IEEE Paper: AI Precision Farming

I’m preparing an IEEE-format research paper for an international college conference and need a skilled writer-researcher to help craft the entire manuscript. The focus is precision farming and resource optimization, and I want it to go beyond a literature survey—I’m aiming to introduce a fresh model or algorithm built around historical agricultural data.

Here’s the scope in plain terms:

• Frame a clear problem statement that shows why traditional practices waste inputs and how AI can optimise them.
• Design or refine an algorithm that mines past yield, soil, and weather records to recommend precise fertiliser, water, or pesticide schedules. You may choose a well-known approach (e.g., random forest, LSTM, or optimisation heuristics) but it must be customised, benchmarked, and justified as a contribution.
• Run experiments on publicly available historical datasets (Kaggle, FAO, state ag-extension sources—whichever offers depth and citation clarity) and report accuracy, cost savings, or other relevant metrics.
• Present results in standard IEEE two-column format: abstract, keywords, introduction, related work, methodology, results, discussion, conclusion, references. Include high-resolution graphs, tables, and a concise algorithm flowchart.
• Adhere strictly to IEEE referencing and citation style; at least 20 recent sources spanning journals, conferences, and reputable ag-tech reports.

Acceptance criteria
1. Complete 6–8-page manuscript (Word or LaTeX) compliant with IEEE template.
2. All code snippets or pseudo-code placed in an appendix or GitHub link.
3. Figures/tables numbered and captioned; all data sources clearly acknowledged.
4. Zero plagiarism (I will run an originality check).
5. Revisions until the paper is conference-ready.

If you’re comfortable blending AI modelling, agricultural context, and academic writing, let’s get started right away.