Towards Generalization of Deep Learning-Based Plant Disease Identification Under Controlled and Field Conditions

Identifying corn diseases under field conditions is crucial for implementing effective disease management systems. Deep learning (DL)-based plant disease identification using deep neural networks (DNN) has been successfully implemented in recent years. Recent work suggests DL models trained on lab-acquired image data do not generalize to similar accuracy levels for identifying diseases in the field. Additionally, most studies have not evaluated the generalizability of DL models for identifying plant diseases from various datasets and diverse imaging conditions. This study evaluates how well DL models generalize across different datasets and environmental conditions for identifying plant diseases using five datasets consisting of foliar disease images in corn: namely PlantVillage, PlantDoc, Digipathos, northern leaf blight (NLB) dataset, and a custom acquired CD&S dataset. Multiple DL-based image classification models were trained and evaluated using different dataset combinations. Transfer learning was utilized using five different pre-trained DNN architectures: InceptionV3, ResNet50, VGG16, DesneNet169, and Xception, for conducting four different experiments. After training the models, images for distinct corn diseases from different datasets were used as testing data for evaluating the generalization ability of each DL model. It was observed that the DenseNet169 model performed the best. The highest generalization accuracy of 81.60% was observed when the DenseNet169 model was trained using red, green, blue, and alpha (RGBA) images from CD&S corn disease dataset with removed backgrounds. In addition, 77.50% to 80.33% accuracy was achieved for the PlantVillage dataset when combined with field-acquired images from either the PlantDoc or the CD&S dataset. The results suggest that background removal using RGBA images from CD&S dataset or augmentation of field and lab data improves the generalization performance of DL models and could be used for developing field-deployable disease management systems

CORN LEAF DISEASE PREDICTION USING DEEP LEARNING

Corn is a widely cultivated agricultural product, serving as a cornerstone in food production and industrial applications such as biofuels, playing a crucial role in the global economy. This study explores the application of deep transfer learning to accurately classify major corn diseases from leaf images, aiming to enhance disease management strategies for improved agricultural productivity and sustainability. The customized Dense net 201 model achieved 95% prediction accuracy on an untrained dataset. Data augmentation improved the model’s accuracy from 91% to 95%. This supervised learning approach enhances the model’s performance by increasing the diversity, leading to better generalization and accuracy. Experimentation of the four optimizers, namely Adagrad, SGD, AdaDelta, and Adam, achieved the same accuracy (95%). Increasing the data by a significant margin leads to a considerable enhancement of the model from 91% to 95% and thus serves as evidence of the effectiveness of the proposed method in improving the model performance, therefore improving the generalization samples for better training samples. It is testified that even the optimizer selection influences the accuracy rate. AdaDelta and Adagard achieved the highest accuracy at 95%, emphasizing the importance of selecting the right optimizer for optimal performance. The optimized deep learning model achieved 95% accuracy in detecting and classifying corn leaf diseases, benefiting farmers in disease identification

Review of the State of the Art of Transfer Learning for Plant Leaf Diseases Detection

Plant leaf diseases can have a significantly negative influence on the quantity and quality of agricultural cultivation, as well as the safety of food production. Plant leaf diseases could potentially entirely prevent the harvest of grains in some situations. Therefore, it is extremely important from a pragmatic standpoint to look for quick, automatic, cheap, and accurate ways to detect plant leaf diseases. One of the well-known plant leaf disease detection approaches is deep learning. Deep learning has several drawbacks as a result of the huge amount of data required to train the network. When a dataset has inadequate photographs, performance falls. An approach called "Transfer Learning" is an extensively used method for addressing the shortcomings of a small dataset, the length of the training process, and improving the performance of the model. In this study, we investigated transfer learning for deep CNNs to improve the learning capability to recognize leaf disease. This survey focuses on categorizing and analyzing the recent developments in transfer learning for Deep CNN situations to enhance learning performance by reducing the need for extensive training data collecting

Enhanced Disease Detection for Potato Crop Using CNN with Transfer Learning

As the fourth most popular basic food in the world,potatoes are widely available. In addition, the worldwidemarket is causing the demand to rise daily. Diseases likeearly and late blight have a significant impact on the quantity and quality of potatoes. Determining which potato leaves are afflicted with a certain illness becomes more challenging when interpreting these diseasesmanually. Thankfully, it is possible to identify potato leafdiseases by examining the leaf conditions. This proposedstudy presents a technique that employs deep learning toidentify the two types of diseases and generates an accurate classifier using heavy designs for convolutionalneural networks, such as GoogleNet, Resnet15, VGG16,and Xception. We achieved 97% accuracy in the first 40 CNN epochs, demonstrating the practicality of the deep neural network approach

