There is an incredible progress in machine learning applications in the field of agricultural research. Detection of various diseases, deficiencies, and factors impacting crops’ productivity is one of the major ongoing research in this field. This paper considers various machine learning and deep learning techniques (transfer learning) for rice disease detection. In this study three different rice diseases viz. bacterial blight, rice blast, and brown spot are considered. A detailed comparative analysis of the results indicates the superiority of transfer learning techniques over conventional machine learning techniques. It is observed that InceptionResNetV2 achieves the best result followed by XceptionNet. This work can be incorporated in assisting the farmers for early diagnosis of rice disease so that future course of action may be taken on time. For future studies, efforts should be directed to work with bigger datasets so as to generalize the findings of the experiment.

Rice is one of the most widely consumed foods all over the world. Various diseases and deficiency disorders impact the rice crop’s growth, thereby hampering the rice yield. Therefore, proper crop monitoring is very important for the early diagnosis of diseases or deficiency disorders. Diagnosis of diseases and disorders requires specialized manpower, which is not scalable and accessible to all farmers. To address this issue, machine learning and deep learning (DL)-driven automated systems are designed, which may help the farmers in diagnosing disease/deficiency disorders in crops so that proper care can be taken on time. Various studies have used transfer learning (TL) models in the recent past. In recent studies, further improvement in rice disease and deficiency disorder diagnosis system performance is achieved by performing the ensemble of various TL models. However, in all these DL-based studies, the segmentation of the region of interest is not done beforehand and the infected-region extraction is left for the DL model to handle automatically. Therefore, this article proposes a novel framework for the diagnosis of rice-infected leaves based on DL-based segmentation with bitwise logical AND operation and DL-based classification. The rice diseases covered in this study are bacterial leaf blight, brown spot, and leaf smut. The rice nutrient deficiencies like nitrogen (N), phosphorous (P), and potassium (K) were also included. The results of the experiment conducted on these datasets showed that the performance of DeepBatch was significantly improved as compared to the conventional technique.