EDIBLE FISH IDENTIFICATION BASED ON MACHINE LEARNING

Automated fish identification system has a beneficial role in various fields. Fish species can usually be identified based on visual observation and human experiences. False appreciation can cause food poisoning. The proposed system aims to efficiently and effectively identify edible fish from poisonous ones based on three machine learning (ML) techniques. A total of 300 fish images are used, collected from 20 species with differences in shapes, sizes, and colors. Hybrid features were extracted and then fed to three types of ML techniques: k-nearest neighbor (K-NN), support vector machine (SVM), and neural networks (NN). The 300 fish images are divided into two: 70% for training and 30% for testing. The accuracy rates for the presented system were 91.1%, 92.2%, and 94.4% for KNN, SVM, and NNs, respectively. The proposed system is evaluated using four terms: precision, sensitivity, F1-score, and accuracy. Results show that the proposed approach achieved higher accuracy compared with other recent pertinent studies

PDCNN: FRAMEWORK for Potato Diseases Classification Based on Feed Foreword Neural Network

           يعتمد الاقتصاد بشكل استثنائي على الإنتاجية الزراعية. لذلك ، في مجال الزراعة ، يعد اكتشاف عدوى النبات مهمة حيوية لأنه يعطي تقدماً واعداً نحو تطوير الزراعة. في هذا العمل، تم اقتراح نظام لتصنيف أمراض البطاطا بالاعتماد على الشبكة العصبية. يهدف النظام إلى كشف وتصنيف أربعة أنواع من أمراض درنات البطاطا وهي: النقطة السوداء ، الجرب الشائع، فيروس البطاطا Y واللفحة المبكرة بالاعتماد على صورهم. يتكون النظام من ثلاثة مستويات: مستوى المعالجة المسبقة هو المستوى الاول، والذي يعتمد على K-means clustering  لاكتشاف المنطقة المصابة من صورة البطاطا، المستوى الثاني هو مستوى استخراج الميزات والذي يستخرج الميزات من المنطقة المصابة بالاعتماد على طريقتين:  grey level run  length matrix  و  first order histogram based features. الميزات المستخرجة من المستوى الثاني تستخدم في المستوى الثالث في تغذية الشبكة العصبية الأمامية لإجراء عملية التصنيف . 120 صورة ملونة استخدمت, 80 صورة استخدمت في تدريب الشبكة و40 صورة استخدمت في عملية الاختبار . النظام المقترح فعال للغاية في تصنيف أربعة أنواع من أمراض درنات البطاطا وكانت نسبة التمييز 91,3 %  .         The economy is exceptionally reliant on agricultural productivity. Therefore, in domain of agriculture, plant infection discovery is a vital job because it gives promising advance towards the development of agricultural production. In this work, a framework for potato diseases classification based on feed foreword neural network is proposed. The objective of this work  is presenting a system that can detect and classify four kinds of potato tubers diseases; black dot, common scab, potato virus Y and early blight based on their images. The presented PDCNN framework comprises three levels: the pre-processing is first level, which is based on K-means clustering algorithm to detect the infected area from potato image. The second level is features extraction which extracts features from the infected area based on hybrid features: grey level run length matrix and 1st order histogram based features. The attributes that extracted from second level are utilized in third level using FFNN to perform the classification process. The proposed framework is applied to database with different backgrounds, totally 120 color potato images, (80) samples used in training the network and the rest samples (40) used for testing. The proposed PDCNN framework is very effective in classifying four types of potato tubers diseases with 91.3% of efficiency

EDIBLE FISH IDENTIFICATION BASED ON MACHINE LEARNING

Automated fish identification system has a beneficial role in various fields. Fish species can usually be identified based on visual observation and human experiences. False appreciation can cause food poisoning. The proposed system aims to efficiently and effectively identify edible fish from poisonous ones based on three machine learning (ML) techniques. A total of 300 fish images are used, collected from 20 species with differences in shapes, sizes, and colors. Hybrid features were extracted and then fed to three types of ML techniques: k-nearest neighbor (K-NN), support vector machine (SVM), and neural networks (NN). The 300 fish images are divided into two: 70% for training and 30% for testing. The accuracy rates for the presented system were 91.1%, 92.2%, and 94.4% for KNN, SVM, and NNs, respectively. The proposed system is evaluated using four terms: precision, sensitivity, F1-score, and accuracy. Results show that the proposed approach achieved higher accuracy compared with other recent pertinent studies

CFNN for Identifying Poisonous Plants

  Identification of poisonous plants is a hard challenge for researchers because of the great similarity between poisonous and non- poisonous plants. Traditional methods to identify poisonous plant can be tiresome, therefore, automated poisonous plants identification system is needed. In this work, cascade forward neural network framework is proposed to identify poisonous plants based on their leaves. The proposed system was evaluated on both (poisonous leaves/non-poisonous leaves) which are collected using smart phone and internet. Combination of shape features and statistical features are extracted from leaf then fed to cascade-forward neural network which used TRAINLM function for training. 500 samples of leaf images are used, 250 samples are poisonous, the remaining 250 samples are non-poisonous.300 samples used in training, 200 samples for testing. Our system is achieved an accuracy value of 99.5%