Automatic Identification Of Plant Diseases Research Articles

Processing Plant Diseases Using Transformer Model

Agriculture faces challenges in achieving high-yield production while minimizing the use of chemicals. The excessive use of chemicals in agriculture poses many problems. Accurate disease diagnosis is crucial for effective plant disease detection and treatment. Automatic identification of plant diseases using computer vision techniques offers new and efficient approaches compared to traditional methods. Transformers, a type of deep learning model, have shown great promise in computer vision, but as the technology is still new, many vision transformer models struggle to identify diseases by examining the entire leaf. This paper aims to utilize the vision transformer model in analyzing and identifying common diseases that hinder the growth and development of plants through the plant leave images. Besides, it aims to improve the model's stability by focusing more on the entire leaf than individual parts and generalizing better results on leaves not in the image center. Added features such as Shift Patch Tokenization, Locality Self Attention, and Positional Encoding help focus on the whole leaf. The final test accuracy obtained is 89.58%, with relatively slight variances in precision, accuracy, and F1 score across classes, as well as satisfactory model robustness towards changes in leaf orientation and position within the image. The model's effectiveness shows the vision transformer's potential for automated plant disease diagnosis, which can help farmers take timely measures to prevent losses and ensure food security.

Processing Plant Diseases Using Transformer Model

Agriculture faces challenges in achieving high-yield production while minimizing the use of chemicals. The excessive use of chemicals in agriculture poses many problems. Accurate disease diagnosis is crucial for effective plant disease detection and treatment. Automatic identification of plant diseases using computer vision techniques offers new and efficient approaches compared to traditional methods. Transformers, a type of deep learning model, have shown great promise in computer vision, but as the technology is still new, many vision transformer models struggle to identify diseases by examining the entire leaf. This paper aims to utilize the vision transformer model in analyzing and identifying common diseases that hinder the growth and development of plants through the plant leave images. Besides, it aims to improve the model's stability by focusing more on the entire leaf than individual parts and generalizing better results on leaves not in the image center. Added features such as Shift Patch Tokenization, Locality Self Attention, and Positional Encoding help focus on the whole leaf. The final test accuracy obtained is 89.58%, with relatively slight variances in precision, accuracy, and F1 score across classes, as well as satisfactory model robustness towards changes in leaf orientation and position within the image. The model's effectiveness shows the vision transformer's potential for automated plant disease diagnosis, which can help farmers take timely measures to prevent losses and ensure food security.

Processing Plant Diseases Using Transformer Model

Agriculture faces challenges in achieving high-yield production while minimizing the use of chemicals. The excessive use of chemicals in agriculture poses many problems. Accurate disease diagnosis is crucial for effective plant disease detection and treatment. Automatic identification of plant diseases using computer vision techniques offers new and efficient approaches compared to traditional methods. Transformers, a type of deep learning model, have shown great promise in computer vision, but as the technology is still new, many vision transformer models struggle to identify diseases by examining the entire leaf. This paper aims to utilize the vision transformer model in analyzing and identifying common diseases that hinder the growth and development of plants through the plant leave images. Besides, it aims to improve the model's stability by focusing more on the entire leaf than individual parts and generalizing better results on leaves not in the image center. Added features such as Shift Patch Tokenization, Locality Self Attention, and Positional Encoding help focus on the whole leaf. The final test accuracy obtained is 89.58%, with relatively slight variances in precision, accuracy, and F1 score across classes, as well as satisfactory model robustness towards changes in leaf orientation and position within the image. The model's effectiveness shows the vision transformer's potential for automated plant disease diagnosis, which can help farmers take timely measures to prevent losses and ensure food security.

