Crop Disease Detection Research Articles

Tomato Plant Leaf Disease Detection Using Convolutional Neural Network

Our country is growing country, India is mostly depended on the agriculture field, and Agriculture is pillar of our country’s economy. Because of this India has to improve in agriculture filed, in many of situations’ farmer face difficulties to detect crop disease in a case large area farm, or late in detection of crop disease in such cases farmer lose their all crop and face the huge loss, to avoid this, in agricultural field is detection of plant diseases is very important. And it is very difficult tasks. In regular normal procedure it requires a more time period and trained person to detect accurate plant disease. In this paper, we proposed effective way for plant disease detection using computer science and machine learning model. Disease transmission from unhealthy plant to all other healthy plants in farm is one of the major damages to crop farm. And these diseases spread like forest fire and have the possible to impact the whole operation if not identified in early on. Now Plant disease detection methods helps to identify infected plants in a very early stage and also help us to identify plant disease in a wide range of area of crops in a cost-effective manner. The aim of this project model is to implement machine learning models, in our proposed system we take a plant leaf image on that leaf images we predict the plant disease using Convolution Neural Networks (CNN) model, in that we build a such a model to predict the plant disease with maximum accuracy and it is for plant disease detection for tomato plant. This machine learning model is analyzing different image metrics pixels data to determine the best performance of network. For that dataset were used around 7around 8016 images we going to use to train the model. We going to use 14 layers CNN models to get better accuracy results. Model consist various layers like convolution, pooling, flatten and dense. Early two layers that is preprocessing and augmentation of images Finally, we get the result of which disease that plant have.

Agricultural Crop Recommendation, Crop Disease Detection and Price Prediction Using Machine Learning

India's foundation is its agriculture. With over 60% of the workforce employed and producing over 18% of the nation's GDP, it is a vital sector of the Indian economy. Although there are many ways in which we can use technology to increase product production, a farmer can only profit if he is able to sell his crops. Three laws have been passed by the Indian government to encourage the export of agricultural products across the nation. But today, we witness farmers all over the nation fighting against these regulations to protect their rights. Farmers worry that big merchants will exploit them as puppets and undercut the price at which they sell their goods. After doing a thorough analysis of the situation, we developed the concept of creating an agricultural produce application that facilitates direct communication between farmers and retailers, allows for product reviews and crop yielding rate prediction, and predicts the price of agricultural produce based on quantity produced and previous years' sales rates. Unpredictable rains, unexpected temperature decreases, and heat waves have all been brought on by the shifting climate, and the ecosystem has suffered significant harm. Thankfully, machine learning has produced useful methods for tackling international problems, such as agriculture. These climate change-related agricultural issues can be resolved by using various machine learning methods. The purpose of this piece is to Create a method to identify crop diseases and suggest crops. For both objectives, publicly accessible datasets were utilized. Regarding the crop recommendation system, feature extraction was done, and a variety of machine learning methods were used to train the dataset, including Support Vector Machine (SVM), Random Forest, Decision Tree, Logistic Regression, and Multilayer Perceptron. 99.30% accuracy was attained via the random forest algorithm.CNN architectures such as ResNet50, and EfficientNetV2 were trained and compared for the plant disease identification system. EfficientNetV2 outperformed the rest, with a high accuracy of 96.08%.

A smart and sustainable framework for millet crop monitoring equipped with disease detection using enhanced predictive intelligence

Today, there is a visible reduction in productivity of common crops like rice and wheat in agricultural sector. At the same time, millet crops have emerged as an alternative to counter global food security issues because of their adaptability and nutritional value that can combat malnutrition. Also, because of its low moisture requirement and tolerance to extreme climatic conditions, it is perceived as a stable cereal. But its cultivation is affected by diseases like rust and blast, thereby causing harm to the farming economy. Conventional operational methods of disease detection require regular manual intervention which are also costly. The correctness of expert suggestions is also under scrutiny. Thus, a sustainable, robust and low cost modern approach for millet crop monitoring and disease detection is required. This research aims to develop a smart and sustainable framework by integrating Internet of things (IoT) and deep learning (DL). In the presented framework, a sensory module based automated crop health data gathering system with an improved deep learning-assisted intelligent disease recognition model is developed for the millet crop. Sensors collect data from millet farmland transfer the crop data readings to the cloud server for storage and Raspberry Pi for further detection. Any abnormality in reading leads to an alert notification communicated to the farmer. A hybrid predictive model, Customized Convolutional neural network (Customized-CNN) model works with the Raspberry Pi to predict the presence of blast and rust disease symptoms in millet. The proposed sustainable model is implemented, and it generates productive outcomes. The recorded accuracy, precision, recall, and f-score values with the customized CNN model were 98.8%, 98.2%, 97.4%, and 97.7%, respectively. The training and testing delays were only 67 s and 88 s, respectively. A sample of yearly sensor readings was generated to show the accuracy and reliability of the model’s data collection. Also, the model proved to be scalable, as it gave noteworthy performance with diverse crops. The evaluation shows the reliability of the model, and it can be used by farming societies to enhance millet cereal yield in a cost-effective way.