Deep Learning for Early Detection, Identification, and Spatiotemporal Monitoring of Plant Diseases Using Multispectral Aerial Imagery

Production of food crops is hampered by the proliferation of crop diseases which cause huge harvest losses. Current crop-health monitoring programs involve the deployment of scouts and experts to detect and identify crop diseases through visual observation. These monitoring schemes are expensive and too slow to offer timely remedial recommendations for preventing the spread of these crop-damaging diseases. There is thus a need for the development of cheaper and faster methods for identifying and monitoring crop diseases. Recent advances in deep learning have enabled the development of automatic and accurate image classification systems. These advances coupled with the widespread availability of multispectral aerial imagery provide a cost-effective method for developing crop-diseases classification tools. However, large datasets are required to train deep learning models, which may be costly and difficult to obtain. Fortunately, models trained on one task can be repurposed for different tasks (with limited data) using transfer learning technique. The purpose of this research was to develop and implement an end-to-end deep learning framework for early detection and continuous monitoring of crop diseases using transfer learning and high resolution, multispectral aerial imagery. In the first study, the technique was used to compare the performance of five pre-trained deep learning convolutional neural networks (VGG16, VGG19, ResNet50, Inception V3, and Xception) in classifying crop diseases for apples, grapes, and tomatoes. The results of the study show that the best performing crop-disease classification models were those trained on the VGG16 network, while those trained on the ResNet50 network had the worst performance. The other studies compared the performance of using transfer learning and different three-band color combinations to train single- and multiple-crop classification models. The results of these studies show that models combining red, near infrared, and blue bands performed better than models trained with the traditional visible spectral band combination of red, green, and blue. The worst performing models were those combining near infrared, green, and blue bands. This research recommends that further studies be undertaken to determine the best band combinations for training single- and multi-label classification models for both crops and plants and diseases that affect them

Customized CNN Model for Multiple Illness Identification in Rice and Maize

Crop diseases imperil global food security and economies, demanding early detection and effective management. Convolutional Neural Networks (CNNs), particularly in rice and maize leaf disease classification, have gained traction due to their automatic feature extraction capabilities. CNN models eliminate manual feature extraction, enabling precise disease diagnosis based on learned features. Researchers have rapidly advanced these models, achieving promising results. Leaf disease characteristics like color changes, texture variations, and lesion appearance have been identified as useful for automated diagnosis using machine learning. Developing CNN models involves crucial stages: dataset preparation, architecture selection, hyperparameter tuning, and model training and evaluation. Diverse and accurately annotated datasets are pivotal, and appropriate CNN architecture selection, such as ResNet101 and XceptionNet, ensures optimal performance. These architectures' pre-training on vast image datasets enhances feature extraction. Hyperparameter tuning fine-tunes the model, and training and evaluation gauge its precision. CNN models hold potential to enhance rice and maize productivity and global food security by effectively detecting and managing diseases

Machine Learning for Leaf Disease Classification: Data, Techniques and Applications

The growing demand for sustainable development brings a series of information technologies to help agriculture production. Especially, the emergence of machine learning applications, a branch of artificial intelligence, has shown multiple breakthroughs which can enhance and revolutionize plant pathology approaches. In recent years, machine learning has been adopted for leaf disease classification in both academic research and industrial applications. Therefore, it is enormously beneficial for researchers, engineers, managers, and entrepreneurs to have a comprehensive view about the recent development of machine learning technologies and applications for leaf disease detection. This study will provide a survey in different aspects of the topic including data, techniques, and applications. The paper will start with publicly available datasets. After that, we summarize common machine learning techniques, including traditional (shallow) learning, deep learning, and augmented learning. Finally, we discuss related applications. This paper would provide useful resources for future study and application of machine learning for smart agriculture in general and leaf disease classification in particular

An Efficient Transfer Learning-based Approach for Apple Leaf Disease Classification

Correct identification and categorization of plant diseases are crucial for ensuring the safety of the global food supply and the overall financial success of stakeholders. In this regard, a wide range of solutions has been made available by introducing deep learning-based classification systems for different staple crops. Despite being one of the most important commercial crops in many parts of the globe, research proposing a smart solution for automatically classifying apple leaf diseases remains relatively unexplored. This study presents a technique for identifying apple leaf diseases based on transfer learning. The system extracts features using a pretrained EfficientNetV2S architecture and passes to a classifier block for effective prediction. The class imbalance issues are tackled by utilizing runtime data augmentation. The effect of various hyperparameters, such as input resolution, learning rate, number of epochs, etc., has been investigated carefully. The competence of the proposed pipeline has been evaluated on the apple leaf disease subset from the publicly available `PlantVillage' dataset, where it achieved an accuracy of 99.21%, outperforming the existing works.Comment: Accepted in ECCE 2023, 6 pages, 6 figures, 4 table