Maize Leaf Disease Detection and Classification Using CNN

Maize, a vital global food crop, is susceptible to various diseases impacting agricultural yield and quality, necessitating effective monitoring strategies. This study presents a comprehensive approach utilizing advanced Convolutional Neural Network (CNN) architecture for efficient maize leaf disease detection and classification. Leveraging datasets from Kaggle, Harvard Dataverse, and Mendeley, the proposed CNN maize disease detection (MDD) system integrates optimization techniques, data augmentation, batch size adjustments, and fine-tuning strategies. Evaluation through data splitting reveals significant correlations among maize leaf classifications and the influence of data augmentation on image quality. Following training, the CNN-MDD model is deployed on a Microcontroller Unit (MCU) simulation, achieving promising optimization parameters: latency of 7.60 ms, RAM size of 726.60 KB, classifier flash size of 344.70 KB, and an accuracy of 94.60 %. These optimized parameters align well with MCU specifications, highlighting the potential for practical deployment on low-cost edge devices like Arduino Ble 33 Sense. Additionally, this research contributes to the creation of a Ghanaian maize crop pest and disease dataset and the development of an optimal and cost-effective CNN-MDD MCU edge device, aiming to provide local farmers in Ghana and Sub-Saharan Africa with a swift, offline solution for maize disease detection, thus fostering sustainable agricultural practices. We conduct a comparative analysis of Edge Impulse and TensorFlow, two prominent machine-learning platforms for TinyML model development and deployment in various applications, including smart crop disease detection. Utilizing a dataset comprising 12,344 raw images and 7,454 augmented images of healthy and diseased maize leaves, we evaluate the workflow, features, performance, and usability of each platform. Our findings indicate that Edge Impulse holds a slight advantage over TensorFlow in terms of ease of use, reliability, and memory footprint, whereas TensorFlow offers greater flexibility, customization options, and higher accuracy. We conclude that while both platforms are suitable for crop disease detection, a combined approach allows for leveraging TensorFlow's robust training capabilities for complex neural networks alongside the simplicity of Edge Impulse for deploying models on low-powered edge devices. This synergistic approach enhances the effectiveness and efficiency of crop disease detection systems, ultimately benefiting agricultural practices and food security.

Maize leaf diseases significantly reduce maize yield; therefore, monitoring and identifying the diseases during the growing season are crucial. Some of the current studies are based on images with simple backgrounds, and the realistic field settings are full of background noise, making this task challenging. We collected low-cost red, green, and blue (RGB) images from our experimental fields and public dataset, and they contain a total of four categories, namely, southern corn leaf blight (SCLB), gray leaf spot (GLS), southern corn rust (SR), and healthy (H). This article proposes a model different from convolutional neural networks (CNNs) based on transformer and self-attention. It represents visual information of local regions of images by tokens, calculates the correlation (called attention) of information between local regions with an attention mechanism, and finally integrates global information to make the classification. The results show that our model achieves the best performance compared to five mainstream CNNs at a meager computational cost, and the attention mechanism plays an extremely important role. The disease lesions information was effectively emphasized, and the background noise was suppressed. The proposed model is more suitable for fine-grained maize leaf disease identification in a complex background, and we demonstrated this idea from three perspectives, namely, theoretical, experimental, and visualization.

This study aims to develop an efficient and accurate deep learning-based model for the classification of plant leaf diseases using Convolutional Neural Networks (CNN). The objective is to automate disease detection in agricultural crops to assist farmers and agricultural experts in early and reliable diagnosis. The model is trained on the publicly available “Plant Village CLAHE Processed Data” dataset, which includes high-resolution RGB images of healthy and diseased plant leaves. Images are preprocessed through resizing (128×128), normalized, and split into training, validation, and test sets. Data augmentation techniques such as flipping, zooming, and rotation are used to improve generalization. A custom CNN architecture comprising convolutional, pooling, dense, and dropout layers is employed and trained using the Adam optimizer. Exploratory Data Analysis (EDA) ensures data quality and balance. The model achieves impressive results, with 93% test accuracy, 91% precision, 93% recall, and an F1-score of 92%, indicating robust performance in identifying diverse plant diseases. Training accuracy reached 94.64% with a validation accuracy of 92.95%, confirming minimal overfitting. These results validate the model’s reliability for practical use in smart farming solutions, especially in mobile or IoT-based applications for real-time disease monitoring and precision agriculture.

