Crop Disease Detection Research Articles

Harmonizing Cross View Image Transformation Through Local and Global Insights- A Review

Hyperspectral and multispectral information processing systems and technologies have demonstrated their value in enhancing agricultural productivity and practices by providing farmers and crop managers with valuable information on factors influencing crop condition and growth. These technologies play a crucial role in various agricultural applications, such as crop management, crop yield forecasting, crop disease detection, and monitoring soil, water, and land usage. To enhance the process of agriculture through effective crop management and assistance to farmer, using an advanced image transformation techniques the study delves into the exploration of techniques for harmonizing cross-view image transformation, with a focus on integrating both local and global insights. Further the study proposes the application of the 2D-DWT (Two-Dimensional Discrete Wavelet Transform) technique for image data preprocessing and the Extreme Learning Machine (ELM) algorithm for image classification in the context of hyperspectral images. Hyperspectral information sensing allows for the coverage of the electromagnetic spectrum in a single acquisition with several hundred spectral bands, resulting in a data cube containing significant spectral and spatial information ELM is particularly effective for classification problems because of its quick training time and capacity to operate with hidden nodes whose parameters are randomly assigned rather than modified iteratively. This comprehensive review aims to provide valuable insights and a critical analysis of the suggested method, shedding light on its potential contributions to the advancement of image transformation techniques for agricultural development.

Crop Disease Detection and Recommendation of Pesticides and Secondary Crops using Deep Learning

Abstract: In India, so many people belong to an agricultural background. Nowadays crop disease detection is a very crucial topic in analysis. Crop diseases are one of the major issues for the depletion of the quality and quantity of the crop. So, detecting and giving suitable solutions for crop disease is very important to increase the quality and quantity of the crops. In this research work, we use Deep Learning Algorithms such as Convolutional Neural Networks to train the crop disease dataset and we use image pre-processing techniques to pre-process the image and to detect the crop disease. In this project, we selected the convolution neural network to get better accuracy for the system. Here, crop leaf images that are affected by diseases are given as input to the system, and after pre-processing that image, it will detect whether there is a disease or not for the crop. If a disease is detected then it will give the name of the disease. After, it will show the suitable pesticides and chemicals that reduce the crop diseases for good yield of the crop. In this project, we also suggest to the farmers some alternative crops that provide various benefits such as improve yield, soil fertility, pest management, etc. These alternative crops often have unique characteristics that complement the yield and growth of main crops

Machine Learning Algorithms for Early Detection of Legume Crop Disease

Background: Legume crops are an essential component of global agriculture and are widely supplied for human consumption, livestock feed and soil improvement due to their vital nutritional nature. The economic and nutritional significance of legumes is threatened by a multitude of diseases that can cause substantial yield losses. Traditional methods for disease detection, relying on visual inspection, are often subjective and inefficient, leading to delayed intervention. Methods: This study investigates the utilization of machine learning algorithms for the early identification of diseases affecting legume crops. A comprehensive evaluation is conducted on machine learning algorithms, namely Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) with respect to the domain of disease detection. Through a comparative analysis of their performance across different environmental conditions and phases of crop development, this study also explores their strengths and weaknesses. Result: The findings and comparative examination offered significant perspectives on the potential of machine learning algorithms in the realm of early legume crop disease detection. In addition to enhancing crop health and disease management, the research provides support for sustainable agricultural practices and possesses the capacity to augment environmental sustainability and food security through the application of machine learning techniques.

Classification of Plant Leaf Disease Recognition Based on Self-Supervised Learning

Accurate identification of plant diseases is a critical task in agricultural production. The existing deep learning crop disease recognition methods require a large number of labeled images for training, limiting the implementation of large-scale detection. To overcome this limitation, this study explores the application of self-supervised learning (SSL) in plant disease recognition. We propose a new model that combines a masked autoencoder (MAE) and a convolutional block attention module (CBAM) to alleviate the harsh requirements of large amounts of labeled data. The performance of the model was validated on the CCMT dataset and our collected dataset. The results show that the improved model achieves an accuracy of 95.35% and 99.61%, recall of 96.2% and 98.51%, and F1 values of 95.52% and 98.62% on the CCMT dataset and our collected dataset, respectively. Compared with ResNet50, ViT, and MAE, the accuracies on the CCMT dataset improved by 1.2%, 0.7%, and 0.8%, respectively, and the accuracy of our collected dataset improved by 1.3%, 1.6%, and 0.6%, respectively. Through experiments on 21 leaf diseases (early blight, late blight, leaf blight, leaf spot, etc.) of five crops, namely, potato, maize, tomato, cashew, and cassava, our model achieved accurate and rapid detection of plant disease categories. This study provides a reference for research work and engineering applications in crop disease detection.

