EasySeer
English中文
Book a demo Download the tool

Fruit classification and grading (sample)

A finished EasySeer delivery for a fruit classification and grading study: an 18-page IEEE-format paper, 22 figures, 84 result graphs, the Python code as it ran, and a recording of the tool taking the project from input to manuscript.

MLRI-Net classifies the fruit type and the quality grade together from ordinary photographs. The paper compares it with four baselines on the public FruitNet and FruitQ datasets.

It is shown as the results came out: the proposed network was best on FruitQ (99.85% grading accuracy), while on FruitNet a fused deep-feature SVM scored higher (99.12% against 97.20%). EasySeer reports what the run produced; it does not tune the story.

The delivery folder holds the IEEE paper (PDF, Word and LaTeX source), all 22 manuscript figures and 84 per-dataset graphs with their CSV data, the complete Python code with requirements, a reproducibility note that maps every figure and table to its source file, and an AI-use statement template. Author names in the paper are placeholders for the scholar to fill in.

Watch the tool at work

Recording: EasySeer taking a project from input to manuscriptFruit classification and grading, about 2 minutes, with narration. Play it, pause it, or jump to a step.
Text description of the recording

The recording shows the EasySeer desktop tool with a fruit classification and grading project. It is narrated.

  1. Input: the scholar chooses the project folder that holds the base papers and the code folder.
  2. Novelty check: a progress list runs, then the tool scores the idea and asks for a yes or no before continuing.
  3. Code generation: the tool lists the generated Python files and shows the main program. A chat panel explains each step.
  4. Code execution: the code runs on the scholar's computer with a live progress list while the chat reports interim results.
  5. Manuscript generation: the paper appears in a reader with a section outline, ready to verify.

8 screens from this sample

Click any screen to enlarge it.

  1. 1

    Input: choose the project folder

    The scholar points EasySeer at a folder holding the concept, the base papers and the dataset.

    EasySeer recording, first step: a Select Folder window for choosing the project folder, which holds the base papers and a code folder.
  2. 2

    Novelty check

    The idea is scored against the base papers and published work. The report asks the scholar to approve or strengthen it before any code is written.

    EasySeer Novelty check in progress for the fruit classification and grading project, with a progress list and a chat panel asking whether to continue.
  3. 3

    Code generation

    The method is written as separate Python files with a main program. Every file can be opened and read.

    EasySeer Code generation screen: the list of generated Python files, 45 files and 43 modules, with the main program open and a chat panel on the right.
  4. 4

    Code execution

    The code runs on the scholar's own computer, with a live progress list. Graphs, tables and logs are saved to the project folder.

    EasySeer Code execution screen: a live progress list while the code runs on the scholar's computer, with interim results reported in the chat.
  5. 5

    Manuscript generation

    The draft is built from the real results, with a section outline, equations and references to verify.

    EasySeer Manuscript screen: a section outline on the left and the generated paper text on the right.
  6. 6

    Delivery: the IEEE paper

    The finished paper, typeset in the IEEE journal layout.

    Page 1 of the fruit classification and grading paper in the IEEE journal layout: title, abstract and introduction.
  7. 7

    Results against the baselines

    Accuracy of every method across the training fractions, as the run produced it.

    Page 14 of the fruit grading paper: line charts of grading and fruit-type accuracy against the training-data percentage, and an accuracy and MCC comparison.
  8. 8

    Ablation

    Removing the segmentation stage improved the result on one dataset. The paper says so.

    Page 15 of the fruit grading paper: the ablation plots and a table comparing the full model with versions without segmentation and without augmentation.

The delivered paper

Read all 18 pages here, or jump to the pages that matter.

Loading the PDF… 0%

Files

More samples

  • Heart disease detection: A finished EasySeer delivery for a heart-disease detection study on the 303 Cleveland records: a 17-…

Book a demo Pricing

← Samples

Last updated 2026-11-09. EasySeer is AI. It creates only an initial draft from the novel concept you provide, and it is designed to follow academic ethical guidelines.