Underwater habitats are home to about 34,800 fish species. Understanding the various fish species and their therapeutic properties is crucial. The majority of bodies of water have a large number of fish. They can be found in almost all aquatic settings, from high alpine streams (like char and gudgeon) to the abyssal and even hadal depths of the deepest oceans, despite the fact that no species have yet been discovered in the bottom 25% of the ocean (such as cusk-eels and snailfish). In the research, a model for categorizing the various fish species was devised. To ascertain the fish’s possible health advantages, different fish species are categorized. The transfer learning is used to pretrain models with our data which is then given to convolutional neural networks (CNNs), visual geometry group (VGG), residual neural network (RESNET), and densely connected neural network (DENSENET) with a moderate amount of change in the output layer. Additionally, this dataset is input to a CNN model with five sequential convolutional layers that uses the “Adam optimizer,” “ReLU,” and the “SoftMax” activation function. Furthermore, convolutional neural network is used with sixteen layers and softmax activation at the bottom to create the VGG model. The transfer learning is used for RESNET and DENSENET by changing the last layers. The 121 thick layers of the DENSENET that has used were interconnected forward and used Adam optimizer. The 50-layer RESNET, which employs skip connections with ReLU and softmax activation functions. By using classic CNN, we obtain an accuracy of 98.11%; with the help of VGG, and able to achieve an accuracy of 99%; with RESNET, a 99.56% accuracy; and finally, DENSENET, a 98.78% accuracy. Potential health advantages of fish.

Food security continues to be a significant challenge the world over, with crop production becoming increasingly threatened by crop diseases and pest infestations. In the case of chili production, farmers often suffer significant yield loss and economic insecurity due to the unpredictable nature of both of these problems. Current pest control options (agrochemical and organic methods alike) have not reliably been enough for timely and effective control and demonstrate the importance of early and effective pest and disease identification processes. To solve this problem, the present work describes a deep learning implementation that leverages advanced YOLO-based architectures for the detection and classification of leaf diseases and pest on chili plant leaves. The present work trains and evaluates three models (YOLOv5, YOLOv7, and YOLOv8) on a newly created and balanced dataset of over 28,800 images combining 20 total classes of pest and leaf diseases. The dataset was supplemented by preprocessing the images and conducting an augmentation process to create a total of 32,000 images for training to generate reliable models. The results from the experiments found that YOLOv8 provided the best baseline performance of 95.1% mean Average Precision (mAP), while YOLOv5 had an mAP of 86.1%, and YOLOv7 had an mAP of 67.5%. An additional enhancement in the construction of a Modified YOLOv8 hybrid model—reflecting all advantages of YOLOv5, YOLOv7, and YOLOv8—achieved a highest mAP of 99.5% to be the most effective model in this study, the results suggest that the newly proposed Modified YOLOv8 framework, is highly accurate and reliable for the early detection of pests and diseases in chili, and is helpful to improve sustainable agricultural practices, mitigate crop losses, and increase global food security.

Anemia is a disorder that causes the body to have insufficient red blood cells or hemoglobin resulting in a decrease in the blood's ability to carry oxygen to the body's tissues. Elevated pulse, weariness, skin pallor, shortness of breath, lightheadedness, and dizziness are a few symptoms that may appear on the onset of Anemia. The underlying diagnosis affects the course of treatment. To treat an iron deficit patient, iron supplements are utilized. Low vitamin levels may be treated with vitamin B supplements. This study explains the use of the Convolutional Neural Networks (CNN) to categorize hemoglobin levels using patient palm photographs. Anemia and non-Anemia are classified according to the hemoglobin levels. ML-assisted illness diagnosis is more accurate, less time-consuming, and requires less additional labour than traditional methods of diagnosis. EfficientNetB0 model with softmax activation function and Adam optimizer achieved the best results, with an accuracy of 97.52% and an F1 score of 97%. The full family of EfficientNet models, from B0 to B7 performed well with both sigmoid and softmax activation functions and Adam optimizer, with results consistently exceeding 92%. In contrast, the same models with linear activation function underperformed.

There are several methods that have been discovered to improve the performance of Deep Learning (DL). Many of these methods reached the best performance of their models by tuning several parameters such as Transfer Learning, Data augmentation, Dropout, and Batch Normalization, while other selects the best optimizer and the best architecture for their model. This paper is mainly concerned with the optimization algorithms in DL. It proposes a modified version of Root Mean Squared Propagation (RMSProp) algorithm, called NRMSProp, to improve the speed of convergence, and to find the minimum of the loss function quicker than the original RMSProp optimizer. Moreover, NRMSProp takes the original algorithm, RMSProp, a step further by using the advantages of Nesterov Accelerated Gradient (NAG). It also takes in consideration the direction of the gradient at the next step, with respect to the history of the previous gradients, and adapts the value of the learning rate. As a result, this modification helps NRMSProp to convergence quicker than the original RMSProp, without any increase in the complexity of the RMSProp. In this work, many experiments had been conducted to evaluate the performance of NRMSProp with performing several tests with deep Convolution Neural Networks (CNNs) using different datasets on RMSProp, Adam, and NRMSProp optimizers. The experimental results showed that NRMSProp has achieved effective performance, and accuracy up to 0.97 in most cases, in comparison to RMSProp and Adam optimizers, without any increase in the complexity of the algorithm and with fine amount of memory and time.

