TY - JOUR AU - Nihar, Fatema AU - Khanom, Nazmun Nahar AU - Hassan, Syed Sahariar AU - Das, Amit Kumar PY - 2021/03/12 Y2 - 2024/03/29 TI - Plant Disease Detection through the Implementation of Diversified and Modified Neural Network Algorithms JF - Journal of Engineering Advancements JA - J. Eng. Adv. VL - 2 IS - 01 SE - Research Articles DO - 10.38032/jea.2021.01.007 UR - https://scienpg.com/jea/index.php/jea/article/view/jea.2021.01.007 SP - 48-57 AB - <p>In the era of artificial systems, disease detection is becoming easier. For detecting disease, monitoring the plants 24 hours, visiting the agricultural office, or asking for help from a specialist seem difficult. This situation demands a user-friendly plant disease detection system, which allows people to detect whether the plant is diseased or not in an easier way.&nbsp; If the plant is diseased, a treatment plan will also be notified. In this way, people can easily save time, money, and, most importantly, plants. In this study, the researchers have collected data of vegetables from a field and applied multiple diversified Neural Network Algorithms such as CNN, MCNN, FRCNN, and, along with that, also proposed a new modified neural network architecture (ModCNN), which has produced 97.69% accuracy. The authors have also classified the bean leaf diseases into four categories according to their symptoms, which will help to identify diseases accurately.</p> ER -