@article{Nihar_Khanom_Hassan_Das_2021, title={Plant Disease Detection through the Implementation of Diversified and Modified Neural Network Algorithms}, volume={2}, url={https://scienpg.com/jea/index.php/jea/article/view/jea.2021.01.007}, DOI={10.38032/jea.2021.01.007}, abstractNote={<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.  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>}, number={01}, journal={Journal of Engineering Advancements}, author={Nihar, Fatema and Khanom, Nazmun Nahar and Hassan, Syed Sahariar and Das, Amit Kumar}, year={2021}, month={Mar.}, pages={48–57} }