Maize Disease Detection Using Convolutional Neural Network

¹ Student, Strathmore University, School of Computing and Engineering Sciences, Nairobi, Kenya
² Lecturer, Strathmore University, School of Computing and Engineering Sciences, Nairobi, Kenya
³ Visiting Asst Professor, University of Cincinnati, School of Information Technology, Ohio, USA
⁴ Asst Professor, University of Cincinnati, School of Information Technology, Ohio, USA

¹ Corresponding author: eric.aganze@strathmore.edu

Abstract

The necessity for accurate and early identification of crop diseases is one of the primary difficulties facing the agricultural industries. Diseases have an impact on crop quality and have the potential to destroy hectares of crop yield, resulting in significant losses for farmers. Current diagnostic approaches are time intensive and necessitate the presence of highly skilled professionals to study the damaged plants, comprehend the symptoms, identify the disease, and offer appropriate treatments. Maize diseases can cause a significant reduction in both the quality and quantity of agricultural products. Visual inspection is the main approach adopted in practice for the detection and identification of maize diseases. However, this necessitates continuous oversight by experts, which can result in substantial expenses. The limitations of such techniques have created the need to look for alternative techniques which can detect and classify diseases at an early stage. In this study, models were trained using an open-source library of around 5000 pictures, including healthy plant samples. The convolutional neural network (CNN) outperformed the other established models, obtaining an amazing total accuracy of 97%. This achievement satisfies the need for a reliable and effective categorization model. Furthermore, these findings were then turned into a complete maize disease identification mobile application that is ready for real-world deployment. This application has the potential to provide the agricultural community with the means to promptly diagnose and address issues, reducing the reliance on professional expertise.