Identifying Medicinal Plant Leaves Using Textures and Optimal Colour Spaces Channel

This paper presents an automated medicinal plant leaf identification system. The Colour Texture analysis of the leaves is done using the statistical, the Grey Tone Spatial Dependency Matrix(GTSDM) and the Local Binary Pattern(LBP) based features with 20 different colour spaces(RGB, XYZ, CMY, YIQ, YUV, $YC_{b}C_{r}$, YES, $U^{*}V^{*}W^{*}$, $L^{*}a^{*}b^{*}$, $L^{*}u^{*}v^{*}$, lms, $l\alpha\beta$, $I_{1} I_{2} I_{3}$, HSV, HSI, IHLS, IHS, TSL, LSLM and KLT). Classification of the medicinal plant is carried out with 70\% of the dataset in training set and 30\% in the test set. The classification performance is analysed with Stochastic Gradient Descent(SGD), kNearest Neighbour(kNN), Support Vector Machines based on Radial basis function kernel(SVM-RBF), Linear Discriminant Analysis(LDA) and Quadratic Discriminant Analysis(QDA) classifiers. Results of classification on a dataset of 250 leaf images belonging to five different species of plants show the identification rate of 98.7 \%. The results certainly show better identification due to the use of YUV, $L^{*}a^{*}b^{*}$ and HSV colour spaces

A Review on Detection of Medical Plant Images

Both human and non-human life on Earth depends heavily on plants. The natural cycle is most significantly influenced by plants. Because of the sophistication of recent plant discoveries and the computerization of plants, plant identification is particularly challenging in biology and agriculture. There are a variety of reasons why automatic plant classification systems must be put into place, including instruction, resource evaluation, and environmental protection. It is thought that the leaves of medicinal plants are what distinguishes them. It is an interesting goal to identify the species of plant automatically using the photo identity of their leaves because taxonomists are undertrained and biodiversity is quickly vanishing in the current environment. Due to the need for mass production, these plants must be identified immediately. The physical and emotional health of people must be taken into consideration when developing drugs. To important processing of medical herbs is to identify and classify. Since there aren't many specialists in this field, it might be difficult to correctly identify and categorize medicinal plants. Therefore, a fully automated approach is optimal for identifying medicinal plants. The numerous means for categorizing medicinal plants that take into interpretation based on the silhouette and roughness of a plant's leaf are briefly précised in this article

Techniques of deep learning and image processing in plant leaf disease detection: a review

Computer vision techniques are an emerging trend today. Digital image processing is gaining popularity because of the significant upsurge in the usage of digital images over the internet. Digital image processing is a practice that can help in designing sophisticated high-end machines, which can hold the ophthalmic functionality of the human eye. In agriculture, leaf examination is important for disease identification and fair warning for any deficiency within the plant. Many prominent plant species are facing extinction because of a lack of knowledge. A proper realization of computer vision techniques aid in extracting a significant amount of information from leaf image. This necessitates the requirement of an automatic leaf disease detection method to diagnose disease occurrences and severity, for timely crop management, by spraying pesticides. This study focuses on techniques of digital image processing and machine learning rendered in plant leaf disease detection, which has great potential in precision agriculture. To support this study, techniques exercised by various researchers in recent years are tabulated

AlexNet-Based Feature Extraction for Cassava Classification: A Machine Learning Approach

