Potato Classification Using Deep Learning

Abstract: Potatoes are edible tubers, available worldwide and all year long. They are relatively cheap to grow, rich in nutrients, and they can make a delicious treat. The humble potato has fallen in popularity in recent years, due to the interest in low-carb foods. However, the fiber, vitamins, minerals, and phytochemicals it provides can help ward off disease and benefit human health. They are an important staple food in many countries around the world. There are an estimated 200 varieties of potatoes, which can be classified into a number of categories based on the cooked texture and ingredient functionality. Using a public dataset of 2400 images of potatoes, we trained a deep convolutional neural network to identify 4 types (Red, Red Washed, Sweet, and White).The trained model achieved an accuracy of 99.5% of test set, demonstrating the feasibility of this approach

Detection of Disease on Corn Plants Using Convolutional Neural Network Methods

Deep Learning is still an interesting issue and is still widely studied. In this study Deep Learning was used for the diagnosis of corn plant disease using the Convolutional Neural Network (CNN) method, with a total dataset of 3.854 images of diseases in corn plants, which consisted of three types of corn diseases namely Common Rust, Gray Leaf Spot, and Northern Leaf Blight. With an accuracy of 99%, in detecting disease in corn plants

Android application development for identifying maize infested with fall armyworms with Tamil Nadu Agricultural University Integrated proposed pest management (TNAU IPM) capsules

Several pests and diseases wreak havoc on maize crops worldwide. Novel and rapid methods for detecting pests and diseases in real-time will make monitoring them and designing effective management measures easier. In the recent past, maize has been imperilled by fall armyworms (Spodoptera frugiperda), which have caused substantial yield losses in maize. This study aimed to create an Android mobile application via  DCNN (Deep Convolutional Neural Network)-based AI pest detection system for maize producers. Everyone benefits from the deployment of these CNN models on mobile phones, especially farmers and agricultural extension professionals because it makes them more accessible. Automatic diagnosis of plant pest infestations from captured images through computer vision and artificial intelligence research is feasible for technological advancements. Therefore, early detection of maize fall armyworm (FAW) infestation and providing relevant recommendations in maize could result in intensified maize crop yields. . An Android mobile application was created to identify fall armyworm infection in maize and included the recommendations given by Tamil Nadu Agricultural University proposed Integrated Pest Management (TNAU IPM ) capsules in the mobile app on as to how to deal with such a problem. Digital and novel technology was chosen to address these issues in maize. Deep convolutional neural networks (DCNNs) and transfer learning have recently moved into the realm of just-in-time crop pest infestation detection, following their successful use in a variety of fields. The algorithm accurately detects FAW (S. frugiperda) infected areas on maize with 98.47% training accuracy and 93.47% validation accuracy

Програмний модуль демонстрації учбового матеріалу

UK: Демонстрація екрану або screen sharing - це зручна функція, яка дозволяє транслювати зображення екрану комп'ютера викладача на комп’ютери студентів в режимі реального часу. Вона дає можливість демонструвати студентам фотографії, таблиці, файли, роботу програм, все те, що відображено на моніторі комп'ютера викладача під час трансляції. EN: The work is devoted to computerization of the process of demonstration of educational material with the help of computer programs, as well as the definition and development of an appropriate methodology for its implementation

Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles

This paper describes preliminary work in the recent promising approach of generating synthetic training data for facilitating the learning procedure of deep learning (DL) models, with a focus on aerial photos produced by unmanned aerial vehicles (UAV). The general concept and methodology are described, and preliminary results are presented, based on a classification problem of fire identification in forests as well as a counting problem of estimating number of houses in urban areas. The proposed technique constitutes a new possibility for the DL community, especially related to UAV-based imagery analysis, with much potential, promising results, and unexplored ground for further research.Comment: Workshop on Deep-learning based computer vision for UAV in conjunction with CAIP 2019, Salerno, italy, September 201

Аналіз використання методів машинного навчання у сільскому господарстві

UK: Машинне навчання - це технологія, яка отримує вхідні дані, аналізуючи їх, вчиться та приймає рішення без втручання людини. Сукупність методів машинного навчання, які працюють з різними наборами необроблених даних і знаходять рішення, називають глибоким навчанням. EN: The work is devoted to the analysis of the necessity of using methods of artificial intelligence and machine learning in the agricultural field

