An Intelligent System-Based Coffee Plant Leaf Disease Recognition Using Deep Learning Techniques on Rwandan Arabica Dataset

Abstract

Rwandan coffee holds significant importance and immense value within the realm of agriculture, serving as a vital and valuable commodity. Additionally, coffee plays a pivotal role in generating foreign exchange for numerous developing nations. However, the coffee plant is vulnerable to pests and diseases weakening production. Farmers in cooperation with experts use manual methods to detect diseases resulting in human errors. With the rapid improvements in deep learning methods, it is possible to detect and recognize plan diseases to support crop yield improvement. Therefore, it is an essential task to develop an efficient method for intelligently detecting, identifying, and predicting coffee leaf diseases. This study aims to build the Rwandan coffee plant dataset, with the occurrence of coffee rust, miner, and red spider mites identified to be the most popular due to their geographical situations. From the collected coffee leaves dataset of 37,939 images, the preprocessing, along with modeling used five deep learning models such as InceptionV3, ResNet50, Xception, VGG16, and DenseNet. The training, validation, and testing ratio is 80%, 10%, and 10%, respectively, with a maximum of 10 epochs. The comparative analysis of the models’ performances was investigated to select the best for future portable use. The experiment proved the DenseNet model to be the best with an accuracy of 99.57%. The efficiency of the suggested method is validated through an unbiased evaluation when compared to existing approaches with different metrics.

Coffee disease detection and classification using image processing: A Literature review

“…The quality of the experimental model was assessed using statistical tests such as Wilcoxon and ANOVA. The suggested model outperformed expectations, with a classification accuracy of 99.57% and strong AUC and AP metrics [19].…”

Revolutionizing Coffee Farming: A Mobile App with GPS-Enabled Reporting for Rapid and Accurate On-Site Detection of Coffee Leaf Diseases Using Integrated Deep Learning

“…Various models were investigated, and the most efficient one has been selected for prospective applications. Deep learning models such as DenseNet, ResNet50, Inception-v3, Xception, and VGG16 are used in classifying five distinct classes of coffee plant leaf diseases [27]. The DenseNet model was observed to be simpler, mainly due to its reduced number of trainable parameters and lower computational intricacy.…”

“…In the scope of the activities of this research regarding the complexity of the coffee leaves as well as the modeling complexity, in order to give the model for the application, the comparative analysis was discussed in [27]. Five different transfer learning model versions of CNN were implemented, and their evaluation measures were discussed.…”