1. Introduction
Agriculture is a primary avenue of income for farmers, who cultivate numerous crops according to their needs and the soil’s ecosystem. However, there are several issues that producers must deal with, including plant ailments, the availability of water, and catastrophes such as storms [
1]. The impacts of such issues can be lessened through the use of certain functional features offered by deep learning techniques, allowing farmers to take timely preventative measures against plant illnesses, which may increase food yield and negate their need to consult specialists. The absence of soil nutrients is a major influencing factor for agricultural yield. While plant disease identification is one of the essential study areas in the field of crop science, the identification and categorization of plant diseases remain difficult tasks [
2].
Globally, rice is a critical staple food, serving as a primary source of nourishment. Rice contributes significantly to cultural heritage, economic development, and food security, with high levels of consumption and production in Asia. For the optimal growth of rice, the particular conditions required include fertile soil, warm temperatures, and ample water supply. Due to its adaptability to diverse environments, rice cultivation is highly valued. It requires temperatures in the range of 20–37 °C and an abundant supply of water, with crops particularly thriving in warm climates. However, the quality and yield of rice are severely impacted by nutrient deficiencies and various diseases. The diseases which commonly affect rice include Sheath Rot, Bacterial Leaf Blight, Narrow Brown Leaf Spot, Brown Spot, and Leaf Smut, while common nutrient deficiencies include potassium, phosphorus, and nitrogen deficiencies [
3].
The identification of pests that affect plants is crucial to prevent reductions in productivity and agricultural yield [
4], in addition to plant wellness tracking and identifying illnesses that are detrimental to environmentally friendly farming. Higher levels of macro-elements providing an optimal nutritional balance are required in paddy crops, with important macro-elements including sulfur, oxygen, hydrogen, magnesium, and carbon; furthermore, minimum quantities of micro-elements—namely, chlorine, molybdenum, manganese, zinc, and copper—are also required for paddy crops. The deficiency of nutrients impacts the yield, regardless of whether it is micro- or macro-nutrients. The major nutrient deficiencies in rice plant leaves are those of nitrogen, manganese, iron, potassium, phosphorous, boron, calcium, and copper [
5].
According to new findings on the identification of plant-borne illnesses, they can be identified through characteristics that are readily apparent during the growth of plants, including viral plant infections [
6], which are tougher to identify through surveillance procedures. The traditional process requires a great deal of labor, longer waiting periods, and expertise in the identification of plant diseases. However, the application of artificial intelligence-based image processing approaches may help to increase productivity. In India, 19.9% of the nation’s economy relies on the farming industries, and rice is one of the main crops consumed. Infections have an impact on the development and overall performance of grain crops, which can decrease the profitability of farming. A lack of training and expertise may make it harder for landowners to detect the various illnesses that can affect a single rice harvest, and the use of an automated information processing platform can provide critical support for the accurate and timely assessment of botanical illnesses [
7].
It is feasible to promote agricultural production to obtain robust and fruitful vegetation across the globe. Grain serves as an essential component for the livelihoods of many people and, so, the productivity and quality of grain crops should be increased. Moreover, grain must be the first ingredient in locally produced products, as an optimal supplier of nutrition. With globalization, the production of paddy crops has increased over recent centuries. To continue this production, it is necessary to check the paddy leaves often, from the starting stage itself, in order to prevent deficiencies [
8,
9,
10,
11]. If a deficiency occurs during the initial stage, it weakens the crop and mitigates the production. The expertise of cultivation specialists has long been the cornerstone of disease assessments; however, when non-experts aim to diagnose rice ailments through the use of commonly available portable applications, there is a greater chance of error, and the method may be less accurate when using low-quality images [
12].
To identify nutritional deficiency in rice plants, Sharma et al. [
13] used averaged ensemble transfer learning (AETL). The performance of predictive models has been improved to a great extent through utilization of the potential of ensemble transfer learning models. For the rice plant deficiency diagnosis task, various ensemble models that comprise VGG19, InceptionResNetV2, DenseNet201, Xception, ResNet152V2, and InceptionV3 have been used, achieving accuracies of 90% and 92% on the Kaggle and Mendeley datasets, respectively. Anami et al. [
14] described a DCNN structure utilizing field photos to automatically identify and categorize different biological and physical rice plant stressors. Eleven kinds of environmental and abiotic difficulties from five distinct grain species were addressed using the proposed strategy. Considering the categorization task, the best-performing artificial intelligence framework was employed. The models that were trained obtained a median reliability of 92.89%, proving that such models are technically feasible. However, the task being performed is difficult, due to the high degree of inconsistency characterizing natural settings.
