Crop Disease Identification Using Computer Vision And Machine Learning Techniques - International Journal of Advanced Scientific Innovation Volume 06 Issue 08

Aug 3, 2026

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The paper reviews and examines classification approaches for identifying crop plant diseases, emphasizing early detection to protect crop quality and yield. It compares traditional machine learning with deep learning methods, including image processing, machine learning models, artificial neural networks, and deep learning with a particular focus on convolutional neural networks. The survey summarizes targeted diseases, data sources, employed models, and evaluation metrics such as accuracy, IoU, and F1-score, noting that deep learning typically achieves higher accuracy and that key factors influence performance. The work aims to improve diagnosis accuracy and reduce response time, with attention to Indian agricultural settings using authentic datasets.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages4

Reading Time10 min

AccessFree to download

Abstract

I. Introduction

Importance of plants and impact of plant diseases

Limitations of traditional diagnosis

Motivation for automated deep-learning systems

CNN-based plant disease detection

Related work and evaluation metrics

Proposed DCNN approach for multi-disease leaf diagnosis

Crop Disease Identification Using Computer Vision And Machine Learning Techniques International Journal of Advanced Scientific Innovation Volume 06 Issue 08, August 2024 ISSN: 2582-8436 Kavya S P G Student Dept of CSE, SIET, Tumkur [kavya26feb@gmail.com](kavya26feb@gmail.com) Dr. Basavesha D Associate Professor Dept of CSE, SIET, Tumkur [basavesha@gmail.com](basavesha@gmail.com) Dr. Girish L Associate Professor Dept of AI&DS, SIET, Tumkur [girishltumkur@gmail.com](girishltumkur@gmail.com) Abstract The paper examines various classification methods dedicated to the identification various illnesses of plants, emphasizing the crucial role of early detection in preserving crop quality and yield. It delves into different approaches, including image processing, machine learning, artificial neural networks, and, more prominently, deep learning. With a focus on emerging methods for in-depth comprehension, the review provides a detailed analysis starting from traditional machine learning methods. It outlines the targeted crop diseases, the utilized models, data sources, and performance metrics employed across studies for disease identification. The review highlights deep learning’s superior accuracy compared to traditional methods and identifies important elements that have an influence its performance. By documenting these approaches, the paper aims to enhance accuracy and reduce response time in plant disease identification, with specific attention given to efforts in diagnosing diseases in Indian agricultural settings using authentic datasets. I. INTRODUCTION Plants are indispensable for the economy and mitigating climate change. With global recognition of climate change asa pressing issue, countries like Pakistan are engaged in treeplanting initiatives to maintain ecological balance. The extinction of plants due to industrial operations is linked to ozone layer depletion and global warming. Climate change forecasts predict a future change rate far exceeding historical warming rates. Moreover, plants are crucial in the food industry, where ensuring global production balance is a significant challenge. Additionally, plants play an important role in healthcare but can suffer from various diseases. Economically, losses in food, fiber, and ornamental production due to plant pests and diseases are estimated to be in the hundreds of billions annually. Given their essential role in human survival, global concern for plant conservation and protection is paramount. Common symptoms of plant diseases include leaf rust, stem rust, sclerotinia, powdery mildew, anthracnose, phytophthora, septoria brown spot, and chlorosis. Traditionally, experts identify plant diseases by examining the physical condition of leaves, stems, or fruits, requiring significant human resources.[1] However, in today’s era of technology and automation, this approach is deemed inefficient. There is a growing interest in developing automated systems to diagnose plant diseases. Numerous investigations have explored this using traditional machine learning methods. This study aims to create an automated system for plant disease detection using deep learning techniques, a subset of machine learning.[2] Deep learning offers advantages Unlike traditional machine learning since feature engineering and domain expertise are no longer required. Instead, deep learning algorithms, similar to CNNs, or Convolutional Neural Networks,able to automatically extract characteristics from images.CNNs are highly proficient in extracting visual features..[3] The proposed system, known as a plant disease detector, utilizes CNNs to examine plant photos leaves to identify illnesses. By utilizing in order to teach the network a large collection of pictures showing both both well and ill plants, The example can learn to precisely categorize the kind of disease present in plant leaves. This automated approach possesses the capacity to revolutionize diagnosisas well as c

FAQs

Why is early detection of crop diseases important in the paper?

Early detection helps preserve crop quality and yield by enabling faster and more accurate identification of illnesses in plants.

What traditional and advanced methods does the paper discuss for disease identification?

The paper covers image-processing and traditional machine learning approaches, and compares them with deep learning approaches, especially convolutional neural networks (CNNs).

How do evaluation metrics like CA, IoU, and F1-score relate to study comparisons?

The review states that different studies use metrics such as classification accuracy (CA), Intersection over Union (IoU), and F1-score, which affects how performances are reported and compared.

Aug 3, 2026

Upload by Asher