eprintid: 479 rev_number: 9 eprint_status: archive userid: 2 dir: disk0/00/00/04/79 datestamp: 2022-03-03 13:02:38 lastmod: 2023-07-04 23:30:19 status_changed: 2022-03-03 13:02:38 type: article metadata_visibility: show creators_name: Trivedi, Naresh K. creators_name: Gautam, Vinay creators_name: Anand, Abhineet creators_name: Aljahdali, Hani Moaiteq creators_name: Gracia Villar, Santos creators_name: Anand, Divya creators_name: Goyal, Nitin creators_name: Kadry, Seifedine creators_id: creators_id: creators_id: creators_id: creators_id: santos.gracia@uneatlantico.es creators_id: creators_id: creators_id: title: Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica full_text_status: none keywords: Image processing, Convolution neural network, Plant leaf disease, Deep learning, Artificial intelligence abstract: Tomato is one of the most essential and consumable crops in the world. Tomatoes differ in quantity depending on how they are fertilized. Leaf disease is the primary factor impacting the amount and quality of crop yield. As a result, it is critical to diagnose and classify these disorders appropriately. Different kinds of diseases influence the production of tomatoes. Earlier identification of these diseases would reduce the disease’s effect on tomato plants and enhance good crop yield. Different innovative ways of identifying and classifying certain diseases have been used extensively. The motive of work is to support farmers in identifying early-stage diseases accurately and informing them about these diseases. The Convolutional Neural Network (CNN) is used to effectively define and classify tomato diseases. Google Colab is used to conduct the complete experiment with a dataset containing 3000 images of tomato leaves affected by nine different diseases and a healthy leaf. The complete process is described: Firstly, the input images are preprocessed, and the targeted area of images are segmented from the original images. Secondly, the images are further processed with varying hyper-parameters of the CNN model. Finally, CNN extracts other characteristics from pictures like colors, texture, and edges, etc. The findings demonstrate that the proposed model predictions are 98.49% accurate. date: 2021-11 date_type: published publication: Sensors volume: 21 number: 23 pagerange: 7987 id_number: doi:10.3390/s21237987 refereed: TRUE issn: 1424-8220 official_url: http://doi.org/10.3390/s21237987 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Abierto Inglés Tomato is one of the most essential and consumable crops in the world. Tomatoes differ in quantity depending on how they are fertilized. Leaf disease is the primary factor impacting the amount and quality of crop yield. As a result, it is critical to diagnose and classify these disorders appropriately. Different kinds of diseases influence the production of tomatoes. Earlier identification of these diseases would reduce the disease’s effect on tomato plants and enhance good crop yield. Different innovative ways of identifying and classifying certain diseases have been used extensively. The motive of work is to support farmers in identifying early-stage diseases accurately and informing them about these diseases. The Convolutional Neural Network (CNN) is used to effectively define and classify tomato diseases. Google Colab is used to conduct the complete experiment with a dataset containing 3000 images of tomato leaves affected by nine different diseases and a healthy leaf. The complete process is described: Firstly, the input images are preprocessed, and the targeted area of images are segmented from the original images. Secondly, the images are further processed with varying hyper-parameters of the CNN model. Finally, CNN extracts other characteristics from pictures like colors, texture, and edges, etc. The findings demonstrate that the proposed model predictions are 98.49% accurate. metadata Trivedi, Naresh K.; Gautam, Vinay; Anand, Abhineet; Aljahdali, Hani Moaiteq; Gracia Villar, Santos; Anand, Divya; Goyal, Nitin y Kadry, Seifedine mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, santos.gracia@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2021) Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network. Sensors, 21 (23). p. 7987. ISSN 1424-8220