International Journal of Control and Automation Vol. 13, No. 3, (2020), pp. 293-305 293 ISSN: 2005-4297 IJCA Copyright ⓒ 2020 SERSC Performance Analysis Of Fine-Tuned Convolutional Neural Network Models For Plant Disease Classification Nilay Ganatra, Atul Patel Faculty of Computer Science and Applications, Charotar University of Science and Technology (CHARUSAT), Changa nilayganatra.mca@charusat.ac.in ,atulpatel.mca@charusat.ac.in Abstract Early identification and detection of plant leaf disease is an essential requirement for sustainable agriculture and optimum yield production. In the field of Artificial Intelligence, Deep Learning has emerged as an effective computing paradigm and shows a great potential to solve many computer vision problems. Deep convolutional neural network (CNN) is one of the deep leaning architecture that proposes implicit outcomes for image recognition and object detection applications. In this research, the benchmark deep CNN models are applied for plant leaf disease identification and classification. We have applied and evaluated performance of VGG 16, Inception V4 and ResNet 50 and ResNet 101. The dataset used during the study contains 38 classes and 87000 images. We have applied transfer learning for training the models and fine- tuned the pre-trained models used. After evaluating the performance, it has been found that ResNet 50 and ResNet 101 exhibit test accuracy 99.70% and 99.73% respectively, whereas Inception V4 achieved 98.36% and VGG16 reached to 81.63%. Thus, ResNet50 and ResNet101 have been appeared with promising results for plant leaf diseases identification and classification. Keywords: Convolutional Neural Network, Plant Leaf Disease Classification, Transfer Learning 1. Introduction The normal condition and growth of plant interrupts by various plant diseases. Plant diseases are one of major reason behind less production that turns to economic losses. For sustainable agriculture and optimum yield production, detection of diseases in plants is an essential requirement as it will increase the yield more than 60% of the total productivity. Food and Agriculture Organization (FAO) estimated that 20% to 40% of global food production is affected by the pests and diseases and created major hazard to food security [1]. Use of pesticides may protect plant from the disease or infection and thus retain yields. However usage of pesticides is environmentally harmful and negatively affects the biodiversity which includes air, water, birds, insects, soil and fishes under the water. It also creates risk for human health with acute and chronic effects. To limit the usage of unsafe substance, such as pesticides, a field’s phytosanitary conditions knowledge plays an important role. It helps farmer to carry out right practice in the affected area at required time. However, it requires an expertise to measure the healthiness of the field and it is time consuming process. Also, it is not a feasible solution to check the condition of the plant many times in a particular season on the farms with large geographical area. There are various ways to identify plant pathologies. However, most of the diseases generate some appearance on the visible scale which can be primarily examined by the trained experts. A phytopathology having good analytical skill can be able to identify the characteristics of disease symptoms [2]. However, it could become difficult even for the phytopathology when there is variation in the symptoms by disease affected plants. Computerized system, which can be able to identify the disease affected plant by its appearance basic symptoms, makes disease identification task more easy and disease can be identify more accurately. Recent technical advancement and availability of low cost devices for the image acquisition have made it possible to gather the large amount of images for application of image based diagnosis [3]. Although, digital image contains condensed information which is very difficult to process by computing device and it requires to perform further steps including pre-processing and segmentation in order to obtain various features like color and shape [4, 5]. However,