Advanced deep learning approach for enhancing crop disease detection in agriculture using hyperspectral imaging

Crop diseases pose significant challenges to global food security and agricultural sustainability. Timely and accurate disease detection is crucial for effective disease management and minimizing crop losses. In recent years, hyperspectral imaging has emerged as a promising technology for non-destructive and early disease detection in crops. This research paper presents an advanced deep learning approach for enhancing crop disease detection using hyperspectral imaging. The primary objective is to propose a hybrid Autoencoder-Generative Adversarial Network (AE-GAN) model that effectively extracts meaningful features from hyperspectral images and addresses the limitations of existing techniques. The hybrid AE-GAN model combines the strengths of the Autoencoder for feature extraction and the Generative Adversarial Network for synthetic sample generation. Through extensive evaluation, the proposed model outperforms existing techniques, achieving exceptional accuracy in crop disease detection. The results demonstrate the superiority of the hybrid AE-GAN model, offering substantial advantages in terms of feature extraction, synthetic sample generation, and utilization of spatial and spectral information. The proposed model’s contributions to sustainable agriculture and global food security make it a valuable tool for advancing agricultural practices and enhancing crop health monitoring. With its promising implications, the hybrid AE-GAN model represents a significant advancement in crop disease detection, paving the way for a more resilient and food-secure future.

Advancements in Agricultural Technology: A Comprehensive Review of Machine Learning and Deep Learning Approaches for Crop Management and Disease Detection

Traditional agricultural practices often lack personalized guidance for farmers, which leads to poor crop choices, wasteful fertiliser use, and inadequate disease control. As a result, sustainability and productivity decline. In order to address these issues, this review examines the developments in agricultural technology, with a focus on the fusion of deep learning and machine learning techniques. We look at techniques like ensemble modelling, which optimises crop selection and fertiliser consumption depending on soil properties, and advanced image processing methods, which use leaf images to diagnose plant diseases accurately. Our goal in conducting this study is to provide light on how agricultural technology is developing and how it affects contemporary farming methods. We emphasise the major trends, approaches, and developments in the field of agricultural technology by examining a variety of research papers covering many elements of the subject. Our thorough analysis highlights the potential of deep learning and machine learning techniques to transform agricultural disease diagnosis, fertiliser selection, and crop management. With this investigation, we hope to add to the current conversation about using technology to solve the urgent problems facing the agriculture industry and, in the process, open the door for more effective and sustainable farming methods in the future

Plant Disease Detection Using Image Processing and Machine Learning

One of the important and tedious task in agricultural practices is detection of disease on crops. It requires huge time as well as skilled labor. This paper proposes a smart and efficient technique for detection of crop disease which uses computer vision and machine learning techniques

Machine Learning based Disease and Pest detection in Agricultural Crops

INTRODUCTION: Most Indians rely on agricultural work as their primary means of support, making it an essential part of the country’s economy. Disasters and the expected loss of farmland by 2050 as a result of global population expansion raise concerns about food security in that year and beyond. The Internet of Things (IoT), Big Data and Analytics are all examples of smart agricultural technologies that can help the farmers enhance their operation and make better decisions.
 OBJECTIVES: In this paper, machine learning based system has been developed for solving the problem of crop disease and pest prediction, focussing on the chilli crop as a case study.
 METHODS: The performance of the suggested system has been assessed by employing performance metrics like accuracy, Mean Squared Error (MSE), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).
 RESULTS: The experimental results reveals that the proposed method obtained accuracy of 0.90, MSE of 0.37, MAE of 0.15, RMSE of 0.61
 CONCLUSION: This model will predict pests and diseases and notify farmers using a combination of the Random Forest Classifier, the Ada Boost Classifier, the K Nearest Neighbour, and Logistic Regression. Random Forest is the most accurate model.

Artificial intelligence and Internet of Things‐enabled decision support system for the prediction of bacterial stalk root disease in maize crop

