One of the primary objectives in the field of precision agriculture is weed detection. Detecting and expunging weeds in the initial stages of crop growth with deep learning technique can minimize the usage of herbicides and maximize the crop yield for the farmers. This paper proposes a sliding window approach for the detection of weed regions using convolutional neural networks. The proposed approach involves two processes: (1) Image extraction and labelling, (2) building and training our neural network. In the image extraction process, sub-images are extracted by slicing the original images into sub-images. Subsequently each sub-image is labelled based on the given annotation images. In the next process for building the network architecture, convolutional neural network model is implemented with 20 layers consisting of one image input layer, four 2-D convolutional layers, six rectified linear unit (ReLU) layers, four 2-D max pooling layers, three fully connected layers, one softmax layer, and one final classification layer. Various collections of sub-images gathered by various sliding window sizes were passed into this network to determine the best sliding window size that resulted in higher true weed detection rate and lower percentage of crop wastage. After the ratios between true weed detection rate and crop wastage values were computed for each sliding window sizes, it was found out that the sliding window size of [80 80] resulted in the maximum ratio with true weed detection rate and crop wastage values of 63.28% and 13.33% respectively. These findings reveal that the sliding window size of [80 80] was able to predict the true weed regions in the crops field imagery with 63.28% accuracy that causes least damage to the crops due to predicting crops as weeds.