Wheat Crop Disease Detection

Abstract

The Pak Plant application represents a significant advancement in agricultural technology, specifically tailored for farmers in Pakistan. By harnessing the power of machine learning, this application aids in the accurate identification of crop diseases, offering insights into farming techniques and fertilizer usage to mitigate agricultural losses. The agricultural sector in Pakistan faces significant challenges due to crop diseases, leading to substantial economic losses for farmers. The absence of a reliable tool for timely disease detection exacerbates these issues, impacting crop yields and overall food security. The methodology employed in this project entailed the utilization of Convolutional Neural Network (CNN) machine learning models. Through a meticulous process of data collection from diverse internet sources, a comprehensive dataset of crop disease images was assembled. The results of the project showcase the effectiveness of the Pak Plant application in accurately identifying crop diseases, thereby enabling farmers to make informed decisions regarding disease management strategies. The Pak Plant application’s contribution to the agricultural sector is multifaceted. It not only addresses the pressing issue of crop disease identification but also empowers farmers with knowledge and insights into effective farming practices and fertilizer usage. By leveraging advanced technology, Pak Plant plays a pivotal role in enhancing agricultural productivity and contributing to the overall economy of Pakistan.