Abstract: Plants and crops that are afflicted by pests or diseases have an impact on agricultural productivity. Generally, farmers and professionals examine plants with their naked eyes in order to discover and identify illness. However, this procedure is time-consuming and frequently wrong. Data augmentation and picture pre-processing techniques are used to detect plant diseases, resulting in faster and more reliable findings. The purpose of this study is to present a novel method to the construction of a disease recognition model using CNN, which supports plant leaf image classification utilising convolutional networks and the Deep Learning algorithm. Advances in technology allow for the expansion and enhancement of plant protection practises, as well as development in the computer vision sector and using machine learning applications in the world of agriculture and farming, making it easier and more successful with a completely unique training method. All of the necessary steps and modules for implementing the plants disease recognition model are fully described throughout the paper, beginning with image collection to create a database, which will be evaluated by agricultural experts, and a deep learning algorithm framework to perform CNN training. Using the deep convolutional neural network that we will train, test, and validate, this technique paper might be a novel strategy to detecting and identifying plant illnesses. The created CNN model's development and innovation are shown in its simplicity; healthy leaves and backdrop pictures are consistent with previous CNN models, Using CNN, the model is able to discriminate between damaged and healthy leaves. Plants are the world's primary food source. Plant infections and illnesses are a significant hazard, and the most frequent method of diagnosing plant diseases is to examine the plant body for visible signs and growth [1]. Different research efforts intend to identify realistic techniques to plant protection and support our farmers as an alternative to the old time-consuming process. In recent years, technological advancements have spawned a slew of new ways to complement old procedures [2]. In picture classification challenges, deep learning approaches are particularly powerful and successful.
Keywords: Plant's Leaf Disease, CNN model (Deep Learning Algorithm)
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DOI:
10.17148/IJARCCE.2022.11326