Lightweight Isotropic Convolutional Neural Network for Plant Disease Identification

In today’s world, agricultural products are becoming increasingly scarce globally due to a variety of factors, and the early and accurate automatic identification of plant diseases can help ensure the stability and sustainability of agricultural production, improve the quality and safety of agricultural products, and help promote agricultural modernization and sustainable development. For this purpose, a lightweight deep isotropic convolutional neural network model, FoldNet, is designed for plant disease identification in this study. The model improves the architecture of residual neural networks by first folding the chain of the same blocks and then connecting these blocks with jump connections of different distances. Such a design allows the neural network to explore a larger receptive domain, enhancing its multiscale representation capability, increasing the direct propagation of information throughout the network, and improving the performance of the neural network. The FoldNet model achieved a recognition accuracy of 99.84% on the laboratory dataset PlantVillage using only 685k parameters and a recognition accuracy of 90.49% on the realistic scene dataset FGVC8 using only 516k parameters, which is competitive with other state-of-the-art models. In addition, as far as we know, our model is the first model that has fewer than 1M parameters while achieving state-of-the-art accuracy in plant disease identification. This proposal facilitates precision agriculture applications on mobile, low-end terminals.

Convolutional Neural Networks in Detection of Plant Leaf Diseases: A Review

Rapid improvements in deep learning (DL) techniques have made it possible to detect and recognize objects from images. DL approaches have recently entered various agricultural and farming applications after being successfully employed in various fields. Automatic identification of plant diseases can help farmers manage their crops more effectively, resulting in higher yields. Detecting plant disease in crops using images is an intrinsically difficult task. In addition to their detection, individual species identification is necessary for applying tailored control methods. A survey of research initiatives that use convolutional neural networks (CNN), a type of DL, to address various plant disease detection concerns was undertaken in the current publication. In this work, we have reviewed 100 of the most relevant CNN articles on detecting various plant leaf diseases over the last five years. In addition, we identified and summarized several problems and solutions corresponding to the CNN used in plant leaf disease detection. Moreover, Deep convolutional neural networks (DCNN) trained on image data were the most effective method for detecting early disease detection. We expressed the benefits and drawbacks of utilizing CNN in agriculture, and we discussed the direction of future developments in plant disease detection.

Plant Diseases Identification through a Discount Momentum Optimizer in Deep Learning

Deep learning proves its promising results in various domains. The automatic identification of plant diseases with deep convolutional neural networks attracts a lot of attention at present. This article extends stochastic gradient descent momentum optimizer and presents a discount momentum (DM) deep learning optimizer for plant diseases identification. To examine the recognition and generalization capability of the DM optimizer, we discuss the hyper-parameter tuning and convolutional neural networks models across the plantvillage dataset. We further conduct comparison experiments on popular non-adaptive learning rate methods. The proposed approach achieves an average validation accuracy of no less than 97% for plant diseases prediction on several state-of-the-art deep learning models and holds a low sensitivity to hyper-parameter settings. Experimental results demonstrate that the DM method can bring a higher identification performance, while still maintaining a competitive performance over other non-adaptive learning rate methods in terms of both training speed and generalization.

An overview of activation functions used in Neuronal Networks: Application for plant disease diagnosis

Deep learning techniques, particularly Convolutional Neural Networks (CNNs), have led to significant progress in image processing. Many applications in automatic identification of plant diseases have been developed. This work adopts a new approach that focuses on studying a relevant parameter that make a significant impact on the performance of CNNs, namely, the variants of activation function, particularly the most famous used functions and their influence on the model’s performance and accuracy. We will also present the different types of activation functions, which are also called transfer functions. Then, and through a case study application to the plant disease detection, we will have the opportunity to compare the results of these different functions with a graphical presentation using evaluation metrics, such as accuracy functions and loss functions as Binary Cross-Entropy. The training of the models was carried out using a free accessible database of 20,639 photographs, taken both in the laboratory and in real conditions from the crop fields. The data includes three plant species in fifteen distinct classes of combinations [plant, disease], including some healthy plants.