Corn leaf image classification based on machine learning techniques for accurate leaf disease detection

Corn leaf disease possesses a huge impact on the food industry and corn crop yield as corn is one of the essential and basic nutrition of human life especially to vegetarians and vegans. Hence it is obvious that the quality of corn has to be ideal, however, to achieve that it has to be protected from the several diseases. Thus, there is a high demand for an automated method, which can detect the disease in early-stage and take necessary steps. However, early disease detection possesses a huge challenge, and it is highly critical. Thus, in this research work, we focus on designing and developing enhanced-K nearest neighbour (EKNN) model by adopting the basic K nearest neighbour (KNN) model. EKNN helps in distinguishing the different class disease. Further fine and coarse features with high quality are generated to obtain the discriminative, boundary, pattern and structural related information and this information are used for classification procedure. Classification process provides the gradient-based features of high quality. Moreover, the proposed model is evaluated considering the Plant-Village dataset; also, a comparative analysis is carried out with different traditional classification model with different metrics

Aplicativo móvil para la detección de Sigatoka negra

Black Sigatoka is one of the main problems that affect the quality and production of the banana crop, it´s because of this, the development of systems to detect diseases, generate an important tool for the monitoring and control carried out by the farmer. The proposed system leverages hardware on mobile devices to implement computer vision techniques to determine the percentage of affected area of the plant. The smartphone is used to acquire data and capture the disease through images. The detection of diseased pixels is then performed through a segmentation algorithm with histogram analysis. A model for the calculation of the affected area is then computed. Finally, the information is presented through the user interface. To validate the proposed method, a database is created with images taken by the application to compare it´s efficiency through the RMS error between manual segmentation and the result of the algorithm. Finally, usability and response time tests are performed.La Sigatoka Negra es uno de los principales problemas que afectan la producción del cultivo de plátano, es por esto, que el desarrollo de sistemas que permitan la detección de enfermedades, generan una herramienta importante para el monitoreo y control realizado por el agricultor. El sistema propuesto, aprovecha el hardware en dispositivos móviles para implementar técnicas de visión por computador que permitan determinar el porcentaje de área afectada de la planta. El Smartphone es utilizado para adquirir datos y capturar la enfermedad a través de imágenes. Después se realiza la detección de los píxeles enfermos a través de un algoritmo de segmentación con análisis por histograma. Posteriormente se computa un modelo para el cálculo del área afectada.  Por último, se presenta la información a través de la interfaz de usuario. Para validar el método propuesto, se crea una base de datos con imágenes tomadas por medio del aplicativo para comparar su eficiencia a través del error RMS entre la segmentación manual y el resultado del algoritmo. Finalmente se realizan pruebas de usabilidad y tiempo de respuesta

Artificial intelligence-based solutions for coffee leaf disease classification

Coffee is one of the most widely consumed beverages and the quantity and quality of coffee beans depend significantly on the health and condition of coffee plants, particularly their leaves. The automation of coffee leaf disease classification using AI is an essential need, providing not only economic benefits but also contributing to environmental conservation and creating better conditions for sustainable coffee cultivation. Through the application of AI, early disease detection is facilitated, thereby reducing pest and disease control costs, minimizing crop losses, increasing coffee productivity and product quality, and promoting environmental preservation. Many studies have proposed AI algorithms for coffee disease classification. However, numerous algorithms employ classical algorithms, while some utilize deep learning, the current state-of-the-art in computer vision. The challenge lies in the fact that when using deep learning, a substantial amount of data is required for training. The design of deep learning architectures to enhance model accuracy while still working with a small training dataset remains an area of ongoing research. In this study, we propose deep learning-based method for coffee leaf disease classification. We propose the combination of different deep convolutional neural networks to further improve overall classification performance. Early and late fusion have been conducted to evaluate the effectiveness of the pre-trained model. Our experimental results demonstrate that the ensemble method outperforms single-model approaches, achieving high accuracy and precision in BRACOL coffee disease leaf