A Research Paper on Crop Disease Detection Using Deep Learning Model

Abstract: Crop disease detection is the process of identifying and classifying plant diseases from images or other data. This can be done manually or using automated methods. Manual methods typically involve a human expert visually inspecting the plant and identifying the disease. Automated methods use computer vision algorithms to identify the disease from images or other data. Crop diseases pose a significant threat to global food security by causing substantial yield losses and reduced quality in agricultural production. Timely and accurate detection of crop diseases is crucial to mitigate these losses and ensure sustainable agricultural practices. In recent years, advancements in sensor technology, data analysis techniques, and machine learning algorithms have enabled the development of various methods for crop disease detection. This survey paper aims to provide a comprehensive overview of the state-of-the-art techniques, methodologies, and challenges in the field of crop disease detection. The paper begins by introducing the importance of crop disease detection in modern agriculture and its impact on both economic and environmental aspects. It then categorizes the existing detection methods into several key approaches, including visual inspection, spectroscopy, image analysis, and sensor-based techniques. For each approach, the paper discusses its underlying principles, advantages, limitations, and representative studies.

Sustainable Agriculture Practice using Machine Learning

The changing climate has caused unpredictable rainfall, unusual temperature drops, and heat waves, leading to considerable damage to the environment. Fortunately Machine Learning has provided effective tools to address global issues, including agriculture. By employing different ML algorithms, it is possible to solve the agricultural problems caused by these climate changes. The objective of this article is to develop a system for crop recommendation and disease detection in a plant. Publicly available datasets were used for both tasks. For the crop recommendation system, feature extraction was performed, and the dataset was trained using various Machine Learning algorithms, namely Decision Tree, Logistic Regression, Random Forest, Support Vector Machine (SVM) and Multilayer Perceptron. The random forest algorithm achieved an excellent accuracy of 99.31%.For the plant disease identification system, CNN architectures like - VGG16, ResNet50, and EfficientNetV2 - were trained and compared. Among these, EfficientNetV2 achieved high accuracy of 96.07%.

Scalable agritech growbox architecture

Introduction: Urban farming has gained prominence in Singapore, offering opportunities for automation to enhance its efficiency and scalability. This study, conducted in collaboration with a leading Singaporean urban farming company, introduces an IoT-based automated farming system. This system incorporates an agnostic growbox and a web application dashboard for intelligent monitoring of crop growth. The presented approach provides an open-source and cost-effective solution for a scalable urban farming architecture. The agnostic growbox system offers both accessibility and scalability, utilizing cost-effective and modular hardware components with open-source software, thereby increasing customizability and accessibility compared to commercial growbox products. The authors anticipate that this approach will find diverse applications within the realm of urban farming, streamlining, and improving the efficiency of urban farming through automation.Methods: The study employs an integrated solution that incorporates an image analytics approach for the proficient and accurate classification of crop disease phenotypes, specifically targeting chlorosis and tip burn in lettuce crops. This approach is designed to be hardware- and software-efficient, obviating the necessity for extensive image datasets for model training. The image analytics approach is compared favourably with a machine learning approach, evaluating the accuracy of categorization using the same dataset. Additionally, the approach is assessed in terms of time and cost efficiency in comparison to machine learning techniques.Results: The image analytics approach demonstrated notable scalability, time efficiency, and accuracy in the detection of crop diseases within urban farming. Early detection, particularly of chlorosis and tip burn, proves critical in mitigating crop wastage. The results indicate that the integrated solution provided a reliable and effective means of disease classification, with significant advantages over traditional machine learning approaches in terms of time and cost efficiency.Discussion: The presented IoT-based automated farming system, incorporating the agnostic growbox and image analytics approach, holds promise for revolutionizing urban farming practices. Its open-source nature, coupled with cost-effectiveness and scalability, positions it as a practical solution for urban farming architecture. The system's ability to efficiently detect and classify crop diseases, particularly chlorosis and tip burn, offers a substantial contribution to reducing wastage and enhancing crop yield. Overall, this approach paves the way for a more efficient and sustainable future for urban farming through the integration of automation and advanced analytics. Further exploration and implementation of this technology in diverse urban farming settings is warranted.

