Advanced Deep Learning Model for Plant Diseases Detection in Precision Agriculture

Abstract

Plant disease detection is becoming a vital research area due to the need to achieve sustainable development goals. This study aims to introduce a new deep learning technique based on inception and a depthwise-separable convolution layer. This approach aims to reduce computational complexity, size, and parameter set without compromising performance. The proposed model was evaluated on two datasets to classify different crop diseases. The proposed model achieved the highest accuracy of 99.1 in the plant village and 98.5 in the potato dataset with the compared studies.