In agriculture, the early and accurate identification of plant diseases is an important step in increasing crop yield. Leaf disease detection is one of the most widely used methods for identifying plant diseases. In recent years, automatic and accurate classification of plant leaf diseases has attracted increasing research attention. With the emergence of deep neural networks (DNN), the identification and classification of plant leaf diseases have achieved competitive results. This study proposes a method based on data augmentation techniques and advanced deep neural network models to improve the accuracy of the identification and classification of plant leaf diseases. First, augmentation techniques using image processing have been applied to efficiently train DNN models. Then, the advanced DNN models were fine-tuned to improve the classification accuracy and execution time. The method was evaluated on two public datasets (apple and potato leaf diseases) with accuracies of 98.08% and 99.07%, respectively.

Botanical plants suffer from several types of diseases that must be identified early to improve the production of fruits and vegetables. Mango fruit is one of the most popular and desirable fruits worldwide due to its taste and richness in vitamins. However, plant diseases also affect these plants’ production and quality. This study proposes a convolutional neural network (CNN)-based metaheuristic approach for disease diagnosis and detection. The proposed approach involves preprocessing, image segmentation, feature extraction, and disease classification. First, the image of mango leaves is enhanced using histogram equalization and contrast enhancement. Then, a geometric mean-based neutrosophic with a fuzzy c-means method is used for segmentation. Next, the essential features are retrieved from the segmented images, including the Upgraded Local Binary Pattern (ULBP), color, and pixel features. Finally, these features are given into the disease detection phase, which is modeled using a Convolutional Neural Network (CNN) (deep learning model). Furthermore, to enhance the classification accuracy of CNN, the weights are fine-tuned using a new hybrid optimization model referred to as Cat Swarm Updated Black Widow Model (CSUBW). The new hybrid optimization model is developed by hybridizing the standard Cat Swarm Optimization Algorithm (CSO) and Black Widow Optimization Algorithm (BWO). Finally, a performance evaluation is undergone to validate the efficiency of the projected model.

Tomato plant diseases caused by microorganisms such as fungus, bacterial etc. have been threatening the quality and quantity of tomato production for a few centuries. Therefore, precaution is necessary during the early stage to detect and cure the diseases that occurred on the leaves before the diseases spread to result in plant death. However, the detection method employed currently is inconsistent and inefficient. Most farmers employ manual visualisations to identify the types of plant diseases as the automatic monitoring and classification of diseases cannot be achieved. Such obstacles drive the need for a plant disease detection system, aided by artificial intelligence (AI). Therefore, in this work, four different Convolutional Neural Network (CNN) models for tomato plant diseases detection, namely VGG-16, ResNet-50, DenseNet-121 and ResNet-101 were implemented and the hyperparameters were manipulated for parametric studies. The performance of the different architectures with the selected hyperparameters was evaluated and compared according to the well-established evaluation metrics for deep learning; Precision, Recall, F1 score, Matthews correlation coefficient (MCC) and Accuracy. Combining all its merits, the ResNet-50 model which had achieved an accuracy of 100% with a shorter training time was proposed as the final model to be implemented in the tomato plant diseases classification system. In short, a CNN model that is capable of effective classification of tomato leaf diseases has been developed and its benefits for tomato cultivation are much anticipated.

Highlights Improving the robustness of maize leaf disease classification models in complex environments using multiple data enhancement methods. Fusing ResNet50 with the triplet attention mechanism to improve feature extraction. Reducing the use of labeled data to some extent by using SimCLR. The classification accuracy of four different leaf types was 91.87%, which was higher than other compared models. Abstract. Deep learning methods for classifying maize leaf diseases often need a lot of labeled data for training during the model training phase, but labeling data is sometimes difficult and expensive. In addition, complicated environmental circumstances can readily impede the recognition effect. In order to solve this issue, SimCLR and the triplet attention mechanism were coupled to create a system for classifying maize leaf diseases. Firstly, to increase the robustness of the model, several data enhancement techniques were used to imitate the interference of complex components in the natural environment. Secondly, the use of labeled data was minimized by learning the similarity between related categories using a self-supervised SimCLR framework. The feature extraction network simultaneously employed a triplet attention mechanism to enhance the model’s ability to extract information about the interactions between space and channel, improve attention to discriminative features, and lessen interference from complex factors. In order to verify the effects of incorporating SimCLR and triplet attention mechanisms, this work used three different types of maize leaf diseases and healthy leaves as experimental objects. The final enhanced model was compared and analyzed with seven widely used image classification models. The experimental results demonstrated that the maize leaf disease classification model suggested in this article effectively enhanced the classification performance of maize leaf diseases containing complex factors of interference with the test set, with the network model proposed in this article having an average classification accuracy of 91.87%, better than the seven comparative models and requiring no additional labeled data during the training process. Keywords: Image classification, Maize leaf disease, Self-supervised learning, SimCLR, Triplet attention module.