Recognition of Wheat Leaf Diseases Using Lightweight Convolutional Neural Networks against Complex Backgrounds.

Wheat leaf diseases are considered to be the foremost threat to wheat yield. In the realm of crop disease detection, convolutional neural networks (CNNs) have emerged as important tools. The training strategy and the initial learning rate are key factors that impact the performance and training speed of the model in CNNs. This study employed six training strategies, including Adam, SGD, Adam + StepLR, SGD + StepLR, Warm-up + Cosine annealing + SGD, Warm-up + Cosine, and annealing + Adam, with three initial learning rates (0.05, 0.01, and 0.001). Using the wheat stripe rust, wheat powdery mildew, and healthy wheat datasets, five lightweight CNN models, namely MobileNetV3, ShuffleNetV2, GhostNet, MnasNet, and EfficientNetV2, were evaluated. The results showed that upon combining the SGD + StepLR with the initial learning rate of 0.001, the MnasNet obtained the highest recognition accuracy of 98.65%. The accuracy increased by 1.1% as compared to that obtained with the training strategy with a fixed learning rate, and the size of the parameters was only 19.09 M. The above results indicated that the MnasNet was appropriate for porting to the mobile terminal and efficient for automatically identifying wheat leaf diseases.

Crop disease recognition based on the YOLOv5 and transfer learning

Crop disease detection is an important factor in agricultural production. Traditional object detection methods can't effectively screen key features, resulting in weak crop disease control in many countries. In recent years, several convolutional neural networks for object detection have been proposed, which makes it possible to apply computer vision to crop disease identification through deep learning. YOLOv5 is an advanced object detection network, which can extract key features and use human visual attention mechanism as a reference. This paper would evaluate the performance of four pre-trained models of YOLOv5 in object detection of crop diseases. And transfer learning was used to train the corresponding dataset. The experiment results have showed that the F1 values of the four models all reached above 0.93, and the Yolov5x got the best result, which achieved 0.963. Furthermore, the detection accuracy of the four models has reached more than 98%. This shows that the YOLOv5 series network models have great application prospects in the identification of crop diseases. In the near future, the object detection model can be applied to various mobile devices, even unmanned aerial vehicles, which would play a significant role in crop disease prevention.

MCD-Yolov5: Accurate, Real-Time Crop Disease and Pest Identification Approach Using UAVs

As the principal factor affecting global food production, accurate identification of agricultural pests and diseases is crucial in ensuring a sustainable food supply. However, existing methods lack sufficient performance in terms of accuracy and real-time detection of multiple pests and diseases. Accordingly, accurate, efficient, and real-time identification of a wide range of pests and diseases is challenging. To address this, we propose an MCD-Yolov5 with a fusion design that combines multi-layer feature fusion (MLFF), convolutional block attention module CBAM, and detection transformer (DETF). In this model, we optimize the MLFF design to realize the dynamic adjustment of feature weights of the input feature layer to (1) find an appropriate distribution of feature information proportion for the detection task, (2) enhance detection speed by efficiently extracting effective images and effective features through CBAM, and (3) improve feature extraction capability through DETF to compensate for the accuracy problem of multiple pest detection. In addition, we established an unmanned aerial vehicle system (UAV) for crop pest and disease detection to assist in detection and prevention. We validate the performance of the proposed method through an established UAV platform, and five indicators are employed to quantify the performance. MCD-Yolov5 can detect pests and diseases with a large improvement in detection accuracy and detection efficiency, obtaining an 88.12% accuracy. The proposed method and system provide an idea for the effective identification of pests and diseases.

