CLASSIFICATION OF CORN PLANT DISEASES USING VARIOUS CONVOLUTIONAL NEURAL NETWORK

Based on data from the East Java Badan Pusat Statistik (BPS) in 2020, corn production in 2019 decreased by 622,403 tons. The decrease in production was caused by a disease that attacked corn plants identified from the corn leaves' physical appearance. This study aims to obtain an architectural model with good performance between AlexNet, LeNet, and MobileNet in detecting diseases of maize plants. The dataset used in this study came from Kaggle, with 4188 images divided into four disease classes: Common Rust, Gray Leaf Spot, Blight, and Healthy. Agricultural experts from Bantul have confirmed the appearance of each class of corn plant diseases.  The preprocessing process is carried out to prepare the data so that the amount of data for each class is balanced. The image data used in this study totaled 4000 images which were divided into training data and testing data with a ratio of 80:20. Based on the experimental results, it was found that the MobileNet architecture has better performance than AlexNet and LeNet with an accuracy value of 83.37%, average precision of 0.8337, and g-mean of 0.8298. These results have been validated by agricultural experts in Bantul Regency and corn farmers experienced in corn farming

Development of a Mobile Application for Plant Disease Detection using Parameter Optimization Method in Convolutional Neural Networks Algorithm

Plant diseases are a serious problem in agriculture that affects both the quantity and quality of the harvest. To address this issue, authors developed a mobile software capable of detecting diseases in plants by analyzing their leaves using a smartphone camera. This research used the Convolutional Neural Networks (CNN) method for this purpose. In the initial experiments, authors compared the performance of four deep learning architectures: VGG-19, Xception, ResNet-50, and InceptionV3. Based on the results of the experiments, authors decided to use the CNN Xception as it yielded good performance. However, the CNN algorithm does not attain its maximum potential when using default parameters. Hence, authors goal is to enhance its performance by implementing parameter optimization using the grid search algorithm to determine the optimal combination of learning rate and epoch values. The experimental results demonstrated that the implementation of parameter optimization in CNN significantly improved accuracy in potato plants from 96.3% to 97.9% and in maize plants from 87.6% to 93.4%

Smart Farm-Care using a Deep Learning Model on Mobile Phones

Deep learning and its models have provided exciting solutions in various image processing applications like image segmentation, classification, labeling, etc., which paved the way to apply these models in agriculture to identify diseases in agricultural plants. The most visible symptoms of the disease initially appear on the leaves. To identify diseases found in leaf images, an accurate classification system with less size and complexity is developed using smartphones. A labeled dataset consisting of 3171 apple leaf images belonging to 4 different classes of diseases, including the healthy ones, is used for classification. In this work, four variants of MobileNet models - pre-trained on the ImageNet database, are retrained to diagnose diseases. The model’s variants differ based on their depth and resolution multiplier. The results show that the proposed model with 0.5 depth and 224 resolution performs well - achieving an accuracy of 99.6%. Later, the K-means algorithm is used to extract additional features, which helps improve the accuracy to 99.7% and also measures the number of pixels forming diseased spots, which helps in severity prediction. Doi: 10.28991/ESJ-2023-07-02-013 Full Text: PD

Robusta coffee leaf diseases detection based on MobileNetV2 model

Indonesia is a major exporter and producer of coffee, and coffee cultivation adds to the nation's economy. Despite this, coffee remains vulnerable to several plant diseases that may result in significant financial losses for the agricultural industry. Traditionally, plant diseases are detected by expert observation with the naked eye. Traditional methods for managing such diseases are arduous, time-consuming, and costly, especially when dealing with expansive territories. Using a model based on transfer learning and deep learning model, we present in this study a technique for classifying Robusta coffee leaf disease photos into healthy and unhealthy classes. The MobileNetV2 network serves as the model since its network design is simple. Therefore, it is likely that the suggested approach will be deployed further on mobile devices. In addition, the transfer learning and experimental learning paradigms. Because it is such a lightweight net, the MobileNetV2 system serves as the foundational model. Results on Robusta coffee leaf disease datasets indicate that the suggested technique can achieve a high level of accuracy, up to 99.93%. The accuracy of other architectures besides MobileNetV2 such as DenseNet169 is 99.74%, ResNet50 architecture is 99.41%, and InceptionResNetV2 architecture is 99.09%

