A Journey on The Exploration of Village Plant Dataset Using ‎Machine Learning Models

Abstract

This article is coined for investigating the Village Plant dataset. Many researchers worldwide, carrying out their research in ‎the domain of agriculture, are dependent on this open source dataset. A plant is vulnerable to several infirmities during its period of growth. ‎Detection of the plant’s ill health and monitoring the environmental parameters is the most challenging task in agriculture. Plant disease epidemic may have a significant effect on crop production, reducing the country’s wealth. Early diagnosis of the occurrence of ill health in plants ‎and the remedies are feasible using Artificial Intelligence (AI). Currently, methods like Deep Learning (DL) algorithms, machine vision ‎techniques, and robotics play an important role in monitoring plant diseases and the growth status. This dataset contains multi-fold in-‎information about the plants. They include the normal and diseased images of plants like Bell Pepper, Tomato, Cucumber, and Potato. An Internet ‎of Things (IoT) based plant data collection and integration system will provide data for this research, which optimizes the feature set through ‎Ant Colony Optimization (ACO) for improving prediction in feature selection using deep learning models like DenseNet, ResNet 50, ‎VGG 19, and Long Short-Term Memory (LSTM) networks, which in turn enhances plant productivity with advances in AI-driven agricul‎tural diagnostics for plant stress prediction‎.

Plant phenotyping relevance

植物の正常・罹病画像から植物の病気・ストレス状態を推定する画像解析ワークフローとデータセット利用が研究の中心であり、植物状態のフェノタイピング手法に該当する。

Code and data availability

植物フェノタイピング解析を再現する公開資産であることを、入力本文と直接リンクから確認できなかったため保留しました。

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