Improving YOLO-Based Plant Disease Detection Using αSILU: A Novel Activation Function for Smart Agriculture

In order to mitigate decreases in agricultural yield and production, the identification of diseases in plants assumes paramount importance. The agricultural sector has been employing various methodologies rooted in machine learning and image processing to address these challenges. This comprehensive analysis focuses specifically on the detection of diseases in rice plants by leveraging a diverse array of machine learning and image processing techniques with input images of infected rice plants. Furthermore, we delve into significant concepts pertaining to machine learning and image processing that aid in the identification and categorization of plant diseases. Various classification methods such as the k-Nearest Neighbor Classifier (KNN), Support Vector Machine (SVM), Probabilistic Neural Network (PNN), Genetic Algorithm (GA), and others find applications in agricultural research endeavors. The selection of an appropriate classification method assumes critical importance as the quality of the output is contingent on the input data. The classification of plant leaf diseases finds utility across multiple domains, including agriculture and biological research. This paper presents an extensive exploration of rice plant diseases, encompassing aspects such as image dataset size, preprocessing techniques, segmentation methods, and classifiers.

Crops play a vital role in the global food supply. Various environmental factors can cause crop diseases and insect pests, resulting in severe production losses. However, manual detection of crop diseases and insect pests is a time-consuming and error-prone process, which often fails to identify and prevent the spread of plant diseases in a timely and effective manner. To overcome these challenges, machine learning (ML) and deep learning (DL) technologies have been introduced in recent years to improve early identification and effective response to plant pests and diseases. In this article, the principles of convolutional neural network (CNN), recurrent neural network (RNN) and adversarial generative neural network (GAN) are explained, as well as the latest progress in the improvement and application of the three algorithms in plant disease detection and detection. By applying DL technology, efficient and accurate detection of pests and diseases can be achieved, thereby providing reliable support for agricultural production. It is hoped that this work will become a valuable resource for researchers studying plant pest and disease detection and provide guidance to further advance research and practice in this field. At the same time, some current challenges and issues that need to be solved are also discussed, such as data quality, target occlusion, model generalization ability, and adaptability in practical applications. Deep learning holds great promise in revolutionizing precision agriculture by providing efficient and accurate solutions for pest and disease management.

Citrus fruit crops are among the world’s most important agricultural products, but pests and diseases impact their cultivation, resulting in yield and quality losses. Computer vision and machine learning have been widely used to detect and classify plant diseases over the last decade, allowing for early disease detection and improving agricultural production. This paper presented an automatic system for the early detection and classification of citrus plant diseases based on a deep learning (DL) model, which improved accuracy while decreasing computational complexity. The most recent transfer learning-based models were applied to the Citrus Plant Dataset to improve classification accuracy. Using transfer learning, this study successfully proposed a Convolutional Neural Network (CNN)-based pre-trained model (EfficientNetB3, ResNet50, MobiNetV2, and InceptionV3) for the identification and categorization of citrus plant diseases. To evaluate the architecture’s performance, this study discovered that transferring an EfficientNetb3 model resulted in the highest training, validating, and testing accuracies, which were 99.43%, 99.48%, and 99.58%, respectively. In identifying and categorizing citrus plant diseases, the proposed CNN model outperforms other cutting-edge CNN model architectures developed previously in the literature.

A review on automated plant disease detection: motivation, limitations, challenges, and recent advancements for future research