Krishnamoorthy et al. [
15] evaluated a convolutional neural network algorithm (CNN) to diagnose illnesses using photos of rice leaves and concluded that the automated detection of pathogenic micro-organisms in the foliage of grain crops has become possible due to the significant advancements in technologies for farming. The proposed model was used to translate the information into ratings that are used in the feature collection procedure. The authors stated that, while the identification of distinct categories of crop lesions achieved greater precision, the model remains inadequate to classify additional leaf-related illnesses. Sun et al. [
16] developed a CNN to identify grain leaf infections. The preprocessing of information was mostly carried out to increase the detection rate and lessen the impact of intense illumination on the recognition performance. To achieve optimal outcomes, feature combination was employed to further enhance the precision of identification. The identification performance when using the suggested technique was found to be higher. In addition, an optimization system was introduced to increase the effectiveness of identification. However, the low number of features from the network interface led to unsatisfactory results.
Sethy et al. [
17] implemented a deep CNN to develop a method for high-accuracy diagnosis of rice diseases. The multi-level structure, which constitutes an Information Corporation, provides better responses at every tier. After retrieving the essential visual features, the removed stages forward it to the next generation. Sathyavani et al. [
18] proposed DenseNet-BC to categorize nutrient deficiencies of rice crops. Appropriate classifier training using leaf texture images was analyzed. For data acquisition, IoT was utilized to enable the acquisition of images for the classification model. The classification task was completed using DenseNet-BC. After several iterations, the model was trained prior to classification. Compared to existing deep learning methodologies, the F-measure and accuracy of classification were enhanced by the proposed method, providing good simulation results.
Alfred et al. [
19] highlighted machine learning approaches for intricate farming tasks involving field-grown rice. In particular, the simulation results could be significantly enhanced through analyzing the complex relationships between information obtained at different points. These improvements should be effectively and rapidly assimilated into relevant methods, as it is challenging to follow the growth of grains from cultivated land. Wang et al. [
20] presented an attention-oriented depth-wise separable neural network which recognizes and classifies rice illnesses from images of leaf surfaces. It was shown to have improved productivity and carried out the procedure effectively. As a result, it is critical to diagnose rice illnesses accurately and quickly.
Aggarwal et al. [
21] presented a pre-trained DL model for rice leaf disease classification, which achieved accuracy and recall values of 0.93 and 0.89, respectively. Latif et al. [
22] presented a deep CNN model for rice leaf disease classification, which achieved an accuracy value of 96.08%. Trinh et al. [
23] presented a rice leaf disease classification model using data gathered at the Vietnam National University of Agriculture. In particular, the modified version of YOLOv8 was considered, and the normal loss function was replaced with the enhanced intersection of union, which achieved an accuracy of 89.9%. Simhadri et al. [
24] developed different pre-trained models for rice leaf disease classification. Among all their pre-trained models, the InceptionV3 model achieved better recall and specificity values (98.23% and 98.21%, respectively).
From the literature survey, it was observed that researchers have used different classification methods and algorithms to detect leaf diseases and nutrient deficiencies in the rice paddy context. These models obtained accuracies in the range of 88–98%, including classification algorithms such as the SVM, DCNN, AETL, YOLOv8, CNN, Naïve Bayes, and Jaya algorithm; different pre-trained models; and different optimization algorithms. They have accomplished rapid and non-invasive nutrient and disease classification, but it remains quite challenging to obtain more reliable and accurate results based on image processing frameworks. Therefore, this study presents an Improved Tunicate Swarm Optimization (ITSO)-based Hybrid Convolutional Neural Network (HCNN) to effectively classify paddy leaf diseases and nutrient deficiencies. The proposed ITSO algorithm enhances the traditional Tunicate Swarm Optimization method through integrating novel search strategies, which improves the optimization process for better convergence and accuracy. The hybrid CNN leverages the power of convolutional layers for feature extraction and classification of critical regions in the leaf images. Extensive experiments on a comprehensive dataset demonstrate that the ITSO-based HCNN [
25] significantly outperforms existing methods in terms of classification accuracy and robustness. This advanced approach provides a reliable tool for farmers and agricultural experts to diagnose rice leaf health issues promptly, allowing for timely and appropriate interventions.
