PFDI: A Precise Fruit disease Identification Model based on Context Data Fusion with Faster-CNN in Edge Computing Environment

Abstract

Fruits have a significant impact on everyday living i.e., citrus fruits. Numerous fruits have a solid nutritious value and are packed with multivitamins and trace components. Citrus fruits are delicate, so they are susceptible to many diseases and infections. Many researchers have suggested various deep learning and machine learning based fruit disease detection and classification models. In this research we are presenting precise fruit disease identification (PFDI) model based on context data fusion with Faster-CNN in edge computing environment. The goal is to develop a precise, efficient, and trustable fruit disease detection model, which is a critical component of an autonomous food production in robotic edge platform. This research examines and explores four different diseases of citrus fruits using CNN deep learning models to be adopted as edge computing solution. Identification of citrus diseases namely cankers black spot, greening, scab, melanose, and healthy citrus fruits are implemented using the proposed sequential model without pruning, with pruning having different sparsity level followed by post quantization. Through transfer learning method, we optimize this model for the assignment of fruit disease detection employing visuals from two patterns: Near-infrared (NIFR) and RGB. For integrating multi-model (NIFR and RGB) facts, early and late data fusion techniques are evaluated. The accuracy obtained from the proposed model for the canker disease is 97%,scab 95%, melanose 99% ,Greening 97%,Black spot 97% and for healthy 97%. In this paper we compared and evaluated the results of proposed model with the sparsity levels of 50–80%, 60–90%, 70–90%, 80–90% pruning and also obtained the results of post-quantization on each level. The results show that the size of the model with 60–90% pruning can be counteracted to the 47.64 of the baseline model without significant loss of accuracy. Moreover, post quantization can further reduces the of 60–90% pruning from 28.16 to 8.72. In addition to enhanced precision, the above initiative is much faster to implement for new fruits diseases because it needs bounding box annotation (BBA) instead of pixel-level annotation (PLA).

State-of-the-art non-destructive approaches for maturity index determination in fruits and vegetables: principles, applications, and future directions

“…In a recent study by Dhiman et al (2023), a precise fruit disease identification model, known as "PFDI," was developed, leveraging context data fusion techniques within an edge computing environment. The primary objective of this research is to create an accurate, efficient, and dependable model for the detection of fruit diseases, which is a vital component of autonomous food production on a robotic edge platform.…”

“…Future research directions may involve expanding the dataset to encompass a broader range of fruit species and disease manifestations, fine-tuning the model's architecture, and optimizing edge computing resources to enhance overall efficiency and applicability. The PFDI model represents a significant advancement in fruit disease identification, offering precise and real-time insights that have the potential to revolutionize disease management practices in agriculture (Dhiman et al 2023).…”

Diagnosis of Rice Diseases using Canny Edge K-means Clustering and Convolutional Neural Network based Transfer Learning

“…This operation can lead to mitigate overfitting of the model by expeditating the model convergence. In the present work, seven data augmentation functions operations, such as shearing, zca whitening, random rotation, horizontal flipping, height shift and width shift, brightness and vertical flipping are used on the image samples [19]. Figure 2 represented image samples of rice plants after applying the data augmentation techniques.…”