Performance Analysis of Deep Transfer Learning Models for the Automated Detection of Cotton Plant Diseases

Cotton is one of the most important agricultural products and is closely linked to the economic development of Pakistan. However, the cotton plant is susceptible to bacterial and viral diseases that can quickly spread and damage plants and ultimately affect the cotton yield. The automated and early detection of affected plants can significantly reduce the potential spread of the disease. This paper presents the implementation and performance analysis of bacterial blight and curl virus disease detection in cotton crops through deep learning techniques. The automated disease detection is performed through transfer learning of six pre-trained deep learning models, namely DenseNet121, DenseNet169, MobileNetV2, ResNet50V2, VGG16, and VGG19. A total of 1362 images of local agricultural fields and 1292 images from online resources were used to train and validate the models. Image augmentation techniques were performed to increase the dataset diversity and size. Transfer learning was implemented for different image resolutions ranging from 32×32 to 256×256 pixels. Performance metrics such as accuracy, precision, recall, F1 Score, and prediction time were evaluated for each implemented model. The results indicate higher accuracy, up to 96%, for DenseNet169 and ResNet50V2 models when trained on the 256×256 pixels image dataset. The lowest accuracy, 52%, was obtained by the MobileNetV2 model when trained on low-resolution, 32×32, images. The confusion matrix analysis indicates the true-positive prediction rates higher than 91% for fresh leaves, 87% for bacterial blight, and 76% for curl virus detection for all implemented models when trained and tested on an image dataset of 128×128 pixels or higher resolution.

Automated Plant Disease Recognition using Tasmanian Devil Optimization with Deep Learning Model

Plant diseases have devastating effects on crop production, contributing to major economic loss and food scarcity. Timely and accurate recognition of plant ailments is vital to effectual disease management and keeping further spread. Plant disease classification utilizing Deep Learning (DL) has gained important attention recently because of its potential to correct and affect the detection of plant diseases. DL approaches, particularly Convolutional Neural Networks (CNNs) demonstrate that extremely effective in capturing intricate patterns and features in plant leaf images, allowing correct disease classification. In this article, a Tasmanian Devil Optimization with Deep Learning Enabled Plant Disease Recognition (TDODL-PDR) technique is proposed for effective crop management. The TDODL-PDR technique derives feature vectors utilizing the Multi-Direction and Location Distribution of Pixels in Trend Structure (MDLDPTS) descriptor. Besides, the deep Bidirectional Long Short-Term Memory (BiLSTM) approach gets exploited for the plant disease recognition. Finally, the TDO method can be executed to optimize the hyperparameters of the BiLSTM approach. The TDO method inspired by the foraging behaviour of Tasmanian Devils (TDs) effectively explores the parameter space and improves the model's performance. The experimental values stated that the TDODL-PDR model successfully distinguishes healthy plants from diseased ones and accurately classifies different disease types. The automated TDODL-PDR model offers a practical and reliable solution for early disease detection in crops, enabling farmers to take prompt actions to mitigate the spread and minimize crop losses.

Revolutionizing Agriculture: Machine Learning-Driven Crop Recommendations and Disease Detection in Fertilizer Management

Modern agriculture faces a multitude of challenges, including crop failures, disease outbreaks, and suboptimal yields, primarily stemming from the underutilization of advanced farming technologies and a lack of expert guidance. This research proposes a comprehensive solution consisting of three key components: a Crop Disease Detection System, a Fertilizer Recommendation System, and a Crop Suggestion System. The Crop Disease Detection System employs state-of-the-art technology to evaluate crop health by analyzing the condition of plant leaves, enabling early and accurate identification of agricultural diseases. Simultaneously, the Fertilizer Recommendation System leverages soil quality data and environmental factors to provide personalized fertilizer recommendations, optimizing nutrient application. An essential element of this system is a robust soil testing module, recognizing the critical importance of assessing soil quality. Soil fertility evaluation, guided by soil pH measurements, enables precise crop predictions. The proposed system utilizes Machine Learning classification algorithms to predict suitable crops based on essential soil parameters—Phosphorus, Potassium, and Nitrogen levels. It also offers tailored fertilizer recommendations to enhance soil fertility. By implementing these interconnected solutions, this research aims to significantly improve crop yields while reducing crop damage. This holistic approach empowers farmers with the tools and knowledge needed to enhance agricultural productivity and food security. Anticipated outcomes include higher crop yields and a reduced vulnerability of crops to diseases, contributing to a more sustainable and prosperous agricultural sector.

Crop leaf disease detection for beans using ensembled-convolutional neural networks

Abstract Crops’ health is affected by a varied range of diseases. Convenient and precise diagnosis plays a substantial role in preventing the loss of crop quality. In the past decade, deep learning (DL), particularly Convolutional Neural Networks (CNNs), has presented extraordinary performance for diverse applications involving crop disease (CD) detection. In this study, a comparison is drawn for the three pre-trained state-of-art architectures, namely, EfficientNet B0, ResNet50, and VGG19. An ensembled CNN has also been generated from the mentioned CNNs, and its performance has been evaluated over the original coloured, grey-scale, and segmented dataset. K-means clustering has been applied with six clusters to generate the segmented dataset. The dataset is categorized into three classes (two diseased and one healthy class) of bean crop leaves images. The model performance has been assessed by employing statistical analysis relying on the accuracy, recall, F1-score, precision, and confusion matrix. The results have shown that the performance of ensembled CNNs’ has been better than the individual pre-trained DL models. The ensembling of CNNs gave an F1-score of 0.95, 0.93, and 0.97 for coloured, grey-scale, and segmented datasets, respectively. The predicted classification accuracy is measured as: 0.946, 0.938, and 0.971 for coloured, grey-scale, and segmented datasets, respectively. It is observed that the ensembling of CNNs performed better than the individual pre-trained CNNs.

