Insights on Assessing Image Processing Approaches Towards Health Status of Plant Leaf Using Machine Learning

Aug 5, 2026

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The paper examines how digital image processing and machine learning can assess plant leaf health by automatically detecting disease conditions from leaf images. It reviews common pipeline components such as feature extraction, segmentation, identification, and classification, while noting that existing methods are not yet conclusive for identifying the optimal approach. The study also highlights strengths, weaknesses, and open research problems to support more definitive conclusions on effectiveness.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Introduction

Diseases in plant leaves

Feature extraction and identification pipeline

Paper organization and scope

Insights on assessing image processing approaches towards health status of plant leaf using machine learning Harsha Raju, Veena Kalludi Narasimhaiah School of Electronics and Communication Engineering, Reva University, Bengaluru, India Article history: Received Jul 3, 2022 Revised Sep 22, 2022 Accepted Oct 22, 2022 Keywords: Classification Disease Identification Image processing Machine learning Plant leaves Corresponding Author: With the advancement of digital image processing in agriculture and crop cultivation, imaging techniques are adopted to acquire real-time health status. Out of all the parts of plants, the leaf is the direct indicator of its health status, and hence applying various image processing approaches could benefit the process of yielding informative cases of plant health. At present, there are various approaches, e.g., feature extraction, segmentation, identification, the classification being evolved up with more dependencies being found in using machine learning; the studies show many contributions towards this challenge. However, it is not yet conclusive to understand the optimal approach. Hence, this paper highlights an explicit strength and weakness associated with the existing approaches existing imaging processing techniques to identify the disease condition from an input of plant leaves' image. The study also contributes to highlighting open-end research problems to have conclusive remarks about effectiveness. This is an open access article under the CC BY-SA license. Harsha Raju School of Electronics and Communication Engineering, Reva University Bengaluru, India [Email: harsh4no1@gmail.com](Email: harsh4no1@gmail.com) Article Info ABSTRACT 1. INTRODUCTION With the increasing population score, there is an increasing demand for quality nutrition from a higher grade of food quality. This can only be confirmed when the cultivation is done because a healthy atmosphere and crops offer higher quality grains. However, diseases in the plants are inevitable, and it offers potential degradation towards the quality of the grains. Therefore, there is a higher degree of concern towards the diseases that inflict plants resulting in minimal production and yield. Such infliction can occur in any plants, right from roots, stems, branches, buds, flowers and leaves. There could be multiple reasons for this viz. adverse climatic condition, degraded quality of soil, poor irrigation, inferior practices in farming and adoption of conventional methods. With the increasing technology usage towards agriculture and cultivation, there are more chances of higher and quality yield in plants [1]–[5] . Sensors can be deployed over the cultivation fields to extract various data associated with the plants [6], [7] . Sensors can capture the images of plants that can be transmitted to another end, where an image processing algorithm can be executed to find the real-time status of the plant’s health. It can be said that the majority of the diseases that have a negative impact on crop yield are highly visible, and this can be an identifier for image processing algorithms. Hence, an algorithm can be constructed based on formulated identifiers matching the specific information about the disease. A human can also assess such visual information in identifying the disease condition in plants. However, a human cannot monitor this abnormality for the crop field of a larger dimension. Therefore, thereis a need for an automated approach that can prevent human interaction from carrying out this task of identification. As most of the problems associated with the disease are visually seen; therefore, this information can be checked autonomously by the machine itself using image processing. However, there are various challenges involved in this process. The first challenge is to perform extraction of features from many crops, and hence feature extraction is one essential operation [8] . There are various feature extrac

FAQs

Why are plant leaves considered an indicator of health status?

Leaf symptoms directly reflect plant health, so analyzing leaf images provides informative evidence for disease assessment and diagnosis.

What main approach components are discussed for disease detection?

The paper covers feature extraction, segmentation, identification, and classification using machine learning-based computer vision.

Why is an automated approach needed instead of relying on humans?

Humans cannot continuously monitor disease across large cultivation areas, while automated image-processing systems can check abnormalities autonomously.

Aug 5, 2026

Upload by Sage