMCOE

5 Mr. Jitendra R. Chavan, Assistant Professor, Information Technology & MMCOE

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Abstract

Plant diseases are some of the most relevant topics affecting crop yield productivity because they lead to economic losses for farmers and impact food availability. To address such a concern, we developed a system that utilizes deep learning techniques, particularly Convolutional Neural Networks (CNNs), to identify diseases by analyzing images.

Detection is just one aspect of our project. In addition, we prepared responses that provide actionable steps for managing the illness in question. The suggested approach is to design the tool as user-friendly and intuitive for farmers. Additionally, users have the ability to inquire further about the illness and its management. Although this past research already demonstrated the efficacy of CNNs for such problems, our approach integrates an additional layer. It leverages a potent AI model to suggest relevant, real-time treatment options tailored to the situation. This model serves to issue practical recommendations comparable to those given by an expert.  This system provides farmers with a sophisticated yet simple method for mitigating risks to their crops and enhancing their decision-making by merging straightforward and useful instructions with the automated identification of plant diseases.

Keywords: Streamlit, CNN, Deep Learning, AI API, Image Classification, Plant Disease Detection, Smart Agriculture.