The potential of UAV and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in Ethiopia

Very high (spatial and temporal) resolution satellite (VHRS) and high-resolution unmanned aerial vehicle (UAV) imagery provides the opportunity to develop new crop disease detection methods at early growth stages with utility for early warning systems. The capability of multispectral UAV, SkySat and Pleiades imagery as a high throughput phenotyping (HTP) and rapid disease detection tool for wheat rusts is assessed. In a randomized trial with and without fungicide control, six bread wheat varieties with differing rust resistance were monitored using UAV and VHRS. In total, 18 spectral features served as predictors for stem and yellow rust disease progression and associated yield loss. Several spectral features demonstrated strong predictive power for the detection of combined wheat rust diseases and the estimation of varieties’ response to disease stress and grain yield. Visible spectral (VIS) bands (Green, Red) were more useful at booting, shifting to VIS–NIR (near-infrared) vegetation indices (e.g., NDVI, RVI) at heading. The top-performing spectral features for disease progression and grain yield were the Red band and UAV-derived RVI and NDVI. Our findings provide valuable insight into the upscaling capability of multispectral sensors for disease detection, demonstrating the possibility of upscaling disease detection from plot to regional scales at early growth stages.

Applications of internet of things (IoT) and sensors technology to increase food security and agricultural Sustainability: Benefits and challenges

Agriculture must overcome escalating problems in order to feed a growing population while preserving the environment and natural resources. Recently, it has become clear that sensors and the Internet of Things (IoT) are effective tools for boosting agricultural sustainability and food security. This study provides insights into the global market size for smart agriculture in future years from 2021 to 2030, In addition, this research offered four levels of the IoT architecture for smart agriculture: the perception or sensing and actuator layer, the network layer, the cloud layer, and the application layer. The state of the art in IoT and sensor technologies for agriculture is examined in this review paper, along with some of their potential uses, including 1) irrigation monitoring systems, 2) fertilizer administration, 3) crop disease detection, 4) monitoring (yield monitoring, quality monitoring, processing monitoring logistic monotoring), forecasting, and harvesting, 5) climate conditions monitoring, and 6) fire detection. Additionally, this review offers a number of sensors for agriculture that can detect parameters like soil NPK, moisture, nitrate, pH, electrical conductivity, CO2, temperature, humidity, light, weather station, water level, livestock, plant disease, smoke, flame, flexible wearable. Subsequently, this study highlights the advantages of IoT in smart agriculture, including superior efficiency, expansion, reduced resources, cleaner method, agility, and product quality improvement. However, there are still issues that need to be resolved in order for IoT technology to be used in agriculture where covered in this paper, and also provide insights into future research directions and opportunities. This study will contribute to helping future readers and researchers to better understand the state of academic achievement in this subject.

Machine learning-based spectral and spatial analysis of hyper- and multi-spectral leaf images for Dutch elm disease detection and resistance screening

Diseases caused by invasive pathogens are an increasing threat to forest health, and early and accurate disease detection is essential for timely and precision forest management. The recent technological advancements in spectral imaging and artificial intelligence have opened up new possibilities for plant disease detection in both crops and trees. In this study, Dutch elm disease (DED; caused by Ophiostoma novo-ulmi,) and American elm (Ulmus americana) was used as example pathosystem to evaluate the accuracy of two in-house developed high-precision portable hyper- and multi-spectral leaf imagers combined with machine learning as new tools for forest disease detection. Hyper- and multi-spectral images were collected from leaves of American elm genotypes with varied disease susceptibilities after mock-inoculation and inoculation with O. novo-ulmi under greenhouse conditions. Both traditional machine learning and state-of-art deep learning models were built upon derived spectra and directly upon spectral image cubes. Deep learning models that incorporate both spectral and spatial features of high-resolution spectral leaf images have better performance than traditional machine learning models built upon spectral features alone in detecting DED. Edges and symptomatic spots on the leaves were highlighted in the deep learning model as important spatial features to distinguish leaves from inoculated and mock-inoculated trees. In addition, spectral and spatial feature patterns identified in the machine learning-based models were found relative to the DED susceptibility of elm genotypes. Though further studies are needed to assess applications in other pathosystems, hyper- and multi-spectral leaf imagers combined with machine learning show potential as new tools for disease phenotyping in trees.