AbstractAlthough the Internet of Things (IoT) has been considered one of the most promising technologies to automate various daily life activities, that is, monitoring and prediction, it has become extremely useful for problem solving with the introduction and integration of artificial intelligence (AI)‐enabled smart learning methodologies. Therefore, due to their overwhelming characteristics, AI‐enabled IoTs have been used in different application environments, such as agriculture, where detection, prevention (if possible), and prediction of crop diseases, especially at the earliest possible stage, are desperately required. Bacterial stalk root is a common disease of tomatoes that severely affects its production and yield if necessary measures are not taken. In this article, AI and an IoT‐enabled decision support system (DSS) have been developed to predict the possible occurrence of bacterial stalk root diseases through a sophisticated technological infrastructure. For this purpose, Arduino agricultural boards, preferably with necessary embedded sensors, are deployed in the agricultural field of maize crops to capture valuable data at a certain time interval and send it to a centralized module where AI‐based DSS, which is trained on an equally similar data set, is implemented to thoroughly examine captured data values for the possible occurrence of the disease. Additionally, the proposed AI‐ and IoT‐enabled DSS has been tested on benchmark data sets, that is, freely available online, along with real‐time captured data sets. Both experimental and simulation results show that the proposed scheme has achieved the highest accuracy level in timely prediction of the underlined disease. Finally, maize crop plots with the proposed system have significantly increased the yield (production) ratio of crops.

Plant Leaf Disease Identification and Classification using Deep Learning Technique

In countries like India, whose important occupation is agriculture, face a huge loss when the crops get affected by any type of disease. These diseases attack the crops in various stages and can destroy the entire production. Since most diseases are transmitted from one crop to another there is an essential requirement to detect the type of disease in the early stage so that farmers can take the required action to “save the crops” and production. However, detection of the kind of disease in a huge amount of crops is very difficult for farmers, and sometimes it becomes more difficult due to lake of knowledge about the disease since there are various kinds of diseases. The automated detection of crop disease with images has been done using many classification techniques, such as k- Nearest Neighbor Classifier, Probabilistic Neural Network, Genetic Algorithm, Support Vector Machine, and Main Component Analysis, Artificial Neural Network, and Fuzzy Logic. In this paper to improve detection capability, CNN has been used with an available database having sufficient knowledge of the disease. In this simulation, analysis of CNN-based leaf disease detection has been done for different values of learning rate and with different training algorithms. K-fold cross-validation has been used for the validation of the classifier. With this configuration, ~90% accuracy has been achieved

A COMPREHENSIVE REVIEW ON SUITABLE IMAGE PROCESSING AND MACHINE LEARNING TECHNIQUE FOR DISEASE IDENTIFICATION OF TOMATO AND POTATO PLANTS

Agriculture plays a vital role in the Sri Lankan economy. Cultivation of crops like tomatoes and potatoes which is being used as a fruit and vegetable will contribute significantly to farmer’s earnings. However, tomato and potato crop faces numerous challenges, such as disease infection can significantly reduce the yield. Early identification of these diseases is crucial for implementing timely interventions and minimizing the potential damage. The current study aims to analyze existing methodologies and identify the most effective approaches for disease detection in tomato and potato crops. Image processing techniques enable the extraction of relevant features from digital images of infected plants, aiding in the identification of diseases accurately. Additionally, machine learning algorithms have proven to be valuable tools for analyzing complex datasets and distinguishing between healthy and diseased plants. The review explores various image processing techniques, including image segmentation, feature extraction, and classification algorithms (support vector machines, random forests, and convolutional neural networks). Suitability of these techniques assured that the disease identification in tomato plants based on their accuracy, efficiency, and robustness. The findings of this review will serve as a foundation for the development of a software application to identify tomato leaf diseases accurately. By enabling accurate disease identification and management, this study seeks to enhance the resilience and productivity of tomato and potato cultivation in Sri Lanka, contributing to the sustainable growth of the agricultural sector.

CNN-based plant disease recognition using colour space models

ABSTRACT The growing population increases the demand for agricultural production. Owing to some plant diseases, this production is continuously decreasing. Detecting and recognising these diseases at the initial stage is crucial for the sake of high yields from crops and plants. The conventional methods involve manual detection, which can be error-prone and laborious. Automatic recognition paves a way for the regular monitoring of huge areas of crops in less time. The deep learning-based approaches have entered in a big way in the detection of diseases in plants and crops because of their ability to overcome the limitation of regular manual monitoring. The use of colour space models helps demarcate the affected area ofimages of plants and crops prior to inputting them to the CNN model so as to improve the detection accuracy. For this, different colour space models that include RGB, HSL, HSV, LAB, LUV, XYZ and YUV are investigated. However, HSL-CNN joint model performs the best by achieving 98.97% accuracy, 98.61% precision, and 99.28% F1-score.

