Image based Chili Crop Disease Prediction Using Deep Transfer Learning

Abstract

Crop diseases have a terrible impact on food protection and can result in considerable reductions in both the supply and quality of agricultural products. Human professional have traditionally been relying on to diagnose crop diseases caused by insects, pests, virus, bacteria, fungal, inadequate nutrition, or adverse environmental conditions. This, however, is costly, time demanding, and in some situations unworkable. Thus, in the area of agricultural information, the automatic identification of crop diseases is significantly required. Many strategies have been presented to solve this challenge, with deep learning becoming as the preferred approach due to its outstanding performance.This research describes a method for detecting chili leaf diseases using a deep convolutional neural network. We compared performances of four architectures: MobileNet, Inception-ResnetV2, EfficientNetB0, and DenseNet. The proposed approach evaluated the findings using measures such as accuracy, loss and time. Our model compares favorably to EfficientNetB0 with an accuracy of 0.995, a loss of 0.023, and time is 5 minutes 45 seconds. EfficientNetB0, a compact deep learning architecture has fine tuned to classify two forms of chili leaf diseases. The method was tested on 2475 photos from the Plant Village dataset.

Plant phenotyping relevance

唐辛子葉の病害状態を画像から推定する深層学習手法が研究の中心であり、複数モデルの性能比較・評価も実施しているため、植物フェノタイピング手法として収録する。

Code and data availability

The paper fine-tunes standard architectures (MobileNet, Inception-ResNetV2, EfficientNetB0, DenseNet) on a subset of the third-party Plant Village dataset. No author-deposited dataset, code, trained models, or supplement with reproducible assets is mentioned; the only public resource (Plant Village, ref [20]) is cited,

No evidence-backed public reproduction asset is currently recorded.