Plant disease can seriously affect the growth and development of plants, resulting in reduced production and even death. Disease control can assist in plant breeding and cultivation, ensuring crop yield and quality. But traditional disease control methods are increasingly difficult to meet modern time, space and cost requirements. Deep learning technology has developed rapidly in recent years; it can improve the efficiency of plant disease diagnosis and detection, and greatly reduce the risk of human intervention and misjudgment. However, challenges such as data acquisition, environmental interference, and model deployment complexity hinder the application of deep learning in plant disease control. Therefore, there is an urgent need for disease feature optimization algorithms to protect current large-scale agricultural production.This research topic provides a platform for sharing the latest research results of deep learning in control of plant disease. The focus on the progress of deep learning technology in this field mainly involves the application of advanced technology in plant disease feature adaptation. This research topic aims to explore the latest research and application of deep learning technology in plant disease identification and control, focusing on: 1) identification techniques for early plant disease, 2) prediction and prevention of the spread of plant disease, 3) new methods for collecting multi-source plant disease image data, 4) quantitative evaluation of plant damage, 5) real-time application deployment based on embedded devices and edge wearable devices, 6) adaptive image processing methods for different application environments.A study by Le Yang et al. explored a new maize weed recognition model, SE-VGG16, and applied the SE-VGG16 model to weed detection of other species to build a weed detection model with the ability to generalize weed control in agricultural production processes. SE-VGG16 model is based on VGG16, adding SE attention mechanism. The model implements automatic focusing and allocates limited information processing resources to important parts. It has the characteristics of high efficiency, stability and high detection accuracy.Pushkar Gole et al. focused on lightweight visual converter networks in a study of automatic plant disease recognition. This study mainly introduces two tasks: one is to replace a multilayer perceptron (MLP) with the initial module and reconstruct the transformer Inception module to reduce the computational complexity of the vision transformer (ViT) model. In addition, the proposed new network is trained and tested on the PlantVillage dataset and the field maize image dataset, which proves its applicability in real scenarios.Further, Xin Xu et al. studied a cotton moth infestation monitoring method. In this study, five different models of cotton aphid infestation identification were constructed, trained and tested, among which YOLOv5 model had the best performance. The real-time identification system established in this study can provide a faster and more convenient technical means for the monitoring of cotton aphid infection.In another article, Nak Jung Choi et al. focused on identifying and tracking the behavior, movement, size, and habits of different species of insects by building a 3D monitoring system. While traditional 2D monitoring systems are static and ignore Zaxis depth dimensions, 3D systems are able to track insect movement more comprehensively, providing valuable depth dimension insights.Another study by Jitong Cai et al., explored a lightweight CNN model, FCA-EfficientNet, to achieve accurate identification of multiple maize disease. The work introduced autofocus modules and FCA modules to filter out background noise, allowing the network to focus on disease areas. It also makes the model more lightweight by removing redundant network structures.In addition, Fei Nan et al. studied the classification of maize disease from another angle. They focused on a deep convolutional neural network with RGB-D images. Among them, a variety of improved data preprocessing methods improved the accuracy and speed of maize diseases image recognition.A study by Zhenghang Zhang et al. explored the potential of applying Generative Adversarial Networks (GANs) to the diagnosis of cotton fusarium wilt. This study designed a model based on small sample learning and proposed an innovative cotton verticillium wilt diagnosis system. The convolutional neural network is used as feature extractor, and the trained GANs model is used for sample enhancement to improve the classification accuracy.In summary, these findings highlight the significant advances that deep learning technology has made in identifying and controlling plant disease in the information age. In the future, these models can be deployed in smart agriculture systems to monitor and identify plant disease in real time, so that appropriate control measures can be taken in a timely manner. As deep learning technology continues to evolve and improve, we can expect these models to play an increasingly important role in plant disease control, providing more effective support for agricultural production.

Background: Plant diseases affect agricultural output, quality and profitability, making them serious obstacles for agriculture. It is essential to detect diseases early in order to reduce losses while retaining sustainable practices. Plant disease detection has benefited greatly from the use of computer vision and deep learning in recent years because of their outstanding precision and computing capability. Objective: In this paper, we intend to investigate the role of deep learning in computer vision for plant disease detection while looking into how these techniques address complex disease identification problems. A variety of deep learning architectures were reviewed, and the contribution of frameworks such as Tensorflow, Keras, Caffe and PyTorch to the researchers' model construction was studied as well. Additionally, the usage of open repositories such as PlantVillage and Kaggle along with the customized datasets were discussed. Methods: We gathered the most recent developments in deep learning techniques for leaf disease detection through a systematic literature review of research papers published over the past decade, using reputable academic databases like Scopus and Web of Science, following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method for guidance. Results: This study finds that researchers consistently enhance existing deep learning architectures to improve prediction accuracy in plant disease detection, often by introducing novel architectures and employing transfer learning methods. Frameworks like TensorFlow, Keras, Caffe, and PyTorch are widely favored for their efficiency in development. Additionally, most studies opt for public datasets such as PlantVillage, Kaggle, and ImageNet, which offer an abundance of labelled data for training and testing deep learning models. Conclusion: While no singular ‘best' model emerges, the adaptability of deep learning and computer vision demonstrates the dynamic nature of plant disease recognition area, and this paper provides a comprehensive overview of deep learning's transformative impact on plant disease recognition by bringing together information from different studies. Keywords: Deep learning, Computer vision, Plant disease, Systematic literature review

