Ethanopharmacology of Myrica esculenta: A Systemic Review

This systematic review focuses on Myrica Esculenta, a medicinal plant with a rich history in traditional medicine. The aim of the review is to provide a comprehensive overview of the ethnopharmacology of the plant, including its traditional uses, phytochemistry and pharmacological benefits. Common uses of M. Esculenta include treating respiratory diseases such as asthma and bronchitis, as well as gastrointestinal problems such as diarrhea and ulcers. The plant is also used to treat fever, anemia and various ear, nose and throat diseases. With its recognition in the Ayurvedic Pharmacopoeia and its widespread use in folk medicine, M. Esculenta has significant ethnopharmacological value. Through phytochemical analysis, flavonoids, tannins, steroids and terpenes have been identified as the plant\u27s main components, which are believed to contribute to its medicinal properties such as analgesic, anti-inflammatory, antioxidant and anti-cancer effects. Pharmacological studies have confirmed the therapeutic potential of M. Esculenta and demonstrated its antiasthmatic, antiulcerative, anxiolytic, hepatoprotective and wound healing properties. Conservation measures are crucial to protect the plant from over-exploitation and habitat loss. Suggestions such as micropropagation, germplasm preservation and synthetic seed production make sense for sustainable use

Performance analysis of support vector machine for early identification of citrus diseases

Early citrus disease detection is necessary for optimum citrus productivity. But detecting a citrus disease at an early stage requires expert views or laboratory tests. But getting an expert view of all time is impossible for rural farmers. The present study aimed to create a low-cost, intelligent, affordable citrus disease classification system. This study offered a Support Vector Machine (SVM) based smart classification method for categorizing various citrus diseases. Citrus photos were subjected to a variety of image processing techniques to categorize the diseases using SVM and the kernel. Prior to classification, the images were segmented and the hue channel threshold value was used to differentiate the diseased area from the remaining portion of the image. The segmented image’s color and grey domains were used to extract 13 different texture and color features. This study outlined three different SVM kernel types- Linear, Gaussian, and Polynomial, and evaluated their accuracy and confusion matrix performances. The Radial Based Function with a polynomial kernel derived from the SVM outperformed the SVM's linear and Gaussian kernel

Cotton Plants Diseases Detection Using CNN

Identifying cotton infections is a major problem that often requires expert assistance in determining and treating the disease. This investigation aims to create a sophisticated learning model that can tell a plant's illness apart from images of its leaves. Convolution Brain Organization is used to do move training to complete deep learning. For the dataset used, this method produced outcomes for a given state of quality. The main goal is to offer this approach to as many individuals as is realistically expected while reducing the cost of professional aid in identifying cotton plant diseases. The ability to recognize and understand items from photographs has been made possible by rapid advancements in deep learning (DL) techniques

Comparative study on Leaf disease identification using Yolo v4 and Yolo v7 algorithm

Agriculture is the primary occupation of nearly all nations that feed the world's population. The population growth and rising demand for food require farmers to increase food production to meet the requirements. On the other hand, farming is not regarded as a lucrative occupation, as farmers incur significant losses due to pests and diseases that reduce the quality and quantity of farm produce. Consequently, predicting plant diseases using modern technologies will aid producers in making well-informed decisions early on. This study employs and compares the results of two important computer vision algorithms, YOLOv4 and YOLOv7, for classifying leaf diseases from images of leaves from various plant species. The models are trained with images of individual leaves captured in various environments, imparting resilience and adaptability. Both models annotate and predict leaf diseases with high confidence for each class. Other classification metrics, such as Precision, F1-score, Mean Average Precision, and recall, also demonstrate competitive performance. However, YOLOv7 performs better because its flexible labeling mechanism dynamically learns the class labels. In addition, the work can be expanded to utilize recommendation strategies to predict the extent of injury.Wang Xinming (Dr, professor, Ph.D., Department of Mechanical and Manufacturing Engineering), Tang Sai Hong (Dr professor, Ph.D., Department of Mechanical and Manufacturing Engineering), Mohd Khairol Anuar b. Mohd Ariffin (Dr, professor, Ph.D., Department of Mechanical and Manufacturing Engineering), Mohd Idris Shah b. Ismail (Dr, professor, Ph.D., Department of Mechanical and Manufacturing Engineering)Includes bibliographical references

