Detect Plant Disease and Recommend Fertilizer and Supplement Using CNN & Mobile Net Algorithm

Abstract

Agriculture plays a crucial role in the Indian economy. Early detection of plant diseases is very much essential to prevent crop loss and further spread of diseases. Most plants such as apple, tomato, cherry, grapes show visible symptoms of the disease on the leaf. These visible patterns can be identified to correctly predict the disease and take early actions to prevent it. This can be overcome by the use of machine learning and deep learning algorithms. Hence, we are proposing a method that which is detecting the disease of a tomato plant from their leaf images. Here the process is performed with the deep learning algorithms Convolutional Neural Network (CNN), and MobileNet which is a one of the transfer learning method of CNN. Once after training the dataset with the algorithms, the accuracy of algorithms is compared and the images are classified. And the precautions are also provided for the classified plant.

Plant phenotyping relevance

トマト葉画像から病害状態をCNN/MobileNetで推定・分類する手法が研究の中心であり、植物病害フェノタイピングに該当する。

Code and data availability

The paper describes training CNN and MobileNet models on the public PlantVillage/Kaggle leaf-image dataset (~54,305 images, 38 classes), but provides no authors' code, trained model checkpoints, or dataset deposit URL. The only public resource mentioned is the third-party PlantVillage dataset, referenced via citedprior

No evidence-backed public reproduction asset is currently recorded.