Comprehensive Study on Plant Disease Detection by using Hybrid Convolution Techniques
DOI : 10.46335/IJIES.2024.9.4.22
Authors : Manesh P. Patil , Dr. Indrabhan S. Borse
Abstract – The food supply of the world depends on plants. Many environmental factors can cause plant diseases, which can lead to significant losses in productivity. However, manually identifying plant diseases is a time-consuming and error-prone process. It is not always a reliable method to identify plant diseases and halt their spread. The early detection of plant diseases made possible by state-of-the-art technologies like machine learning (ML) and deep learning (DL) which can help to overcome these challenges. The most recent advancements in the use of deep learning (DL) and machine learning (ML) methods for plant disease diagnosis are examined in this paper Effectiveness in identifying plant diseases is the primary focus of the study. This work also discusses the drawbacks and challenges of using ML and DL for plant disease identification. These include issues with the quality of the images, data accessibility, and the capacity to identify healthy plants from diseased plants.