Agriculture is crucial in determining land availability and economic productivity in developing countries, where most of the population relies on this sector. Even though plant diseases are common, they are primarily identified in agriculture. Automated disease detection technology is necessary for the timely identification of plant diseases. Detecting plant diseases is crucial in avoiding loss of production and enhancing agricultural produce quality. Lack of proper attention in this area can cause severe damage to plants, resulting in loss of product quality, quantity, or productivity. Diagnosing plant diseases can be a complex process that requires specialized knowledge and hands-on attention. The previous methods didn't find accurate disease in the plant leaves. To combat this problem, this paper presents deep learning (DL) based Assessment-based Convolutional Neural Network (A-CNN) method to detect healthy and unhealthy plant leaves. We first collect the plant image dataset from a Kaggle repository. To enhance the quality of plant images and achieve a smoother appearance, an Optimized Gaussian Wiener Filter (OGWF) can be utilized for image pre-processing. Additionally, the edges of plant leaves can be detected by implementing Sobel and Canny operators. Then we use Otsu's Threshold Fragment (OTF) algorithm to segment the disease-affected region. Additionally, the Spatial Grey-Level Dependence Matrix (SGLDM) algorithm is utilized to identify the most suitable feature of the impacted leaf. The A-CNN method is employed to detect healthy and unhealthy plant leaves accurately. The proposed simulation results have shown higher accuracy, sensitivity, and specificity in predicting plant diseases than other methods.

A comprehensive empirical study on bug characteristics of deep learning frameworks

The continuously growing population throughout globe demands an ample food supply, which is one of foremost challenge of smart agriculture. Timely and precise identification of weeds, insects, and diseases in plants are necessary for increased crop yield to satisfy demand for sufficient food supply. With fewer experts in this field, there is a need to develop an automated system for predicting yield, detection of weeds, insects, and diseases in plants. In addition to plants, livestock such as cattle, pigs, and chickens also contribute as major food. Hence, livestock demands precision methods for reducing the mortality rate of livestock by identifying diseases in livestock. Deep learning is one of the upcoming technologies that when combined with image processing promises smart agriculture to be a reality. Various applications of DL for smart agriculture are covered.

Deep learning (DL) frameworks serve as the backbone for a wide range of artificial intelligence applications. However, bugs within DL frameworks can cascade into critical issues in higher-level applications, jeopardizing reliability and security. While numerous techniques have been proposed to detect bugs in DL frameworks, research exploring common API patterns across frameworks and the potential risks they entail remains limited. Notably, many DL frameworks expose similar APIs with overlapping input parameters and functionalities, rendering them vulnerable to shared bugs, where a flaw in one API may extend to analogous APIs in other frameworks. To address this challenge, we propose MirrorFuzz, an automated API fuzzing solution to discover shared bugs in DL frameworks. MirrorFuzz operates in three stages: First, MirrorFuzz collects historical bug data for each API within a DL framework to identify potentially buggy APIs. Second, it matches each buggy API in a specific framework with similar APIs within and across other DL frameworks. Third, it employs large language models (LLMs) to synthesize code for the API under test, leveraging the historical bug data of similar APIs to trigger analogous bugs across APIs. We implement MirrorFuzz and evaluate it on four popular DL frameworks (TensorFlow, PyTorch, OneFlow, and Jittor). Extensive evaluation demonstrates that MirrorFuzz improves code coverage by 39.92% and 98.20% compared to state-of-the-art methods on TensorFlow and PyTorch, respectively. Moreover, MirrorFuzz discovers 315 bugs, 262 of which are newly found, and 80 bugs are fixed, with 52 of these bugs assigned CNVD IDs.