Analysis on Leaf Disease Identification using Classification Models

The Researchers have all been aware of the rising food demand brought on by the population's rapid growth and the high mortality rates caused by medical developments. One of the many farming practises where computerization in agriculture has made significant progress is the identification of numerous plant diseases. The focus of almost every nation has shifted towards mechanising agriculture in order to achieve accuracy and precision and to meet the continually increasing demand for food. Identification of plant diseases is one of the most difficult tasks in agriculture and has a significant effect on crop yield. Artificial intelligence has recently begun to concentrate on smart agriculture science.Ground-breaking methods in plant science through deep learning and hyperspectral imaging to locate and recognise plant diseases has been addressed in this study

Machine and Deep Learning Approaches for Plant Disease Detection: A Comprehensive Review

People have been using edible foods since ancient times, and they continue to be an essential component of a healthy diet and traditional food systems today. Food crops as a major source of human energy intake, and the challenges they face due to biotic and abiotic stress factors, such as pollution, insects, bacteria, and unfavourable weather conditions. Detecting plant diseases in the early stage is critical for ensuring a stable supply of healthy food, and traditional methods of disease detection by experts are lengthy and have some limitations. The use of Machine and Deep learning is a key aspect of precision farming for crop growth monitoring. Plenty ML strategies, including random forest and support vector machines (SVMs), Convolutional Neural Networks, Deep learning as well as image processing have been used to precisely detect, classify, and predict plant diseases. By leveraging machine learning algorithms, farmers and agricultural experts can accurately detect and diagnose crop diseases, enabling them to take appropriate measures to control and prevent further spread of the disease.  This article provides a comprehensive overview of the different AI approaches for plant disease identification and control, drawing on a range of research articles in the field. The application of machine learning in agriculture holds promise for improving crop health and increasing yields and represents an important area of innovation for sustainable agriculture in the future

Optimized Matrix Feature Analysis – Convolutional Neural Network (OMFA-CNN) Model for Potato Leaf Diseases Detection System

One of the most often grown crops is the potato. As a main food, potatoes are prioritised for cultivation worldwide. Because potatoes are such a rich source of vitamins and minerals, we can create a robust system for food security. However, a number of illnesses delay the growth of agriculture and harm potato output. Consequently, early disease identification can offer a better answer for effective crop production. In this research work aim is to classify and detect the potato leave (PL) diseases using OMFA-CNN deep learning model. An optimized matrix feature analysis-CNN deep learning model for PL disease detection is implemented. In the first phase, the PLs features are extracted from the potato leave images using K-means clustering image segmentation method. At the last phase, a new OMFA-CNN model are proposed using CNN to classify virus, and bacterial diseases of PLs, The PL disease dataset consists 2351 images gathered in real-time and from the Kaggle (PlantVillage) dataset. The implemented OMFA-CNN model attained 99.3 % precision and 99 % recall on potato disease detection. The implemented method is also compared with MASK RCNN,SVM and other models and attained significantly high precision and recall

Comparing machine learning and deep learning classifiers for enhancing agricultural productivity: case study in Larache Province, Northern Morocco

The agriculture sector in the Tangier-Tetouan-Al-Hoceima-Region (Northern Morocco) contributes a significant percentage to the national revenue. The Larache Province is at the regional forefront in agriculture terms due to its large irrigated areas. Golden-Gogi is a biological farm located in the Larache Province, and its objective is to produce organic crops. Besides climate change, this farm suffers from biotic factors such as snails and insects. These problems cause diseases in plants, resulting in massive crop production losses. Early detection of disease and biotic factors in plants is a difficult task for farmers, but it is now possible thanks to artificial intelligence. For that reason, we aim to contribute to this Province by comparing the well-known models in machine learning (ML) and deep learning (DL) used in early plant disease detection to specify the best-classifier in terms of detecting mint plant diseases. Mint plant is a major crop on the Golden-Gogi farm, and its dataset was collected from there. As per findings, DL classifiers outperform ML classifiers in disease detection. The best-classifier is DenseNet201, with high accuracy of 94.12%. Hence, the system using DenseNet201 offers a solution for farmers of this Province in making urgent decisions to avoid mint yield losses

A fuzzy system to stimulate the planting of beans in Brazil

Beans are traditional elements on the table of the Brazilian population. In spite of this, the production of beans has been falling in Brazil in the last years. This article tries to identify the causes for the lack of motivation for growing beans in Brazil. It also aims to point out which factors are the most relevant for obtaining an expressive plantation and bean harvest. Given this, we were able to develop a system based on fuzzy logic that can be useful for measuring the expected loss until the bean harvest. With the output from such system the farmer can be provided incentives to start planting once that loss is considered acceptable.  In order to generate the rules of the system the multicriteria TOPSIS method is used. The prototype fuzzy system explained and proposed in this article can be further expanded by agricultural experts thus leading to a large scale planting of beans