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Optimized Deep Learning Techniques for Disease Detection in Rice Crop using Merged Datasets


Muhammad Junaid, Sohail Jabbar, Muhammad Munwar Iqbal, Saqib Majeed3, Mubarak Albathan, Qaisar Abbas, Ayyaz Hussain


Vol. 23  No. 3  pp. 57-66


Rice is an important food crop for most of the population in the world and it is largely cultivated in Pakistan. It not only fulfills food demand in the country but also contributes to the wealth of Pakistan. But its production can be affected by climate change. The irregularities in the climate can cause several diseases such as brown spots, bacterial blight, tungro and leaf blasts, etc. Detection of these diseases is necessary for suitable treatment. These diseases can be effectively detected using deep learning such as Convolution Neural networks. Due to the small dataset, transfer learning models such as vgg16 model can effectively detect the diseases. In this paper, vgg16, inception and xception models are used. Vgg16, inception and xception models have achieved 99.22%, 88.48% and 93.92% validation accuracies when the epoch value is set to 10. Evaluation of models has also been done using accuracy, recall, precision, and confusion matrix.


:Rice; Disease; Detection; deep learning; CNN; brown spots; bacterial blight; tungro; Vgg16; in-ception; Xception.