Ensemble-Based Plant Disease Detection with Mini TensorFlow on Risc Devices and Chatbot

Abstract

The research trains and evaluates multiple CNN architectures, including Basic CNN, AlexNet, VGG16, and EfficientNet B0, to enhance the accuracy of plant disease identification. Each model was tested using the New Plant Diseases Dataset from Kaggle, which includes various plant species and diseases, in order to assess performance, accuracy, and efficiency. The trained models were subsequently integrated into a Marathi language chatbot to facilitate real-time disease detection and provide agricultural guidance. This study provides valuable insights into the strengths and limitations of different models for precision agriculture, especially in applications that support regional languages to encourage accessible and sustainable farming practices. Additionally, a Marathi language chatbot is incorporated, enabling users to obtain plant disease information instantly through a user-friendly web application

Plant phenotyping relevance

植物病害状態を画像から識別するCNN群を訓練・評価し、リアルタイム検出システムへ統合しており、病害フェノタイプの取得・推定手法が中心である。

Code and data availability

The paper's plant-phenotyping input is the public New Plant Diseases Dataset from Kaggle (healthy/diseased leaf images of tomato, potato, corn) used to train and evaluate the CNN ensemble. No author analysis code, trained model checkpoints, or supplementary data deposit is mentioned with an availability statement orURL