Application of machine learning in detection of blast disease in South Indian rice crops

It is a well-known fact that the quality and quantity of the rice crop is reduced due to plant disease. This paper proposes rice blast disease detection mechanism using Machine learning algorithm, to identify the disease in the early stage of the crop cultivation. The proposed method would find the blast disease and reduce the crop loss and hence increase the rice agriculture production in an effective manner. The images of the paddy field are captured and eight features are extracted to distinguish the healthy and the disease affected leaves. The proposed machine learning based classification methodology includes KNN and ANN. The performance of these two classification techniques is compared using an appropriate confusion matrix. The simulation results show that KNN based classification method provides an accuracy of 85% for the blast affected leaf images and 86% for the normal leaf images. The accuracy is improved to 99% and 100% respectively for the ANN based classification mechanisms

Numerical Modeling and Design of Machine Learning Based Paddy Leaf Disease Detection System for Agricultural Applications

In order to satisfy the insatiable need for ever more bountiful harvests on the global market, the majority of countries deploy cutting-edge technologies to increase agricultural output. Only the most cutting-edge technologies can ensure an appropriate pace of food production. Abiotic stress factors that can affect plants at any stage of development include insects, diseases, drought, nutrient deficiencies, and weeds. On the amount and quality of agricultural production, this has a minimal effect. Identification of plant diseases is therefore essential but challenging and complicated. Paddy leaves must thus be closely watched in order to assess their health and look for disease symptoms. The productivity and production of the post-harvest period are significantly impacted by these illnesses. To gauge the severity of plant disease in the past, only visual examination (bare eye observation) methods have been employed. The skill of the analyst doing this analysis is essential to the caliber of the outcomes. Due to the large growing area and need for ongoing human monitoring, visual crop inspection takes a long time. Therefore, a system is required to replace human inspection. In order to identify the kind and severity of plant disease, image processing techniques are used in agriculture. This dissertation goes into great length regarding the many ailments that may be detected in rice fields using image processing. Identification and classification of the four rice plant diseases bacterial blight, sheath rot, blast, and brown spot are important to enhance yield. The other communicable diseases, such as stem rot, leaf scald, red stripe, and false smut, are not discussed in this paper. Despite the increased accuracy they offer, the categorization and optimization strategies utilized in this work lead it to take longer than typical to finish. It was evident that employing SVM techniques enabled superior performance results, but at a cost of substantial effort. K-means clustering is used in this paper segmentation process, which makes figuring out the cluster size, or K-value, more challenging. This clustering method operates best when used with images that are comparable in size and brightness. However, when the images have complicated sizes and intensity values, clustering is not particularly effective

Machine Learning-Based Algorithms for the Detection of Leaf Disease in Agriculture Crops

Identifying plant leaves early on is key to preventing catastrophic outbreaks. An important studyarea is automatic disease detection in plants. Fungi, bacteria, and viruses are the main culprits in most plantillnesses. The process of choosing a classification method is always challenging because the quality of the results can differ depending on the input data. K-Nearest Neighbor Classifier (KNN), Probabilistic NeuralNetwork (PNN), Genetic Algorithm, Support Vector Machine (SVM) and Principal Component Analysis,Artificial Neural Network (ANN), and Fuzzy Logic are a few examples of diverse classification algorithms.Classifications of plant leaf diseases have many uses in a variety of industries, including agriculture andbiological research. Presymptomatic diagnosis and crop health information can aid in the ability to managepathogens through proper management approaches. Convolutional neural networks (CNNs) are the mostwidely used DL models for computer vision issues since they have proven to be very effective in tasks likepicture categorization, object detection, image segmentation, etc. The experimental findings demonstrate theproposed model's superior performance to pre-trained models such as VGG16 and InceptionV3. The range ofcategorization accuracy is 76% to 100%, based on

A Novel Paddy Leaf Disease Detection Framework using Optimal Leaf Disease Features in Adaptive Deep Temporal Context Network

Since paddy has become the staple food for all human beings, crop productivity is highly demanded. Nowadays, the agriculture industry faces the leaf disease issue as the insect or pests affects the plant leaves to hinder further growth. Owing to this, the productivity gets affected that makes the farmers have economic loss. In earlier time, several methods have been explored to detect the disease significantly. However, such methods become more time consuming, structure complexity and other issues. To alleviate such complex, a new paddy leaf disease detection model is proposed using adaptive methodology. Initially, images related with paddy leaf are gathered from standard resources and offered as the input to segmentation region. Here, segmentation is performed by Fuzzy C-Means (FCM) to get the abnormal regions. Then, the segmented images are fed to ensemble feature extraction region to attain different features like deep, textural, morphological, and color features. Further, the acquired ensemble features are provided to concatenation phase to obtain the concatenate features and the optimal features are selected by the Fire Hawk Optimizer (FHO). Finally, the optimal features are subjected to paddy leaf detection phase, where leaf disease will be detected by Adaptive Deep Temporal Context Network (ADTCN), where the parameters are tuned by the FHO. Hence, the developed model secures efficient leaf disease detection rate than the classical techniques in the experiential analysis