2. Materials and Methods
The present study investigates the detection of leaf diseases and nutrient deficiencies in rice plants using the proposed HCNN-ITSO algorithm in order to increase yield and productivity. Initially, affected rice leaf images were collected from field work and Kaggle datasets, following which the collected images underwent augmentation. The augmented results were fed to the FCM with ITSO for the segmentation of lesions. Finally, the proposed HCNN-ITSO was used for the classification of rice leaf diseases and nutrient deficiencies in rice leaf images. The flowchart of the proposed research work is outlined in
Figure 1.
2.1. Dataset Collection and Augmentation of Paddy Leaf Images
In the proposed approach, 365 rice leaf images (field work data) were utilized, which were captured using a Canon Power Shot G2 digital camera. The captured images were split into 282 images for training and 83 images for testing (i.e., 80% for training and 20% for testing). Image augmentation techniques were applied to extend the images, including shearing, random zooming, vertical and horizontal flipping, and rotation operations. This resulted in an increased number of images; more specifically, 1926 training images and 447 testing images [
26]. The nutrients dataset was gathered from the Kaggle website, which includes 1157 images of rice leaves. Of these, 440 images are nitrogen deficiency images, 334 images are phosphorous deficiency images, and 383 images are potassium deficiency images [
27]. For identification, the dark brown older leaves indicate phosphorous deficiency, irregular necrotic spotting indicates potassium deficiency, and a pale yellow color indicates nitrogen deficiency. Similarly to above, 8072 images were obtained through the augmentation process. Of these, 3081 were nitrogen deficiency images, 2330 were phosphorous images, and 2681 were potassium deficiency images.
2.2. Segmentation of Augmented Images
The augmented images were fed into the FCM-ITSO for segmentation of the lesions. The FCM calculates the membership function of data points to identify the centers of clusters through optimizing a fitness function. This process determines the class of the
, achieving the goal of paddy classification. The fundamental definition can be expressed as follows:
where
denotes the clusters,
is the distance between
and
,
n is the fuzzified term, and
m is the total number of data points. The process of the FCM is explained in the following stages [
28]:
Stage 1: Initialize the values of , and .
Stage 2: Initialize the fuzzy partition matrix .
Stage 3: Update
and
using the following two expressions:
Stage 4: When the satisfied criteria are met, stop the process. Then, to select the centers of clusters in an optimal manner, the ITSO algorithm is used.
2.3. Improved Tunicate Swarm Optimization
In this study, TSO and BFO are integrated to enhance the segmentation performance [
29]. While traditional TSO and BFO provide better outcomes, these two optimizers are affected by issues such as poor exploration and exploitation ability. To tackle these issues, this work presents ITSO for the selection of optimal cluster centers.
The foraging behavior of BFO is based on the behavior of E. coli. The numerical expressions for all four stages are elucidated below.
Chemotaxis: The strategy of bacterial foraging is accomplished in this stage, and the bacteria stay in one place for a long time prior to changing their direction. Then, the change of direction takes place in one step. If the required nutrients are available in the observed position, they will swim in the same direction [
30]. If the location of the bacterium is
l, the
ith bacterium with chemotaxis
j, reproduction
m, and elimination dispersal
n is denoted as
li (
j,m,n). The movement of the bacteria is calculated as
where Δ(
i) is a random vector with a value ranging between 1 and R. The total number of bacteria is R, and each element Δ
k (
i),
k = 1, 2,….
y lies between −1 and 1. Updating the location of the bacteria in this stage can be defined as
The movement step size while swimming is
E(
i).
Tunicate swarming: The simulation of inter-cell characteristics; for example, a bacterium may release chemical substances to other bacteria if it attains the satiated stage. The tunicate swarming behavior is included to enhance the performance of BFO, thereby offering good convergence speed. Depending on the search agents
Rp(
y), the current agent’s position
Rp(
y + 1) is updated to represent the tunicate swarming behavior [
31].
The optimization domain for the bacteria is taken as
l = [
l1,…,
lp], where the location of the
hth component of the
ith bacterium is
lih. The cell-to-cell communication in the chemotaxis phase is denotes as
CC(
l, li(
j,m,n)), and the attraction and repulsion coefficients include
VAT,
fAT,
gRE, and
VRE. The swarming effect can be defined as
Reproduction: After completing the chemotactic phases, the reproduction step takes place. Considering
R as a positive number [
32], the population of bacteria with adequate nutrients is denoted as
Rs, which generate offspring without mutations. This is interpreted as
The total cost is used to determine the health of the bacteria, where a higher value represents lower nutrients, causing the bacteria to be unhealthy and not reproduce. Based on the health status, the bacteria are arranged in a reverse manner. During this stage, the weak bacteria might be removed and reach the death stage, while the healthy bacteria are divided into two new bacteria and can be positioned in similar positions.
