Early Predictive Model for Detection of Plant Leaf Diseases Using MobileNetV2 Architecture - T. R., Mahesh; Kumar V., Vinoth; Sivakami, R.; Manimozhi, I.; Krishnamoorthy, N.; Swapna, B. (International Journal of Intelligent Systems and Applications in Engineering, 2023)
In farming, it is vital to recognize diseases of plant leaves and to improve the prediction quality of diseased plant leaves. Several laboratory-based techniques such as polymerase reaction decrease in agricultural output, and pesticide application, have really been identified for recognizing different diseases of plant leaves with human sight. Agriculture yield is improving every day as a result of current technological advancements. However, they are very time-intensive and costly for farmers. Deep Learning (DL) methods may help boost crop yields by identifying recently upgraded methodologies and diverse systematic patterns. To improve the reliability of the measurements, researchers focused on new methodologies in deep learning algorithms for diagnosing leaf diseases. Every model is essential and focuses on the path of deep learning applications as well as the challenges faced by farmers. The mobilenetv2 architecture is used in this study to determine how to diagnose diseases that affect leaves. This design is built on an inverted residual structure, with narrow bottleneck layers serving as the input and output of the residual block and extended representations as the inputs. Lightweight depth-wise convolutions are used in this architecture to select leaf characteristics in the intermediate expansion stage. This network has 53 layers in the very beginning. There are 32 filters in the first completely convolution layer, followed by 19 more bottleneck layers. To retain representational strength, non-linearities were eliminated in the narrow layers. The proposed method enables the decoupling of the input/output leaves from the transformation’s expressiveness, offering a framework for additional study. On Imagenet classification, COCO object detection, and VOC picture segmentation, the performance is evaluated. The proposed model provides an accuracy of 95% in identifying the plant diseases.