The research work in this paper presents a unique approach to rice crop disease classification by neural network using Fast Fourier Transform (FFT) and CNN. The study will go through various models like AlexNet, typical CNNs, and FFT-enhanced CNNs for the purpose of classifying and diagnosing diseases found in rice crops based on image data. The FFT-CNN model integrates FFT in capturing frequency-domain information to enhance the feature extraction process thereby making it more effective and accurate in classifying output results. It is shown from the results that while both CNN and FFT-CNN outperform AlexNet, the FFT-CNN model achieves an accuracy rate similar to traditional CNNs but with much less processing time. This novel integration of FFT and CNN may be instrumental in ensuring farmers are provided with speedy and precise disease identification so as to enable well-informed crop management for better productivity.