Neglected Dispersal: An increase in temperature might kill a wide range of bacteria, which occurs with the probability PEL-DE. Meanwhile, new bacteria will be produced from the other living bacteria as a replacement.
2.4. Feature Extraction and Classification of Rice Leaf Images
This section proposes the HCNN with ITSO for the classification of rice leaf diseases with respect to nutrient deficiencies.
HCNN: The input augmented images are fed to the convolutional layers. This work utilizes a hybrid CNN model characterized by convolutional filter series and non-linearity. It is the combination of a hierarchy of three CNN structures, namely, the CNN1, CNN2, and CNN3 structures.
The major elements in the hybrid CNN are convolutional, ReLU, and pooling layers.
Figure 2 shows the structural diagram of HCNN [
33], where 4 × 4 and 3 × 3 denote the filter sizes of the convolutional layers. The mathematical operation applies the convolution with the help of filter sets. The feature map is accomplished through applying a filter to the input matrix. Based on element-wise multiplication, the kernel is calculated and the dot products between the input matrix and the single scalar values are summed up. From the input image
Y, we derive the following layers as
Yk.
The operator
Yk is non-linear, and the convolution in
HCNN is carried out using
Mk. Here, [1 +
exp(−
Y)]
−1 and
max(
y;0) are the exponential sigmoid and rectifier, denoted as
δ. Assume that
Mk is the stack of convolutional filters. The entire convolution layer defines each layer’s convolution:
Employing the process of convolution, we have
The issues related to non-convexity are solved through the use of the optimized HCNN architecture. The stochastic gradient descent trains the weights
Mk, and back-propagation is used to determine the gradients.
HCNN with ITSO: This section details the use of the ITSO algorithm to tune the hyper-parameters of the HCNN, such as its weights, biases, and layers, to improve the classification of rice leaf diseases and nutrient deficiencies (e.g., BLB, SR, NBLS, BS, LS, and PPN). The HCNN was separately trained on the field work and Kaggle datasets, and its bias and weight parameters were learned automatically, which were further optimized. Furthermore, the classification accuracy of the HCNN in each training set was enhanced through the use of the ITSO algorithm. The following steps explain the rice leaf classification process using the HCNN-ITSO model. Algorithm 1 provides the details of the HCNN-ITSO for classification of rice leaf images.
Algorithm 1: HCNN-ITSO for disease and nutrient deficiency classification |
Start Initialize the input rice leaf images, ITSO parameters, and the number of iterations While(To meet termination condition) For (Neglect dispersal, reproduction, and chemotaxis loop) Evaluate the fitness function Use Equation (5) for bacterium location updating Use Equation (6) to update the tunicate swarming behavior End For(Chemotaxis loop) Evaluate diseases and nutrient deficiency in rice leaf Use these rice leaf data to sort the bacteria Best bacterium is sorted End For(Reproduction) Generate offspring without mutations Fine-tune CNN1, CNN2, and CNN3 End For(Neglect dispersal) End While Obtain best classification output based on nutrient deficiency and rice leaf diseases End |
2.5. Implementation of Augmented and Segmented Process
The abovementioned augmentation and segmentation processes were implemented in order to demonstrate the classification of diseases and nutrient deficiencies in rice leaves. The simulations were conducted using the MATLAB R2024 platform and the field work dataset, as well as the Kaggle dataset known as Nutrient-Deficiency-Symptoms-in Rice [
34]. The field work dataset was collected in the fields of Agricultural Research Station Tirupathisaram, Tamil Nadu. Sample images for rice disease classification from the dataset collected from the paddy field are shown in
Figure 3.
Figure 3a presents different input images showing rice leaf diseases such as Bacterial Leaf Blight (BLB), Brown Spot (BS), Leaf Smut (LS), Narrow Brown Leaf Spot (NBLS), and Sheath Rot (SR).
Figure 3b presents the augmented images, which were subjected to shearing, random zooming, vertical flipping, horizontal flipping, and rotation.
Figure 3c presents the segmentation of images using the FCM-ITSO technique. Similarly,
Figure 4 illustrates sample rice leaf nutrition deficiency images from the Kaggle dataset.
Figure 4a presents input images indicating nutrient deficiencies of potassium, phosphorous, and nitrogen, while
Figure 4b,c provide the associated augmented and segmented images, respectively.
