An Ai-Powered Based Solution for Automated Plant Disease Detection

Abstract

Reducing agricultural yield and food security all over the world are major impacts of diseases on crops. Particularly in developing regions this is becoming a severe issue. Early disease identification followed by necessary steps is the way to control further infection. But, identification of crop diseases on the spot can be quite tough because of the scarcity of skilled agronomists who can recognize different plant diseases. A web application based framework is introduced in this paper for real time, automatic recognition of leaf diseases using an AI application. With this framework the plants which have got infected with 38 categories of diseases over 14 plants of apple, corn, tomato, grape, peach, strawberry, citrus, etc can be detected automatically. Size of the image can be anything but here, 160 x 160 pixel leaf image is used as input to the algorithm. It would helps to identify the category of the plant, disease, probability of detection and reason of the infection along with suggesting the appropriate solutions. It is being developed in Python language with the support of TensorFlow, Keras and flask web application for instant response via an interactive web application with Drag and Drop facility. In this, experimental results show good accuracy to classify and recognize the leaf diseases making this system a smart application for farmers, researcher and the expert.

Plant phenotyping relevance

植物葉の画像から病害状態を自動推定するAI手法とWebアプリケーションが中心であり、植物の病徴・病害状態を直接評価するため、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 describes a CNN (PHASAL) trained on the PlantVillage dataset, but provides no public deposit, URL, or availability statement for the authors' code, trained model, knowledge base, or any paper-specific dataset. PlantVillage is cited prior work, not a paper-specific asset.

No evidence-backed public reproduction asset is currently recorded.