An Improved MobileNet for Disease Detection on Tomato Leaves

Tomatoes are widely grown vegetables, and farmers face challenges in caring for them, particularly regarding plant diseases. The MobileNet architecture is renowned for its simplicity and compatibility with mobile devices. This study introduces MobileNet as a deep learning model to enhance disease detection efficiency in tomato plants. The model is evaluated on a dataset of 2,064 tomato leaf images, encompassing early blight, leaf spot, yellow curl, and healthy leaves. Results demonstrate promising accuracy, exceeding 0.980 for disease classification and 0.975 for distinguishing between diseases and healthy cases. Moreover, the proposed model outperforms existing approaches in terms of accuracy and training time for plant leaf disease detection

Research and Development of the Pupil Identification and Warning System using AI-IoT

Currently, pupils being left in the classroom, in the house or in the car is happening a lot, causing unintended incidents. The reason is that parents or caregivers of pupils go through busy and tiring working hours, so they accidentally leave pupils in the car, indoors, or forget to pick up students at school. In this paper, we developed an algorithm to recognize students who use neural networks and warn managers, testing on a model integrated Raspberry Pi 4 kit programmed on Python in combination with cameras, sim modules, and actuators to detect and alert abandoned pupils to the manager to take timely remedial measures and avoid unfortunate circumstances. With the ability to manage students, the system collects and processes images and data on student information for artificial intelligence (AI) systems to recognize when operating. The system of executive structures serves to warn when students are left in the car, in the classroom, or in the house to avoid unintended incidents or safety risks

Image based Plant leaf disease detection using Deep learning

Agriculture is important for India. Every year growing variety of crops is at loss due to inefficiency in shipping, cultivation, pest infestation in crop and storage of government-subsidized crops.  There is reduction in production of good crops in both quality and quantity due to Plants being affected by diseases. Hence it is important for early detection and identification of diseases in plants. The proposed methodology consists of collection of Plant leaf dataset, Image preprocessing, Image Augmentation and Neural network training. The dataset is collected from ImageNet for training phase. The CNN technique is used to differentiate the healthy leaf from disease affected leaf. In image preprocessing resizing the image is carried out to reduce the training phase time. Image augmentation is performed in training phase by applying various transformation function on Plant images. The Network is trained by Caffenet deep learning framework. CNN is trained with ReLu (Rectified Linear Unit). The convolution base of CNN generates features from image through the multiple convolution layers and pooling layers. The classifier part of CNN classifies the image based on the features extracted from the convolution base. The classification is performed through the fully connected layers. The performance is measured using 10-fold cross validation function. The final layer uses activation function like softmax to categorize the outputs

Segmentation and detection of Woody Trunks using Deep Learning for Agricultural Robotics

