International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 04 | Apr 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 442 Detection of Plant Diseases Using CNN Architectures Nidhi Kunal Jha 1 , Kamal Shah 2 1 Student, M.E(IT), Thakur College Of Engineering And Technology, India 2 Vice Principal, Thakur College Of Engineering And Technology, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - The agriculture industry is a significant sector in farming, and it is possible to automate plant processes based on diseases. In order to monitor the agricultural environment effectively, it is important to track both healthy and diseased plant leaves. This will help to separate them and generate higher crop yields and returns. Modern technologies such as machine learning, deep learning, and artificial intelligence have been used to classify healthy and diseased plants using image classification techniques. Transfer learning based models are continuously evolving to identify the presence of disease in plant leaves accurately, adding efficiency to the detection process and increasing the chances of identifying diseases at the right stage. The author recommends the use of Convolutional Neural Network, ResNet-50, Efficient-B2, and VGG-16 to detect and validate the presence of plant diseases in leaves. The dataset used in this paper includes 87,000 plant images from Kaggle repository, consisting of healthy and diseased plant images from 38 different categories. However, the final implementation of the models is tested on 250 healthy and 250 diseased plant images. The dataset is trained, tested, and validated using performance metrics such as accuracy and recall factors. Efficient-B2 was found to be the most accurate model, generating an accuracy of 94%. Key Words: CNN, Efficient-B2, machine learning, deep learning, ResNet-50, VGG-16 1.INTRODUCTION The agricultural sector has always been the primary source and origin of food and serves the purpose of providing basic necessities for humans. Therefore, it has been recognized as the survival center of the world responsible for human lives [1]. As a result, the agricultural sector can be declared as the most important and central pillar of any economy. About 70% of the world's population depends on this sector for their livelihood, so the lives and health of individuals are a reflection of the agricultural sector [2]. Hence, this sector must be given due attention and not neglected. The forests and plants that they produce are an important aspect of the agricultural sector. The quality of such plants must be checked and monitored regularly to avoid decay. Detecting the presence of diseases in plants on time becomes a significant challenge in the agricultural sector to maintain the health of the plants and crops. Diseases in plants may occur due to various factors, such as improper or infertile land, inadequate water and sunlight, or an excessive number of pesticides [3]. All such factors are responsible for affecting the growth of the plant and creating a hurdle in its development and seedling growth, leading to diseases in plant growth. When a disease occurs in a plant, its growth is significantly impacted, and it may result in morphological and biological changes. The overall diseases in plants that cause such changes are mainly caused due to biotic and abiotic stress. Biotic stress is caused by living creatures in the soil, such as bacteria and viruses, that come in direct contact with the plant and negatively affect its growth [4]. On the other hand, abiotic stress is caused by non-living creatures, such as man-made or environmental factors [5]. Figure 1 below shows a diagrammatic representation of biotic and abiotic stress. Figure 1: Schematic representation of diseases in plant[5] The traditional method used by farmers to detect diseases in plants involves manual inspection, which is a time-consuming process due to the large fields of crops. Therefore, it is feasible to use machine learning techniques such as deep learning, transfer learning, and artificial intelligence for more precise and efficient detection. These algorithms can focus on specific features of the plant leaf, such as its saturation color, gradient orientation, and RGB features, to classify the plant leaf as healthy or diseased. The proposed research paper aims to automate the disease detection process using CNN and deep learning models like Efficient-B2, ResNet-50, and VGG-16. The study involves collecting a dataset of 250 images of healthy and diseased plant leaves from Kaggle repository and comparing the results obtained from the different