AI-Based Image Recognition for Early Diagnosis and Precise Classification of Pepper and Maize Leaf Diseases

In this work, we design a neural network named MCRNN for classifying signals with slight distinction. This model combines advantages of both Convolutional Neural Network and Recurrent Neural Network, allowing the network to distinguish between long signals with long time-frequency maps. We use an architecture involving both convolutional layers, for extracting high-level features and reduce the dimension of data from time-frequency maps, and recurrent layers, for aggregating long-term dependencies. We apply techniques like Adam optimizer, batch normalization, and softmax layers, obtaining highly competitive results of 99.9% accuracy even in a low SNR of -2dB. Moreover, we conduct extensive experiment through a range of noisy scenarios from -10dB to 30dB. The model can be extended to signal dataset with short time-frequency maps easily. We leverage open source dataset like DeepSig as verification.

In today's world, Internet of Things (IoT) technology has revolutionized various fields – including agriculture – by offering smart solutions that enhance crop yield and quality. One crucial aspect of smart farming is the application of IoT for disease detection and prevention. Crop diseases caused by bacteria and viruses can have devastating effects, leading to significant financial losses for farmers. Early detection plays a crucial role in managing these diseases, and IoT technology provides reliable and efficient methods to achieve this goal. To aid in the research on disease recognition in corn or maize plants, a large-scale dataset comprising 4188 images across four disease categories – namely, healthy, common rust, gray-leaf spot, and blight – is utilized. This dataset is used to train various machine learning algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), decision tree (DT), convolutional neural network (CNN), naïve Bayes (NB), gradient boosting trees (GBT), long short-term memory (LSTM), and the algorithms from recent literature such as genetic algorithm-based ensemble classifier, XGBoost, AdaBoost and XGBoost + KNN to accurately identify disease patterns in plants and is loaded into the Raspberry Pi. By interfacing a camera with a Raspberry Pi, leaf diseases can be detected, and the farm’s status can be sent to the farmer’s webpage using a Wi- Fi server. This information empowers farmers to take immediate action to control the disease such as activating sprinklers or applying pesticides. Through manually controllable on/off mechanisms, farmers can remotely manage these actions, ensuring the health and disease-free state of their crops. Among the selected algorithms, the XGBoost + KNN algorithm demonstrates the highest accuracy, reaching an impressive 98.77%.

The classification of plant leaf diseases is a critical step in safeguarding global food security and ensuring sustainable agricultural productivity.Recent advances in artificial intelligence (AI) and deep learning have accelerated the development of automated disease detection systems, improving both efficiency and diagnostic accuracy.This study introduces a novel hybrid strategy that integrates Convolutional Neural Networks (CNN) with traditional machine learning classifiers, specifically Support Vector Machines (SVM) and XGBoost, to leverage the complementary strengths of both paradigms.Two hybrid variants, CNN+SVM and CNN+XGBoost, are proposed, alongside the use of Particle Swarm Optimization (PSO) for feature selection, enabling the extraction of the most informative features for robust classification.The dataset used for experimentation comprises 15,000 maize leaf images, covering four major diseases: Northern Corn Leaf Blight, Common Rust, Brown Spot, and Gray Leaf Spot, along with healthy samples.Experimental evaluations confirm that the hybrid models outperform standalone CNN classifiers, achieving superior accuracy and generalization.Specifically, CNN+XGBoost achieved 95.2% accuracy, 93% precision, 96.3% recall, and 94.3% F1-score, while CNN+SVM achieved 92.7% accuracy, 91.1% precision, 94% recall, and 92.2% F1-score.These results demonstrate the synergistic benefits of combining CNNs with classical classifiers, while PSO significantly enhanced classification outcomes by identifying highly discriminative features.This research contributes to the advancement of precision agriculture by presenting a scalable hybrid framework for maize disease detection, offering timely diagnostic insights for farmers and promoting sustainable crop management practices.