Background: The timely and precise detection of foliage diseases is essential for the efficient management of bean crops. Conventional techniques for detecting leaf diseases need professionals to physically examine the leaves, which is laborious and subject to human mistakes. Due to advanced&nbsp;machine learning (ML) computations, convolutional neural networks (CNNs)&nbsp;are capable of examining leaf diseases. MobileNetV3 is particularly effective for edge device applications because of its lightweight and efficient design. 
 
Methods: In this study, the effectiveness of MobileNetV3 in distinguishing three disease categories: Angular Leaf Spot, Bacterial Blight and Healthy, from images of bean leaves is evaluated. The dataset utilized in this study is sourced from Kaggle and comprises a total of 1295 images categorized into three classes. Each image was resized to 224&acute;224 pixels and normalized to a range of [0, 1] to prepare it for input into the MobileNetV3 model. Data augmentation techniques, including rotation, flipping and zooming, were applied to enhance the model&rsquo;s generalization capability. The MobileNetV3 architecture was implemented with a batch size of 32 and trained for 25 epochs using the Adam optimizer with a learning rate of 1e-4. Performance was evaluated based on accuracy, precision, recall and F1-score, using a split dataset consisting of training (80%), validation (10%) and test (10%) subsets. 
 
Result: The MobileNetV3 model demonstrated a high level of performance in classifying bean leaf diseases. The model achieved an overall accuracy of 92.19% with a macro average F1-score of 92.27%. These results indicate that MobileNetV3 is effective for automated classification of bean leaf diseases, with high precision and recall, particularly for Angular Leaf Spot.

Detailed Analysis and Advanced Classification of Bean Leaf Diseases using MobileNetV3

<div>Background: Legumes play an important role in improving soil quality and nutrition in humans. Soybean is one of the legumes which are rich in protein and oil content. Nutrients deficiency in soybean plant could affect the growth of the plants and might lead to loss in its yield. The developments in modern technologies such as computer vision and deep learning are being leveraged in identifying nutrient deficiencies in soybean plants. 
 
Methods: Convolutional neural network with six feature extraction blocks and one classification block is developed to identify macro nutrient deficiency in soybean plants. Images are first collected, pre-processed and labeled. They are then split into training images and testing images in the ratio of 80:20. The proposed convolutional neural network model is trained over training images and the final trained model is tested with testing images. 
 
Result: In detecting the deficiency of macro nutrients such as nitrogen, phosphorus and potassium in soybean plants, the testing results of the proposed convolutional neural network architecture achieved an accuracy of 97.43%. Accuracy comparison against existing models such as VGG16, ResNet50 and MobileNetV3 demonstrate that the proposed model effectively identifies nutrient deficiencies in soybean plants. Thus, the proposed system is designed to support farmers in making timely decisions and to contribute to food security by leveraging deep learning techniques.</div>

Detailed Analysis and Advanced Classification of Bean Leaf Diseases using MobileNetV3

Smart Detection of Macro Nutrient Deficiency in Soybean Plant using Convolutional Neural Network