The objective of this research work is to categorize and identify various plant diseases at their earliest stages. Due to the late 20th century’s dramatic expansion in human population, there is an enormous demand for crops, fruits, and vegetables that much exceeds availability. Due to plant disease and pests, nearly more than 30% of crops are lost. Pests have a direct negative influence on crops because they eat the plants, spread illnesses, and open the door for bacterial, viral, and fungal growth. The illnesses and pests can be located by professionals using a visual check. However, it is quite expensive to continually consult professionals to treat sickness. Advance discovery of pests can help in reducing disease transmission to crops nearby, which is advantageous given the wastage factor. Artificial intelligence and algorithms of machine learning can be utilized for early detection. The identifying and categorization of various plant ailments may be done using deep learning techniques like Convolution Neural Network and Resnet. To identify and categorize the plant illness at an early stage, the suggested approach employs an object detection and picture recognition model. For the locally prevalent plant diseases brought on by pests, this model has been specifically trained to operate more accurately. This study assists in developing a creative solution to the problem and lessens harm to crops, vegetables, and fruits by identifying and eradicating pests early on. 

Performance Analysis of ResNet50 Architecture based Pest Detection System

The objective of this research work is to categorize and identify various plant diseases at their earliest stages. Due to the late 20th century’s dramatic expansion in human population, there is an enormous demand for crops, fruits, and vegetables that much exceeds availability. Due to plant disease and pests, nearly more than 30% of crops are lost. Pests have a direct negative influence on crops because they eat the plants, spread illnesses, and open the door for bacterial, viral, and fungal growth. The illnesses and pests can be located by professionals using a visual check. However, it is quite expensive to continually consult professionals to treat sickness. Advance discovery of pests can help in reducing disease transmission to crops nearby, which is advantageous given the wastage factor. Artificial intelligence and algorithms of machine learning can be utilized for early detection. The identifying and categorization of various plant ailments may be done using deep learning techniques like Convolution Neural Network and Resnet. To identify and categorize the plant illness at an early stage, the suggested approach employs an object detection and picture recognition model. For the locally prevalent plant diseases brought on by pests, this model has been specifically trained to operate more accurately. This study assists in developing a creative solution to the problem and lessens harm to crops, vegetables, and fruits by identifying and eradicating pests early on.

Automated classification of distinct leaf categories through advanced deep neural technologies has emerged as a notable research trend. The primary aim of this research is to design “GreenMedNet,” an advanced and computationally efficient deep neural network. This network is specifically tailored to automatically differentiate between diseased and healthy insulin leaves, based on their inherent hyperglycemic properties. This research is driven by the practical importance of the field. The primary contribution of our work lies in the creation of the novel “GreenMedNet” architecture which is designed to effectively balance performance efficiency with a minimal total count of parameters. This balance is achieved through integrating Depth-wise Convolution, GlobalAveragePooling2D, Batch Normalization, and Dropout layers into the architecture of GreenMedNet. Depth-wise Convolution and GlobalAveragePooling2D layers play a crucial role in minimizing the total parameter count, whereas Batch Normalization and Dropout layers expedite convergence and mitigate overfitting, respectively. Additionally, the incorporation of a dual attention mechanism enhances the network’s performance by selectively emphasizing important features both spatially and channel-wise. To support our research, we have curated a novel well-balanced database namely, “Nature’s Diabetes Helpers: Insulin Leaves Repository,” containing 429 images distributed between healthy and damaged classes exclusively which serves as another for this work. Notably diverse, this repository forms a unique resource. Our designed network demonstrates exceptional performance, achieving a Test Accuracy of 99.47%, along with high Precision, Recall, and F1 scores, which underscores its superiority over established networks.

GreenMedNet: A computationally inexpensive hyperparameter optimised dual attention based insulin leaves categorization approach

Automation in agricultural sector is an emerging area of research as it is responsible for enhancing crop development as well as to save the crop yield from getting wasted due to several plant diseases caused by bacteria, fungi, virus, and pest attacks as every year a significant portion of total crop yield gets wasted globally due to such causes which lead to huge financial loss. Manual detection of these crop diseases by experts involves huge cost as well as time. To provide cost-effective and time-efficient solutions to this problem, various researchers have designed several machine learning and deep learning frameworks-based plant diseases detection models. Apart from their architectures, another significant factor on which the performances of these models depend is the databases using which these models are trained. Although due to the relevance of this research topic, in recent years, many researchers have created several databases by incorporating images of diseased and healthy samples of different types of crop species, and there are still a large number of crop species present whose automated disease detection is not possible due to unavailability of proper databases. Hence, in this work, we have conducted a comprehensive survey on existing plant disease databases and listed the type of samples included in them in order to provide the researchers an idea about the research advancements achieved in this domain till date. 

Plant Data Survey: A Comprehensive Survey on Plant Disease Detection Database

Accurately identifying diseases that harm pumpkin leaves is crucial to maintaining crop health and ensuring a successful harvest. This research offers a hybrid model that is a combination of support vector machines (SVM) and convolutional neural networks (CNN). Through the use of support vector machines (SV) in conjunction with a hybrid model, this research presents an innovative approach to the classification of photographs of pumpkin leaves. In the course of the research, the following five categories will be investigated: Gummy stem blight, Angular Leaf Spot, Powdery Mildew, Downy Mildew, and Healthy. Through the use of an 80:20 ratio, the dataset was partitioned into training and assessment sets. The exceptional performance of the model is shown by the fact that the Healthy class attained an accuracy of 98% and a precision of 95.65%. The Angular Leaf Spot and the Downy Mildew both had high accuracy ratings, with the former coming in at 93.21% and the latter at 93.57%. Precision, recall, and F1-score macro, weighted, and micro averages were all higher than 92%, which indicates consistent and reliable performance in every area. The overall accuracy of the model was more than 96%, which demonstrates its effectiveness in accurately identifying a variety of diseases that might harm pumpkin leaves or stems. By demonstrating how CNN as well as SVM may be used to detect advanced agricultural illnesses, this work provides a powerful instrument that can be utilized for improved crop health tracking and oversight applications.

Multi-Model Learning: Exploiting CNN and SVM for Robust Pumpkin Disease Analysis

Identification of plant disease is tough in agribusiness arena. If it is inaccurate, there occurs a tremendous damage in the production and economical price. Leaf Disease detection requires huge amount of work, knowledge, processing time in plant disease. The most used and edible vegetable all over the world is from cucurbitaceous family. The crops under this family have great economic value in the food industry and its production is done in large scale. This family consists of 965 species. If any of these plants catch disease then there would be a tremendous loss in the production of this field yields. Thus, treating them at early stage is best way to prevent such losses. Hence, Deep Learning Algorithm like CNN can be used to detect the diseases of the plants. The leaves of the plants would be used as primary material for identification of the disease, as they are much more visible on the leaves.

/pdf/leaf-disease-detection-of-cucurbits-using-cnn-21flnnzt7g.pdf

Leaf disease detection of cucurbits using cnn

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Leaf disease detection of cucurbits using cnn