A Systematic Review of Geographic Information Systems (GIS) in Agriculture for Evidence-Based Decision Making and Sustainability

The aim of this study was to consolidate current information on the utilization of Geographic Information Systems (GIS) and Remote Sensing (RS) in the agricultural sector, with a focus on their role in promoting evidence-based policies and practices to enhance agricultural sustainability. Additionally, this review sought to identify the challenges hindering the widespread adoption of GIS and RS applications, particularly in low- and middle-income nations. This study employed the methodology of systematic literature review. The findings indicate that the utilization of GIS technology in the agricultural sector has experienced a notable increase over the past few years. The primary areas of use for GIS that have been identified encompass crop yield estimation, assessment of soil fertility, monitoring of cropping patterns, evaluation of drought conditions, detection and management of pests and crop diseases, implementation of precision agriculture techniques, and management of fertilizer and weed control. GIS technology possesses the capacity to augment the sustainability of agriculture by incorporating the spatial aspect of agricultural practices into agricultural policies. Furthermore, the potential of GIS in facilitating evidence-based decision making is expanding. Given the escalating peril of climate change on agriculture and food security, there exists a heightened imperative to include GIS into policy formulation and decision-making processes to enhance the sustainability of agricultural practices. The findings of this study might be beneficial in informing the development of policies that effectively integrate sustainable and climate-smart practices in agriculture.

Seeding a Sustainable Future: Navigating the Digital Horizon of Smart Agriculture

Agriculture is essential to the existence of the human race, as well as the foundation of our civilization, because it provides food, fuel, fiber, and other resources necessary for survival; however, it is facing critical challenges due to anthropogenic climate change, which hampers food and nutritional security. Consequently, the agriculture industry must adjust to farming issues, such as the shift in global temperatures and environmental degradation, the scarcity of farm workers, population growth, and dietary changes. Several measures have been implemented to enhance agricultural productivity, including plant breeding, genetic engineering, and precision agriculture. In recent years, the world has witnessed the burgeoning development of novel scientific innovations and technological advancements enabled by drones, smart sensors, robotics, and remote sensing, resulting in a plethora of revolutionary methods that can be applied to real-time crop modeling, high-throughput phenotyping, weather forecasting, yield prediction, fertilizer application, disease detection, market trading, farming practices, and other environmental practices vital to crop growth, yield, and quality. Furthermore, the rise in big data, advanced analytics, falling technology costs, faster internet connections, increased connectivity, and increases in computational power are all part of the current digitalization wave that has the potential to support commercial agriculture in achieving its goals of smart farming, resilience, productivity, and sustainability. These technologies enable efficient monitoring of crops, soil, and environmental conditions over large areas, providing farmers with data to support precise management that optimizes productivity and minimizes environmental impacts. Though smart farming has significant potential, challenges like high implementation costs, data security concerns, and inadequate digital literacy among farmers remain. In summary, agriculture is rapidly transforming from conventional to digital farming, offering global solutions, efficient resource utilization, and minimized input costs while fostering farmer livelihoods and economic growth. Delivering a comprehensive view of how technology could help in tackling critical issues like environmental degradation and threatened world biodiversity, this perspective emphasizes the perks of digitalization. Future advancements may involve data encryption, digital literacy, and particular economic policies.

Enhanced disease detection through image fusion in solanum tuberosum l.

Disease detection in agricultural crops, such as Solanum tuberosum L. (potato), is of utmost importance to ensure crop health and maximize yield. Traditional methods for disease detection in potatoes rely on manual inspection, which can be time-consuming and prone to human error. Image processing and machine learning techniques have shown promise in automating disease detection processes. This study proposes a novel approach for disease detection in Solanum tuberosum L. by leveraging image fusion techniques. The proposed method involves the fusion of multiple images of potato plants, acquired using different sensors or imaging modalities, to create a comprehensive and informative representation of the crop. Image fusion methods, such as discrete wavelet transform and continuous wavelet transform, are employed to combine the spectral and spatial information from the images effectively. The different image fusion rule is applied to the input images and resultant fused images, where relevant features are extracted to distinguish between healthy and diseased potato plants. The training dataset comprises diverse samples of both healthy and diseased potato plants, captured under various environmental conditions and disease stages. The performance of the proposed disease detection system is evaluated using standard metrics such as entropy. The results demonstrate the effectiveness of the image fusion approach in accurately identifying diseased potato plants, achieving high detection accuracy and generalization capabilities. The potential benefits of this paper include providing farmers and agricultural experts with an efficient and reliable tool for early disease detection in potato crops. Early detection can lead to timely intervention, minimizing crop losses and optimizing agricultural practices. The proposed methodology also lays the groundwork for future research in using advanced image processing techniques and machine learning algorithms for disease detection in other agricultural crops, contributing to the overall improvement of crop management and food security.