Plant Disease Detection using Deep Learning in Banana and Sunflower

In recent years plant disease detection and classification is finding a lot of scope in the field of agriculture. The use of image pre-processing along with deep learning techniques is making the role of farmers easy in the process of plant leaf disease detection. In this paper we propose a deep learning technique, ResNet-50 for the identification and classification of leaf diseases mainly in banana and sunflower. Images for the training and testing purpose are collected by visiting the farms and from village dataset for normal, leaf spot, leaf blight, powdery mildew, bunchy top, sigatoka, panama wilt. Pre-processing is done to remove eliminate the noise in the image by converting the RGB input to HSV image. Binary pictures are retrieved to separate the diseased and unaffected portions based on the hue and saturation components. A clustering method is utilized to separate the diseased region from the normal portion and the background. Classification of the disease is carried out using ResNet-50 algorithm. The experimental results obtained are compared with CNN, machine learning algorithms like SVM, KNN, DT and Ensemble algorithm like RF and XG booster. The proposed algorithm provided maximum efficiency compared to other algorithms

Artificial Neural Networks in Agriculture

Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible

Smart Farm-Care using a Deep Learning Model on Mobile Phones

Deep learning and its models have provided exciting solutions in various image processing applications like image segmentation, classification, labeling, etc., which paved the way to apply these models in agriculture to identify diseases in agricultural plants. The most visible symptoms of the disease initially appear on the leaves. To identify diseases found in leaf images, an accurate classification system with less size and complexity is developed using smartphones. A labeled dataset consisting of 3171 apple leaf images belonging to 4 different classes of diseases, including the healthy ones, is used for classification. In this work, four variants of MobileNet models - pre-trained on the ImageNet database, are retrained to diagnose diseases. The model’s variants differ based on their depth and resolution multiplier. The results show that the proposed model with 0.5 depth and 224 resolution performs well - achieving an accuracy of 99.6%. Later, the K-means algorithm is used to extract additional features, which helps improve the accuracy to 99.7% and also measures the number of pixels forming diseased spots, which helps in severity prediction. Doi: 10.28991/ESJ-2023-07-02-013 Full Text: PD

Methods for Detecting and Classifying Weeds, Diseases and Fruits Using AI to Improve the Sustainability of Agricultural Crops: A Review

The rapid growth of the world’s population has put significant pressure on agriculture to meet the increasing demand for food. In this context, agriculture faces multiple challenges, one of which is weed management. While herbicides have traditionally been used to control weed growth, their excessive and random use can lead to environmental pollution and herbicide resistance. To address these challenges, in the agricultural industry, deep learning models have become a possible tool for decision-making by using massive amounts of information collected from smart farm sensors. However, agriculture’s varied environments pose a challenge to testing and adopting new technology effectively. This study reviews recent advances in deep learning models and methods for detecting and classifying weeds to improve the sustainability of agricultural crops. The study compares performance metrics such as recall, accuracy, F1-Score, and precision, and highlights the adoption of novel techniques, such as attention mechanisms, single-stage detection models, and new lightweight models, which can enhance the model’s performance. The use of deep learning methods in weed detection and classification has shown great potential in improving crop yields and reducing adverse environmental impacts of agriculture. The reduction in herbicide use can prevent pollution of water, food, land, and the ecosystem and avoid the resistance of weeds to chemicals. This can help mitigate and adapt to climate change by minimizing agriculture’s environmental impact and improving the sustainability of the agricultural sector. In addition to discussing recent advances, this study also highlights the challenges faced in adopting new technology in agriculture and proposes novel techniques to enhance the performance of deep learning models. The study provides valuable insights into the latest advances and challenges in process systems engineering and technology for agricultural activities

Applications of Image Processing for Grading Agriculture products

Image processing in the context of Computer vision, is one of the renowned topic of computer science and engineering, which has played a vital role in automation. It has eased in revealing unknown fact in medical science, remote sensing, and many other domains. Digital image processing along with classification and neural network algorithms has enabled grading of various things. One of prominent area of its application is classification of agriculture products and especially grading of seed or cereals and its cultivars. Grading and sorting system allows maintaining the consistency, uniformity and depletion of time. This paper highlights various methods used for grading various agriculture products. DOI: 10.17762/ijritcc2321-8169.15036

Big Earth Data and Machine Learning for Sustainable and Resilient Agriculture

Big streams of Earth images from satellites or other platforms (e.g., drones and mobile phones) are becoming increasingly available at low or no cost and with enhanced spatial and temporal resolution. This thesis recognizes the unprecedented opportunities offered by the high quality and open access Earth observation data of our times and introduces novel machine learning and big data methods to properly exploit them towards developing applications for sustainable and resilient agriculture. The thesis addresses three distinct thematic areas, i.e., the monitoring of the Common Agricultural Policy (CAP), the monitoring of food security and applications for smart and resilient agriculture. The methodological innovations of the developments related to the three thematic areas address the following issues: i) the processing of big Earth Observation (EO) data, ii) the scarcity of annotated data for machine learning model training and iii) the gap between machine learning outputs and actionable advice. This thesis demonstrated how big data technologies such as data cubes, distributed learning, linked open data and semantic enrichment can be used to exploit the data deluge and extract knowledge to address real user needs. Furthermore, this thesis argues for the importance of semi-supervised and unsupervised machine learning models that circumvent the ever-present challenge of scarce annotations and thus allow for model generalization in space and time. Specifically, it is shown how merely few ground truth data are needed to generate high quality crop type maps and crop phenology estimations. Finally, this thesis argues there is considerable distance in value between model inferences and decision making in real-world scenarios and thereby showcases the power of causal and interpretable machine learning in bridging this gap.Comment: Phd thesi