Convolutional Neural Network for Automatic Identification of Plant Diseases with Limited Data

Automated identification of plant diseases is very important for crop protection. Most automated approaches aim to build classification models based on leaf or fruit images. These approaches usually require the collection and annotation of many images, which is difficult and costly process especially in the case of new or rare diseases. Therefore, in this study, we developed and evaluated several methods for identifying plant diseases with little data. Convolutional Neural Networks (CNNs) are used due to their superior ability to transfer learning. Three CNN architectures (ResNet18, ResNet34, and ResNet50) were used to build two baseline models, a Triplet network and a deep adversarial Metric Learning (DAML) approach. These approaches were trained from a large source domain dataset and then tuned to identify new diseases from few images, ranging from 5 to 50 images per disease. The proposed approaches were also evaluated in the case of identifying the disease and plant species together or only if the disease was identified, regardless of the affected plant. The evaluation results demonstrated that a baseline model trained with a large set of source field images can be adapted to classify new diseases from a small number of images. It can also take advantage of the availability of a larger number of images. In addition, by comparing it with metric learning methods, we found that baseline model has better transferability when the source domain images differ from the target domain images significantly or are captured in different conditions. It achieved an accuracy of when the shift from source domain to target domain was small and when that shift was large and outperformed all other competitive approaches.

RETRACTED ARTICLE: Kuan noise filter with Hough transformation based reweighted linear program boost classification for plant leaf disease detection

In agriculture, plant leaf disease detection is significant concern to attain high crop yield and production. The product quality and productivity are affected, when proper care is not taken, it causes severe effects on plants. Several standard techniques have been developed for disease classification. The major issue facing these techniques is the automatic identification of plant diseases with minimal processing time. In order to improve the disease detection accuracy (DDA) with minimum time, Kuan filtered Hough transformation based reweighted linear program boost classification (KFHT-RLPBC) technique is introduced. KFHT-RLPBC technique includes three processes such as pre-processing, feature extraction and classification. A number of leaf images are gathered from plant dataset. In the pre-processing, the noises in the input leaf images are removed using Kuan filter to improve the image quality for achieving the higher disease detection accuracy. The Hough transform is utilized for extracting shape, texture and color features. KFHT-RLPBC technique reduces the time complexity (TC) in disease identification through the feature extraction process. Finally, the classification is done by applying the reweighted linear program to boost classification (RLPBC) to identify the disease at an earlier stage by constructing the number of weak learners. The boosting classifier combines the weak learner results and makes a strong one for achieving higher disease detection accuracy with minimum error. With plant village dataset, experimental evaluation is performed using certain parameters namely peak signal to noise ratio, disease detection accuracy and time complexity. Experimental results confirm that KFHT-RLPBC technique enhances disease detection accuracy and peak signal to noise ratio and reduces time complexity than the existing works.

Convolutional Neural Networks for the Automatic Identification of Plant Diseases.

Deep learning techniques, and in particular Convolutional Neural Networks (CNNs), have led to significant progress in image processing. Since 2016, many applications for the automatic identification of crop diseases have been developed. These applications could serve as a basis for the development of expertise assistance or automatic screening tools. Such tools could contribute to more sustainable agricultural practices and greater food production security. To assess the potential of these networks for such applications, we survey 19 studies that relied on CNNs to automatically identify crop diseases. We describe their profiles, their main implementation aspects and their performance. Our survey allows us to identify the major issues and shortcomings of works in this research area. We also provide guidelines to improve the use of CNNs in operational contexts as well as some directions for future research.

Plant disease identification from individual lesions and spots using deep learning

Deep learning is quickly becoming the standard technique for image classification. The main problem facing the automatic identification of plant diseases using this strategy is the lack of image databases capable of representing the wide variety of conditions and symptom characteristics found in practice. Data augmentation techniques decrease the impact of this problem, but those cannot reproduce most of the practical diversity. This paper explores the use of individual lesions and spots for the task, rather than considering the entire leaf. Since each region has its own characteristics, the variability of the data is increased without the need for additional images. This also allows the identification of multiple diseases affecting the same leaf. On the other hand, suitable symptom segmentation still needs to be done manually, preventing full automation. The accuracies obtained using this approach were, in average, 12% higher than those achieved using the original images. Additionally, no crop had accuracies below 75%, even when as many as 10 diseases were considered. Although the database does not cover the entire range of practical possibilities, these results indicate that, as long as enough data is available, deep learning techniques are effective for plant disease detection and recognition.