Utilizing Aerial Imagery and Deep Learning Techniques for Identifying Banana Plants Diseases

The primary agricultural pursuit in Malaysia centres around banana cultivation; however, this vital crop faces the daunting challenge of multiple diseases that hinder its growth. The adverse consequences of these diseases extend beyond the farms to impact the nation’s economy. To empower farmers with the tools to promptly identify and categorize these diseases, image processing techniques offer a valuable solution. This research leverages deep learning Convolutional Neural Networks (CNN) implemented through MATLAB in conjunction with a DJI drone. By harnessing this technology, the system can automatically detect and classify major banana diseases. The study meticulously fine-tuned several hyperparameters to achieve impressive training and testing accuracy levels. The results revealed that the model attained its highest training accuracy of 81.27% at epoch 8 and its lowest accuracy of 78.40% at epoch 4, demonstrating its potential to aid in early disease detection and classification in banana crops.

CSXAI: a lightweight 2D CNN-SVM model for detection and classification of various crop diseases with explainable AI visualization.

Plant diseases significantly impact crop productivity and quality, posing a serious threat to global agriculture. The process of identifying and categorizing these diseases is often time-consuming and prone to errors. This research addresses this issue by employing a convolutional neural network and support vector machine (CNN-SVM) hybrid model to classify diseases in four economically important crops: strawberries, peaches, cherries, and soybeans. The objective is to categorize 10 classes of diseases, with six diseased classes and four healthy classes, for these crops using the deep learning-based CNN-SVM model. Several pre-trained models, including VGG16, VGG19, DenseNet, Inception, MobileNetV2, MobileNet, Xception, and ShuffleNet, were also trained, achieving accuracy ranges from 53.82% to 98.8%. The proposed model, however, achieved an average accuracy of 99.09%. While the proposed model's accuracy is comparable to that of the VGG16 pre-trained model, its significantly lower number of trainable parameters makes it more efficient and distinctive. This research demonstrates the potential of the CNN-SVM model in enhancing the accuracy and efficiency of plant disease classification. The CNN-SVM model was selected over VGG16 and other models due to its superior performance metrics. The proposed model achieved a 99% F1-score, a 99.98% Area Under the Curve (AUC), and a 99% precision value, demonstrating its efficacy. Additionally, class activation maps were generated using the Gradient Weighted Class Activation Mapping (Grad-CAM) technique to provide a visual explanation of the detected diseases. A heatmap was created to highlight the regions requiring classification, further validating the model's accuracy and interpretability.

Smart Agriculture Solutions: Harnessing AI and IoT for Crop Management

This review article, “Smart Agriculture Solutions: Harnessing AI and IoT for Crop Management,” explores the transformative impact of digital technologies in agriculture. It focuses on the integration of the Internet of Things (IoT), Cloud Computing, and Artificial Intelligence (AI) in enhancing sustainable farming practices. IoT technologies enable real-time monitoring of agricultural conditions using sensors and drones, while Cloud Computing provides robust data processing capabilities. AI leverages this data for predictive analytics in crop yields, disease detection, and automation of farming processes, optimizing resource use and adapting to climate change. The paper also examines the synergy of renewable energy sources with AI to power smart farming systems, proposing innovative solutions for energy-efficient, AI-driven agriculture. It highlights AI’s extensive role in improving crop management, from seed selection to soil analysis and crop health monitoring, thereby increasing yield quality and fostering sustainability. Furthermore, the article discusses the significance of space technologies in smart agriculture, showcasing advancements like drones and big data analytics. These innovations are crucial in developing economies, addressing global challenges like food security and contributing to economic growth. This review underlines the necessity of AI and IoT in revolutionizing agricultural methodologies, ensuring efficiency and sustainability in the face of a rapidly growing global population.

A data-driven fusion of deep learning and transfer learning for orange disease classification

In agriculture, early detection of crop diseases is imperative for sustainability and maximizing yields. Rooted in Agriculture 4.0, our innovative approach combines pre-trained Convolutional Neural Networks (CNNs) models with data-driven solutions to address global challenges related to water scarcity. By integrating the combined L1/L2 regularization technique to our model layers, we enhance their flexibility, reducing the risk of the overfitting effect of the model. In the orange dataset used in our experiments, we have 1790 orange images, including a class of fresh oranges and three disease categories. Applied on this dataset for classification, our model exhibits notable performance, namely 92.17% for CNN and 97.28% for ResNet-50 model. Evaluated across metrics like accuracy, precision, recall, F1-score, confusion matrix, and cross validation, our approach surpasses traditional classifiers, significantly contributing to smart agricultural and global food resilience amidst mounting water scarcity pressures.

Role of Machine Learning Algorithms in Analysis of Crop Disease Detection 

Role of Machine Learning Algorithms in Analysis of Crop Disease Detection