This project aims to help the implementation of image processing algorithms in agriculture robots so that they are robust to different aspects like weather conditions, vineyard terrain irregularities and efficient to operate in small robots with low energy consumption. Along with this, Deep Learning models became more complex. Thus, not all processors can handle such models. So, to develop a system with real-time detection for low-power processors becomes demanding because there is a lack of real datasets annotated for vine trunks and expedite tools to support this work. To support the deployment of deep-learning technology in agricultural robots, this dissertation presents the first public dataset of vine trunk images, called VineSet, with respective annotations for each trunk. This dataset was built from scratch, having a total of 9481 images of 5 different Douro vineyards, resulting from the images initially collected by AgRob V16 and various augmentation operations. Then, this dataset was used to train different state-of-the-art Deep Learning object detection models, together with Google Tensor Processing Unit. In parallel with this, this work presents an assisted labelling procedure that uses our trained models to reduce the time spent on labelling in the creation of new datasets. Also, this dissertation proposes the segmentation of vine trunks, using object detection models and semantic segmentation models. In this way, all the work done will allow the integration of edge-AI algorithms in SLAM, like Vine-SLAM, which will serve for the localisation and mapping of the robot, through natural markers in the vineyards.Agricultural robots need image processing algorithms, which should be reliable under all weather conditions and be computationally efficient. Furthermore, several limitations may arise, such as the characteristic vineyard terrain irregularities or overfitting in the training of neural networks that may affect the performance. In parallel with this, the evolution of Deep Learning models became more complex, demanding an increased computational complexity. Thus, not all processors can handle such models efficiently. So, developing a system with a real-time performance for low-power processors becomes demanding and is nowadays a research and development challenge because there is a lack of real data sets annotated and expedite tools to support this work. To support the deployment of deep-learning technology in agricultural robots, this dissertation presents a public VineSet dataset, the first public large collection of vine trunk images. The dataset was built from scratch, having a total of 9481 real image frames and providing the vine trunks annotations in each one of them. VineSet is composed of RGB and thermal images of 5 different Douro vineyards, with 952 initially collected by AgRob V16 robot, and others 8529 image frames resulting from a vast number of augmentation operations. To check the validity and usefulness of this VineSet dataset, in this work is presented an experimental baseline study, using state-of-the-art Deep Learning models together with Google Tensor Processing Unit. To simplify the task of augmentation in the creation of future datasets, we propose an assisted labelling procedure - by using our trained models - to reduce the labelling time, in some cases ten times faster per frame. This dissertation presents preliminary results to support future research in this topic, for example with VineSet leads possible to train (by transfer learning procedure) existing deep neural networks with Average Precision (AP) higher than 80% for vineyards trunks detection. For example, an AP of 84.16% was achieved for SSD MobileNet-V1. Also, the models trained with VineSet present good results in other environments such as orchards or forests. Our automatic labelling tool proves this, reducing annotation time by more than 30% in various areas of agriculture and more than 70% on vineyards. In this dissertation, we also propose the segmentation of the vine trunks. Firstly, object detection models were used together with VineSet to perform the trunk segmentation. To evaluate the performance of the different models, a script that implements some metrics of semantic segmentation was built. The results showed that the object detection models trained with VineSet were not only suitable for trunk detection but also trunk segmentation. For example, a DICE Similarity Index (DSI) of 70.78% was achieved for SSD MobileNet-V1. Finally, semantic segmentation was also briefly approached. A subset of the images of VineSet was used to train several models. Results show that semantic segmentation can substitute DL-based object detection models for pixel-based classification if a proper training set is provided. In this way, all the work done will allow the integration of edge-AI algorithms in SLAM, like Vine-SLAM, which will serve for the localisation and mapping of the robot, through natural markers in the vineyards

DeepLab V3+ Based Semantic Segmentation of COVID -19 Lesions in Computed Tomography Images

Abstract- Coronavirus 2019 spreads rapidly worldwide causing a global epidemic. Early detection and diagnosis of COVID-19 is critical for treatment as it causes respiratory syndrome appears in the chest medical images, such as computed tomography (CT) images, and X-ray images. The CT images are more sensitive and have more details compared to the X-ray images. Thus, automated segmentation plays an imperative role in detecting, diagnosing, and determining the spreading of COVID-19. In this paper, the DeepLabV3+ combined with MobileNet-V2 model was implemented. To validate this combination, we conducted a comparative study between the DeepLabV3+ variants by its combination with MobileNet-V2 against DeepLabV3+ combined with different CNN, namely ResNet-18, and ResNet50. Also, a comparative study with the basic traditional U-Net and modified Alex for segmentation was carried out. The experimental results showed the superiority of the using DeepLabV3+ combined with MobileNet-V2 for COVID-19 segmentation by achieving 97.5% mean accuracy, 95.2% sensitivity, 99.7% specificity, 99.7% precision, 99.3 % weighted Jaccard coefficient, and 97.5% weighted dice coefficient