Abstract:
An early detection of crop diseases is important as it helps in minimizing the losses which would otherwise be incurred and ensuring food security for the agricultural sectors worldwide including Pakistan Army's agriculture-based initiatives. This specific project aims to diagnose cotton diseases through a deep learning approach— more precisely Convolutional Neural Networks (CNNs). The system proposed based on CNN endeavors to detect different types of diseases by studying pictures of cotton plants that are taken in the field— this can lead to an immediate implementation of control measures. Despite its simplicity, this project plays a major role in improving sustainability and productivity among the large scale of cotton farming undertaken by the Pakistan Army as it covers thousands acres with agricultural lands. This study highlights the fusion of cutting-edge deep learning algorithms with pragmatic agricultural goals— an epitome of where technology meets agriculture. This could resonate with various other agricultural development projects in the locality, hence having a broader reach for the impact.
