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| Publish by | Navedita Mahajan |
| Project Name | Crop prediction |
| Upload Date | February 12, 2022 |
| Platform | Python |
| Programming Language | Python |
| Database | Mysql |
| Front end | |
| Back end | |
| Project Type | web Application |
| View | 1475 |
Crop Prediction using Machine Learning Life on the farm is tiring and full of effort because a lot of tasks have to be done within the day to achieve the ultimate goal, for example, transplanting seedlings, checking the crops for insects or beetles, checking on their livestock, and many more. However, hard-working is not the only factor that influences production and the rate of return. Choosing the right types of the crop remains equally important. Different crops need a different type of soils, different types and amounts of nutrients, and different types and amounts of water. By selecting the right crop for the given soil conditions and climate, one can optimise yields and save water requirements for irrigation. When a farmer would like to know what kind of crops grow well, they could place sensors in the soil that measure the concentration of various chemicals, and the local weather station delivers accurate information on the current temperature, humidity and average rainfall.
Query any SQLite database using plain English with no SQL knowledge required. The agent automatically discovers the schema, translates plain English questions into SQL queries, executes them and returns human readable answers. Includes auto repair for broken queries. Features include automatic schema discovery, natural language to SQL conversion ... [ Download Source Code ]
A document summarization pipeline capable of handling texts of any length using map-reduce strategy. Long documents are split into chunks, each chunk is summarized independently then all summaries are combined into a final output. Works on books, reports and transcripts. Features include three summary modes Brief, Detailed and Bullet Points, map ... [ Download Source Code ]
An autonomous AI agent that searches the live web in real time to answer any question. Uses the ReAct framework where the agent thinks about what to search, performs the search, observes the results and repeats until confident answer is found. Features include live web search via Tavily API, multi-step ReAct reasoning loop, transparent step by s ... [ Download Source Code ]
Upload any PDF and ask natural language questions about it using Retrieval-Augmented Generation. The program splits the PDF into chunks, embeds them into a FAISS vector store, and retrieves the most relevant sections to answer each question accurately. Key features include PDF ingestion and smart chunking, semantic search via FAISS vector store, ... [ Download Source Code ]
A conversational chatbot built with LangChain and GPT-4o that remembers the full session history. Users type any question and receive intelligent context-aware replies. The bot uses ConversationBufferMemory to retain all previous messages. Features include persistent conversation memory, GPT-4o integration, custom system prompt, clean CLI interf ... [ Download Source Code ]
