AI-powered banana diseases and pest detection

Abstract

Background: Banana (Musa spp.) is the most popular marketable fruit crop grown all over the world, and a dominant staple food in many developing countries. Worldwide, banana production is affected by numerous diseases and pests. Novel and rapid methods for the timely detection of pests and diseases will allow to surveil and develop control measures with greater efficiency. As deep convolutional neural networks (DCNN) and transfer learning has been successfully applied in various fields, it has freshly moved in the domain of just-in-time crop disease detection. The aim of this research is to develop an AI-based banana disease and pest detection system using a DCNN to support banana farmers. Results: Large datasets of expert pre-screened banana disease and pest symptom/damage images were collected from various hotspots in Africa and Southern India. To build a detection model, we retrained three different convolutional neural network (CNN) architectures using a transfer learning approach. A total of six different models were developed from 18 different classes (disease by plant parts) using images collected from different parts of the banana plant. Our studies revealed ResNet50 and InceptionV2 based models performed better compared to MobileNetV1. These architectures represent the state-of-the-art results of banana diseases and pest detection with an accuracy of more than 90% in most of the models tested. These experimental results were comparable with other state-of-the-art models found in the literature. With a future view to run these detection capabilities on a mobile device, we evaluated the performance of SSD (single shot detector) MobileNetV1. Performance and validation metrics were also computed to measure the accuracy of different models in automated disease detection methods. Conclusion: Our results showed that the DCNN was a robust and easily deployable strategy for digital banana disease and pest detection. Using a pre-trained disease recognition model, we were able to perform deep transfer learning (DTL) to produce a network that can make accurate predictions. This significant high success rate makes the model a useful early disease and pest detection tool, and this research could be further extended to develop a fully automated mobile app to help millions of banana farmers in developing countries.

Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

“…Most of the recently presented models, including YOLO-v3 and R-CNN family architectures, are supported by the FPN model, which enables small object detection and enhances semantic segmentation and multi-object detection (as shown in Table 1 [ 107 , 108 , 110 , 111 , 113 , 114 , 117 , 118 ]).…”

Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz)

“…[79]. Early detection and crop management associated with yield limitations can help increase productivity [4,23,80]. Crop yield prediction models could aid in early decision-making, optimizing the time required for field evaluation, thus reducing the resources allocated to the research programs [81].…”

Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz)

“…[79]. Early detection and crop management associated with yield limitations can help increase productivity [4,23,80]. Crop yield prediction models could aid in early decision-making, optimizing the time required for eld evaluation, thus reducing the resources allocated to the research programs [81].…”