Identifying plant diseases has been mainstay in any farming and agricultural activity since time immemorial. The most common method has been visually assessing the plants by experts to identify the diseases of the plants and recommending pesticides and chemicals which can be used to treat diseases in plants. Technologies like mobile apps have brought about advancements to this traditional method of identifying and addressing plant diseases. Although there are now a plethora of mobile apps aiding farmers with addressing the issue of plant diseases, there is a scarcity of studies that explore the strategies that are being employed by these apps. This paper identifies the strategies that are being used by mobile apps in plant disease identification to set a foundation for further studies with regards to this field. This is performed through analyzing mobile apps on both Google Play Store and Apple Store, through the description given by the developers and the use of the installed apps to determine the content offered and how it aids farmers in identifying curbing plant diseases. The findings using the inclusion and exclusion criteria that was set showed that overall, they are a few mobile apps in plant pest and disease identification with 152 apps only examined. The database strategy was identified as the most used strategy by developers with 78 mobile apps and the AI Diagnostics was the second most used strategy with 26 mobile apps from both the Google Play Store and Apple App Store combined.

Plant diseases and pests have always been major contributors to losses that occur in agriculture. Currently, the use of deep learning-based convolutional neural network models allows for the accurate identification of different types of plant diseases and pests. To enable more efficient identification of plant diseases and pests, we design a novel network architecture called Dise-Efficient based on the EfficientNetV2 model. Our experiments demonstrate that training this model using a dynamic learning rate decay strategy can improve the accuracy of plant disease and pest identification. Furthermore, to improve the model's generalization ability, transfer learning is incorporated into the training process. Experimental results indicate that the Dise-Efficient model boasts a compact size of 13.3 MB. After being trained using the dynamic learning rate decay strategy, the model achieves an accuracy of 99.80% on the Plant Village plant disease and pest dataset. Moreover, through transfer learning on the IP102 dataset, which represents real-world environmental conditions, the Dise-Efficient model achieves a recognition accuracy of 64.40% for plant disease and pest identification. In light of these results, the proposed Dise-Efficient model holds great potential as a valuable reference for the deployment of automatic plant disease and pest identification applications on mobile and embedded devices in the future.

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.

Objective: To design a deep transfer learning framework for modeling fluence map predictions for stereotactic body radiation therapy (SBRT) of adrenal cancer and similar sites that usually have a small number of cases. Approach: We developed a transfer learning framework for adrenal SBRT planning that leverages knowledge in a pancreas SBRT planning model. Treatment plans from the two sites had different dose prescriptions and beam settings but both prioritized gastrointestinal sparing. A base framework was first trained with 100 pancreas cases. This framework consists of two convolutional neural networks (CNN), which predict individual beam doses (BD-CNN) and fluence maps (FM-CNN) sequentially for 9-beam intensity-modulated radiation therapy (IMRT) plans. Forty-five adrenal plans were split into training/validation/test sets with the ratio of 20/10/15. The base BD-CNN was re-trained with transfer learning using 5/10/15/20 adrenal training cases to produce multiple candidate adrenal BD-CNN models. The base FM-CNN was directly used for adrenal cases. The deep learning (DL) plans were evaluated by several clinically relevant dosimetric endpoints, producing a percentage score relative to the clinical plans. Main results: Transfer learning significantly reduced the number of training cases and training time needed to train such a DL framework. The adrenal transfer learning model trained with 5/10/15/20 cases achieved validation plan scores of 85.4/91.2/90.7/89.4, suggesting that model performance saturated with 10 training cases. Meanwhile, a model using all 20 adrenal training cases without transfer learning only scored 80.5. For the final test set, the 5/10/15/20-case models achieved scores of 73.5/75.3/78.9/83.3. Significance: It is feasible to use deep transfer learning to train an IMRT fluence prediction framework. This technique could adapt to different dose prescriptions and beam configurations. This framework potentially enables DL modeling for clinical sites that have a limited dataset, either due to few cases or due to rapid technology evolution.

The aim of this article to introduces various image processing and machine learning techniques used to identify plant diseases based on images of diseased plants in order to recognize disease in plants from images and necessary in image processing and machine learning as they apply to the identification and categorization of plant diseases. We meticulously review more content and provide important standards. These characteristics include things like the size of the photo collection, preprocessing, segmentation techniques, classifier types, classifier resolution, and other things. To suggest and arrange our work on the classification and identification of plant diseases, we explain our study here. Then, based on the principal technical solution used in the method, each of these groups is split using machine learning techniques. Photos of plant disease samples were processed using support vector (SVM) and k-mean clustering techniques to extract color and texture information. The results show that the SVM classifier is a very good tool for detecting and identifying plant-borne diseases in agricultural crops.