Abstract
We find Apple Leaf Disease Identification Based On Optimized Deep Neural Network. The primary idea is that leaf disease symptoms appear in the leaf area whereas the background region contains no useful information regarding leaf diseases. To realize this idea, two subnetworks are first designed.
One is for the division of the input image into three areas: background, leaf area, and spot area indicating the leaf diseases, which is the region of interest (ROI), and the other is for the classification of leaf diseases. respectively; subsequently, they are trained separately through transfer learning with a new training set containing class information, according to the types of leaf diseases and the ground truth images where the background, leaf area, and spot area are separated.
Next, to connect these sub networks and subsequently train the connected whole network in an end-to-end manner, the predicted ROI feature map is stacked on the top of the input image through a fusion layer, and subsequently fed into the subnetwork used for the leaf disease identification.
The experimental results indicate that correct recognition accuracy can be increased using the predicted ROI feature map. It is also shown that the proposed method obtains better performance than the conventional state-of-the-art methods: transfer-learning-based methods, bilinear model, and multiscale-based deep feature extraction and pooling approach.
Introduction To Apple Leaf Disease Indentification Project
Information and communications technology has been applied into the existing farming practices to increase the quantity and quality of plants and crops.
Smart agriculture sensors including optical sensors, accelerometer, electrochemical sensors, and airflow sensors have been used to measure a leaf’s angle and colors , soil properties, pH, soil nutrient levels.
Continuous monitoring yields a vast amount of sensing data, from which a plant diseases and its growth condition can be evaluated through data analysis, thereby enabling an increase in yield while minimizing resources such as water and fertilizer.
The deep convolutional neural network (DCNN) introduced recently have demonstrated powerful performance for image classification and detection problems. Therefore, image-based approaches have been studied actively using mobile cameras or digital cameras built on autonomous agricultural vehicles for plant disease identification.
System Architecture
H/w and S/W requirements
Hardware:
Computer : System.
Ram : 1GB
Rom : 32GB
Software:
Technology : Machine Learning.
Front End : GUI-tkinter.
IDLE : python 3.10.4
Virtual Envs : Anaconda
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