Deep learning based assessment of disease severity for early blight in tomato crop
- PDF / 4,014,688 Bytes
- 12 Pages / 439.37 x 666.142 pts Page_size
- 6 Downloads / 155 Views
Deep learning based assessment of disease severity for early blight in tomato crop Maheswari Prabhakar 1 & Raja Purushothaman 1 & Durga Prasad Awasthi 2 Received: 27 June 2019 / Revised: 21 July 2020 / Accepted: 28 July 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Assessment of disease severity is one of the major challenges which helps in the prediction of yield quantitatively and to decide the control factors that improve the yield of any crop. Hence a perfect system is essential to measure the severity level of the disease in order to improve its productivity. An intelligent state of the art technique i.e., deep learning plays an inevitable role in most of the real-time applications including smart farming. Tomato crops are frequently affected by a dangerous fungal disease i.e., early blight, resulting in 100% production loss to farmers. In this work, an identification of early blight disease in tomato leaves is performed by a recently invented paper microscope named Foldscope. Further, a deep Residual Network101 (ResNet101) of Convolutional Neural Network (CNN) architecture is used to measure the severity level of early blight disease in tomato leaves. The dataset in the model is trained by using an open database i.e., PlantVillage dataset for mild, moderate, and severely diseased leaves along with healthy tomato leaves. The results of ResNet101 architecture is compared with other pre-trained CNN such as Visual Geometry Group16 (VGG16), VGG19, GoogLeNet, AlexNet, and ResNet50. Among these networks, the deep ResNet101 architecture has achieved the highest accuracy of 94.6%. Finally, a case study has been conducted based on the estimated severity levels and the required fungicide treatment is also prescribed. Keywords Precision agriculture . Deep learning . CNN . ResNet101 . Foldscope . Fungicide
1 Introduction Globally crop yields are affected due to the incidence of diseases. The yield loss resulting from a disease can be correlated to the severity level of the specific disease. In early methods of
* Raja Purushothaman [email protected]
1
School of Mechanical Engineering, SASTRA Deemed University, Thanjavur, Tamilnadu 613401, India
2
Department of Plant Pathology, College of Agriculture, Lembucherra, Tripura 799210, India
Multimedia Tools and Applications
disease severity measurement, accurate results could not be achieved due to the limitation of visual assessment by experts, as it is a complex and time-consuming process. Hence an intelligent system is required to measure the disease severity and further treatment based on the results. The manual task of traditional agricultural practices have been reduced due to the development of Precision Agriculture (PA). Advanced technologies are implemented in PA in order to improve the security, sustainable growth and production of crops [7]. In most of the real-time applications, Artificial Intelligence (AI) has achieved remarkable growth, with the simultaneous advancement of computational algorithms and processing p
Data Loading...