2.6. Performance Measures
The performance of the proposed approach over the dataset that was captured in the field was analyzed using the metric presented in this section. For the analysis, we used statistical factors such as the F-measure, accuracy, kappa, precision, sensitivity (recall), and specificity. This dataset was used to classify rice leaf diseases and nutrient deficiencies. Our proposed ITSO-based HCNN algorithm was found to provide impactful classification outcomes. The statistical factors are explained below:
where Γ denotes the number of exact positive detections, in which the detected and actual values match each other. Sensitivity is also known as Recall.
where
η is used to detected healthy cases; in this case, the detected and actual values are healthy.
where
δ is used to determine when a nutrient deficiency or disease sample is detected as healthy.
where
denotes the case in which the detected value is unhealthy and actual value is healthy.
where
FM is the F-measure value, which balances the recall and precision values.
4. Discussion
The present study introduces a novel approach for the segmentation and classification of lesions in rice leaves in order to identify nutritional deficiencies using FCM clustering combined with the ITSO algorithm. Furthermore, the deep learning model employed in this study, the HCNN, is optimized using the ITSO metaheuristic algorithm. This integration enables the model to effectively classify rice leaf diseases and identify nutrient-deficient leaves. Two datasets were used for validation: a field work dataset and a Kaggle dataset. The HCNN-ITSO model demonstrated remarkable classification capabilities across various rice leaf diseases. Furthermore, the model successfully classified leaves with potassium, phosphorus, and nitrogen deficiencies in the Kaggle dataset. The experimental analysis was conducted employing both the field work and Kaggle datasets. Various performance measures were considered in order to evaluate the proposed method’s effectiveness. The accuracy obtained by the proposed method significantly outperformed those of existing methods, underscoring the efficacy of the proposed approach in both disease and nutrient deficiency classification tasks.
Utilizing both the field work and Kaggle datasets helped to ensure that the proposed method is validated on diverse data sources, enhancing its generalizability and robustness.
Table 2 presents the comparative analysis with the recent works of Sharma et al. [
13], Anami et al. [
14], Krishnamoorthy et al. [
15], Sun et al. [
16], Sethy et al. [
17], Sathyavani et al. [
18], and Alfred et al. [
19]. It can be observed, from the comparison, that the proposed model obtained a better accuracy of 99.01%, precision of 99%, recall of 98.9%, and F1-score of 99.3%. The use of HCNN optimized with ITSO effectively leverages the power of deep learning for complex classification tasks, enabling the precise identification of both leaf diseases and nutrient deficiencies.
5. Conclusions
This study utilized the novel approach of an Improved-Tunicate-Swarm-Optimization-based Hybrid Convolutional Neural Network (HCNN-ITSO) algorithm for disease and nutrient deficiency classification in rice leaves. The novel HCNN-ITSO method classified both diseases and nutrient deficiencies effectively in images from two datasets (a field work dataset and a Kaggle dataset). The dataset images were first augmented and then fed into the proposed HCNN-ITSO for classification. The proposed method was found to effectively classify leaf diseases including Bacterial Leaf Blight (BLB), Sheath Rot (SR), Brown Spot (BS), Narrow Brown Leaf Spot (NBLS), and Leaf Smut (LS), as well as successfully classifying the deficiency of nutrients such as potassium, phosphorous, and nitrogen from the rice leaf images. Compared to traditional methods, different rice nutrient deficiencies and leaf diseases can be recognized and categorized, thereby providing more flexibility and accuracy. Rigorous testing on diverse datasets validated the efficiency of the proposed model. The platform used for the simulation was MATLAB, and the obtained results indicated that the proposed HCNN-ITSO algorithm outperforms all existing methods used in the comparison, with accuracies of 98.8% and 99.01% on the field work and Kaggle datasets, respectively. For Dataset 1 (reflecting leaf diseases), the proposed approach obtained 98.8% accuracy, 98.5% kappa, 98.7% recall, 99.5% specificity, and 99% F-score. For Dataset 2 (reflecting nutrient deficiencies), the proposed approach obtained 99.01% accuracy, 98.3% kappa, 98.9% recall, 99.3% specificity, and 99.5% F-score. In particular, these results are superior to those reported in existing works. The proposed HCNN-ITSO achieved better outcomes through the integration of a deep learning model (HCNN) with a metaheuristic algorithm (ITSO). In the future, various agricultural applications should be tested and adapted using a hybrid optimization model, potentially enabling the effective and versatile determination of a range of crop parameters.