تعتبر الكسافا محصولًا مهمًا في أجزاء كثيرة من العالم، لا سيما في إفريقيا وآسيا وأمريكا الجنوبية، حيث تعمل كغذاء أساسي لملايين الأشخاص. يعتبر استخدام ميزات اللون والملمس والشكل أقل كفاءة في تصنيف أنواع الكسافا. وذلك لأن أوراق الكسافا لها نفس لون مورفولوجيا بين نوع وآخر. بالإضافة إلى ذلك، فإن أوراق الكسافا لها شكل مشابه نسبيًا لنوع واحد من الكسافا، وبالمثل، مع قوام أوراق الكسافا. إلى جانب ذلك، هناك أيضًا المنيهوت السامة. الكسافا السامة وغير السامة لها لون وشكل وملمس أوراق متطابق نسبيًا. يهدف هذا البحث إلى تصنيف أنواع الكسافا باستخدام طريقة التعلم العميق مع AlexNet المدربة مسبقًا كمستخرج للميزات. تم استخدام ثلاث طبقات مختلفة متصلة بالكامل لاستخراج السمات، وهي fc6 و fc7 و fc8. كانت المصنفات المستخدمة هي Support Vector Machine (SVM) و K-Nearest Neighbours (KNN) و Naive Bayes. تتكون مجموعة البيانات من 1400 صورة لأوراق الكسافا تتكون من أربعة أنواع من الكسافا: Gajah و Manggu و Kapok و Beracun. أوضحت النتائج أن أفضل طبقة استخلاص كانت fc6 وبدقة 90.7٪ للطبقة المتناهية الصغر (SVM). كان أداء SVM أيضًا أفضل مقارنةً بـ KNN و Naive Bayes، بدقة 90.7٪، وحساسية 83.5٪، ونوعية 93.7٪، ودرجة F1 83.5٪. ستساهم نتائج هذا البحث في تطوير تقنيات تصنيف النباتات، وتوفير رؤى حول الاستخدام الأمثل للتعلم العميق وطرق التعلم الآلي لتحديد الأنواع النباتية. في النهاية، يمكن للنهج المقترح أن يساعد الباحثين والمزارعين وعلماء البيئة في تحديد الأنواع النباتية ومراقبة النظام البيئي والإدارة الزراعية.Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has 350 images. Three fully connected (FC) layers were utilized for feature extraction, namely fc6, fc7, and fc8. The classifiers employed were support vector machine (SVM), k-nearest neighbors (KNN), and Naive Bayes. The study demonstrated that the most effective feature extraction layer was fc6, achieving an accuracy of 90.7% with SVM. SVM outperformed KNN and Naive Bayes, exhibiting an accuracy of 90.7%, sensitivity of 83.5%, specificity of 93.7%, and F1-score of 83.5%. This research successfully addressed the challenges in classifying cassava species by leveraging deep learning and machine learning methods, specifically with SVM and the fc6 layer of AlexNet. The proposed approach holds promise for enhancing plant classification techniques, benefiting researchers, farmers, and environmentalists in plant species identification, ecosystem monitoring, and agricultural management

Reliable and Automatic Recognition of Leaf Disease Detection using Optimal Monarch Ant Lion Recurrent Learning

Around 7.5 billion people worldwide depend on agriculture production for their livelihood, making it an essential component in keeping life alive on the planet. Negative impacts are being caused on the agroecosystem due to the rapid increase in the use of chemicals to combat plant diseases. These chemicals include fungicides, bactericides, and insecticides. Both the quantity and quality of the output are impacted when there is a high-scale prevalence of diseases in crops. Plant diseases provide a significant obstacle for the agricultural industry, which has a negative impact on the growth of plants and the output of crops. The problem of early detection and diagnosis of diseases can be solved for the benefit of the farming community by employing a method that is both quick and reliable regularly. This article proposes a model for the detection and diagnosis of leaf infection called the Automatic Optimal Monarch AntLion Recurrent Learning (MALRL) model, which attains a greater authenticity. The design of a hybrid version of the Monarch Butter Fly optimization algorithm and the AntLion Optimization Algorithm is incorporated into the MALRL technique that has been proposed. In the leaf image, it is used to determine acceptable aspects of impacted regions. After that, the optimal characteristics are used to aid the Long Short Term Neural Network (LSTM) classifier to speed up the process of lung disease categorization. The experiment's findings are analyzed and compared to those of ANN, CNN, and DNN. The proposed method was successful in achieving a high level of accuracy when detecting leaf disease for images of healthy leaves in comparison to other conventional methods

DETECTION OF PLANT LEAF DISEASES IN AGRICULTURE USING RECENT IMAGE PROCESSING TECHNIQUES – A REVIEW

Purpose: Agricultural productivity is something on which the economy highly depends in India as well in all over the world. India is an agriculture-dependent country; wherein about 70% of the population depends on agriculture. Methodology: This is one of the main reasons that disease detection in agriculture plays an important role, as having the disease in plant leaf is quite natural. If proper observations are not taken in the agriculture field then it causes serious effects on plants due to which respective product quality and productivity are affected. Detection of plant leaf disease through effective and accurate automatic technique is beneficial at the starting stage as it reduces a large work of monitoring in big farms of crops. Result: This paper presents the review on the state of the art disease classification techniques presently used using image processing that can be used for plant leaf disease detection in agriculture