AGRI_SMART_PRESENTATION IPD.pptx its all

about agriculture

1. AGRI SMART:CROP RECOMMENDATION SYSTEM TEAM 9: K.ASFIYA (21RH1A6730) S.AKSHAYA DEEPA(21RH1A6759) M.SRAVANI (21RH1A6707) HARINI (22RH1A6703)

2. INTRODUCTION: A crop recommendation system is a technology that helps farmers choose the right crops to grow by analyzing data like climate, soil, and past performance. It aims to boost productivity, save resources, reduce risk, and increase profits in agriculture. These systems are vital for sustainable and efficient farming.

3. PROBLEM STATEMENT: Agricultural production heavily relies on crop selection. Our goal is to develop an intelligent system that accurately recommends the best crops for specific conditions, maximizing yields and profitability while minimizing environmental impact.

4. LITERATURE SURVEY: We conducted a comprehensive review of existing research and identified key factors affecting crop suitability, such as soil quality, climate, water availability, and market demand. This knowledge forms the foundation of our recommendation system.

5. EXISTING SYSTEM: 5 T raditional crop recommendations rely on general guidelines, local knowledge, and trial and error. However, these methods are limited in accuracy and fail to account for complex interdependencies. Our machine learning algorithm aims to overcome these limitations.

6. PROPOSED METHOLOGY: We build a Machine learning model which includes all the crops which are included in the dataset and we train and test the data. we use the random forest algorithm for combining the outputs of multiple decision trees to reach a single result

7. Summary RESULTS :

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9. CONCLUSION : In conclusion, crop recommendation systems represent a critical advancement in modern agriculture. By harnessing data and technology, they empower farmers to make informed decisions, optimize resource use, and adapt to changing conditions. These systems are pivotal for enhancing agricultural productivity, sustainability, and the well-being of farming communities in our ever-evolving world.

10. REFERENCE: Smith, J. (2020). Machine Learning Applications in Agriculture: A Review. Journal of Agricultural Science, 25(3), 135-148. Jones, A., et al. (2019). A Comparative Study of Crop Recommendation Systems. International Conference on Machine Learning, 72-89.

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