Precision Agriculture is a concept of farm management which makes use of IoT and networking concepts to improve the crop. Plant diseases are one of the underlying causes in the decrease in the number of quantity and quality of the farming crops. Recognition of diseases from the plant images is an active research topic which makes use of machine learning (ML) approaches. A novel deep neural network (DNN) classification model is proposed for the identification of paddy leaf disease using plant image data. Classification errors were minimized by optimizing weights and biases in the DNN model using a crow search algorithm (CSA) during both the standard pre-training and fine-tuning processes. This DNN-CSA architecture enables the use of simplistic statistical learning techniques with a decreased computational workload, ensuring high classification accuracy. Paddy leaf images were first preprocessed, and the areas indicative of disease were initially extracted using a
The early identification of plant disease indicators is of significant agricultural benefit. However, this task remains challenging due to a lack of embedded computer vision techniques designed for agricultural applications. Agricultural yields are also subject to multiple challenges, such as insufficient water and plant disease [
These issues have been addressed in recent years through the application of accurate and robust disease detection systems based on machine learning (ML). For example, Lu et al. [
Nidhis et al. [
This study proposes a novel DNNfor paddy leaf disease classification. The error was minimized by optimizing weights and biases using a crow search algorithm (CSA), a metaheuristic search technique that mimics the behavior of crows. Optimization was conducted during both the standard pre-training and fine-tuning steps, to establish a DNN-CSA architecture that enables the use of simplistic statistical learning, thereby decreasing computational workload and ensuring high classification accuracy. Paddy leaf images were first preprocessed and areas indicative of disease were extracted using a
The workflow for the proposed rice plant disease identification and classification technique is illustrated in
In real-time applications, photographs of rice plant leaves are collected using a high-resolution digital camera. A dataset containing images of both normal and diseased leaves was used in the analysis process [
Images in the dataset were scaled to a uniform size of
Diseased regions in the background-eliminated HSV images were detected as clusters using a
Colors were also used to define shape and texture. The mean values of color, shape, and texture features were determined from the non-zero pixels in the RGB images, which represented the non-background regions of an image.
Diseased regions were defined using 14 colors. Initially, the color extraction process filtered regions of the RGB images that included diseased areas. The mean2 function in MATLAB was then applied after completion of the feature extraction process. The mean values of HSV components in the images were then identified. Finally, the std2 function in MATLAB was applied to the RGB color components.
Shape features extracted from binary images acquired during preprocessing were based on irregularly shaped diseased regions (blobs) in the images. These blobs were generally used to detect image areas that represented different objects. Determining the number of diseased sections is therefore a critical component of the blob prediction approach.
A GLCM was used to extract texture features from images. The GLCM records the number of times a pixel with a given gray level
The DNN architecture is composed of an input layer consisting of
In the pre-training step, the deep belief network (DBN) is applied to the input layer of the DNN, which is subsequently forwarded through hidden layers to the output layer, thereby assigning parameters for the activation functions employed in the individual nodes of the network. The assignment of activation function parameters was further performed by a restricted Boltzmann machine (RBM) using the following procedure. The elements of
Here,
Here,
where
The fine-tuning of network weights was conducted using back propagation (BP), which was applied using the network weights acquired from the pre-training phase. The improved network weights were obtained in the training phase using the training data once a minimum error rate was achieved.
Crows often monitor locations where neighboring birds hide food and will often steal food when competitors leave the immediate area. In addition, crows make use of this information to identify pilfering behavior in other birds and devise safeguarding measures to conceal food by varying hiding locations [ Crows move in a flock comprised of Crows remember their food concealment locations; Crows follow each other in the execution of a theft.
The CSA involves a metaheuristic search conducted in a
Suppose crow
State 1: Crow
where
State 2: Crow
States 1 and 2 are determined by the level of awareness
Here,
Decision variables are evaluated by introducing the locations stored in memory into the objective function. These locations are defined for each of the
Individual steps in the proposed DNN-CSA module are provided in Algorithm 1. This approach was developed to maximize classification accuracy by reducing the error rate.
An open-source database of rice plant leaves is not presently available. As such, we collected a total of 120 photographs of normal and diseased rice plant leaves using a NIKON D90 digital SLR camera with image dimensions of
Examples of initial RGB images and segmented diseased regions are provided in
Rice disease classification results produced by the proposed DNN-CSA module were compared with those of an SVM algorithm during the training and testing phases. Two different multiple cross-fold validations (accuracy and precision) were used as recall metrics. Accuracy is defined as the number of correctly classified samples divided by the total number of samples:
Precision is defined as the ratio of correctly identified positive cases to all predicted positive cases:
Recall compares correctly identified positive cases to the actual number of positive cases:
In these expressions, TP denotes true positives, TN is true negatives, FP is false positives, and FN is false negatives.
The results shown in
Methods | Accuracy | Precision | Recall |
---|---|---|---|
DNN-CS | 96.96 | 95.92 | 96.41 |
Training Phase-SVM | 93.33 | 92.46 | 91.74 |
Testing Phase-SVM | 73.33 | 73.10 | 72.40 |
5-Fold-SVM | 83.80 | 82.50 | 81.20 |
10-Fold-SVM | 88.57 | 87.46 | 88.27 |
In fact, SVM accuracy remained inferior to that of the proposed model, even after applying cross-fold validation. The accuracy, precision, and recall values produced by the proposed algorithm were respectively 9.5%, 9.7%, and 9.2% higher than those of the 10-fold SVM.
This study addressed a lack of embedded computer vision techniques suitable for agricultural applications by proposing a novelDNN classification model for the identification of paddy leaf diseases in image data. Classification errors were minimized by optimizing weights and biases in the DNN model using the CSA, which was performed during both the standard pre-training and fine-tuning processes to establish a novel DNN-CSA architecture. This approach facilitates the use of simplistic statistical learning techniques together with a decreased computational workload to ensure both high efficiency and high classification accuracy. The performance of the proposed DNN-CSA for the identification of paddy leaf diseases was compared experimentally with that of an SVM algorithm under multiple cross-fold validation. In the experiments, the DNN-CSA achieved an accuracy of 96.96%, a precision of 95.92%, and a recall of 96.41%, each of which were more than 9% higher than that of a 10-fold validated SVM classifier. These results suggest the DNN-CSA could be applied in the future to assisting farmers in the detection and diagnosis of plant diseases in real time, using images collected in the field and loaded onto a remote device.