Next-generation methods for early disease detection in crops.

Plant pathogens are commonly identified in the field by the typical disease symptoms that they can cause. The efficient early detection and identification of pathogens are essential procedures to adopt effective management practices that reduce or prevent their spread in order to mitigate the negative impacts of the disease. In this review, the traditional and innovative methods for early detection of the plant pathogens highlighting their major advantages and limitations are presented and discussed. Traditional techniques of diagnosis used for plant pathogen identification are focused typically on the DNA, RNA (when molecular methods), and proteins or peptides (when serological methods) of the pathogens. Serological methods based on mainly enzyme-linked immunosorbent assay (ELISA) are the most common method used for pathogen detection due to their high-throughput potential and low cost. This technique is not particularly reliable and sufficiently sensitive for many pathogens detection during the asymptomatic stage of infection. For non-cultivable pathogens in the laboratory, nucleic acid-based technology is the best choice for consistent pathogen detection or identification. Lateral flow systems are innovative tools that allow fast and accurate results even in field conditions, but they have sensitivity issues to be overcome. PCR assays performed on last-generation portable thermocyclers may provide rapid detection results in situ. The advent of portable instruments can speed pathogen detection, reduce commercial costs, and potentially revolutionize plant pathology. This review provides information on current methodologies and procedures for the effective detection of different plant pathogens. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Early Pathogen Prediction in Crops Using Nano Biosensors and Neural Network-Based Feature Extraction and Classification

The most prevalent microbe-caused issues that reduce agricultural output globally are viral and bacterial infections. It is currently quite challenging to identify pathogens due to the current living situation. Biosensors have become the standard for monitoring microbial and viral macromolecules. Disease diagnosis is improved by following the nanoparticles released by infections. Since the sensors' data includes different learning patterns, Machine Learning (ML) methods are used to analyze and interpret it. This research paper aimed to study whether Near-infrared (nIR) and Red, Green, and Blue (RGB) imaging might be used to define and detect Plant Disease (PD) using Convolutional Neural Network (CNN)-based Feature Extraction (FE) and Feature Classification (FC). A home-built Single-Walled Carbon NanoTube (SWCNTs) implemented with a Deoxyribonucleic Acid (DNA) aptamer that binds to a Hemi (HeApt + DNA + SWCNT) sensing device was used to analyze near-infrared (nIR) and RGB images of tea plant leaf samples. Three labels are extracted from the nIR + RGB using a Wasserstein Distance (WD)-based Feature Extraction Model (FEM), and then all those labels are loaded into the proposed CNN model to ensure precise classification. The proposed Wasserstein Distance-to-Convolutional Neural Network (WD2CNN) model was compared to different CNN architectures on the same dataset, achieving the highest accuracy of 98.72%. It is also the most computationally efficient, with the shortest average time per epoch. The model demonstrates high performance and efficiency in classifying biosensor images, which could aid in the early detection and prevention of Crop Diseases (CD).

An Interpretable High-Accuracy Method for Rice Disease Detection Based on Multisource Data and Transfer Learning.

With the evolution of modern agriculture and precision farming, the efficient and accurate detection of crop diseases has emerged as a pivotal research focus. In this study, an interpretative high-precision rice disease detection method, integrating multisource data and transfer learning, is introduced. This approach harnesses diverse data types, including imagery, climatic conditions, and soil attributes, facilitating enriched information extraction and enhanced detection accuracy. The incorporation of transfer learning bestows the model with robust generalization capabilities, enabling rapid adaptation to varying agricultural environments. Moreover, the interpretability of the model ensures transparency in its decision-making processes, garnering trust for real-world applications. Experimental outcomes demonstrate superior performance of the proposed method on multiple datasets when juxtaposed against advanced deep learning models and traditional machine learning techniques. Collectively, this research offers a novel perspective and toolkit for agricultural disease detection, laying a solid foundation for the future advancement of agriculture.

Enhancing Crop and Crop Disease detection along with Classification using Machine Learning Techniques

Enhancing Crop and Crop Disease detection along with Classification using Machine Learning Techniques