Region-aggregated attention CNN for disease detection in fruit images

Abstract

Background Diseases and pests have a profound effect on a yearly harvest and productivity in agriculture. A precise and accurate detection of the diseases and pests could facilitate timely treatment and management of the diseases and pests and lessen the resultant loss in economy and health. Herein, we propose an improved design of the disease detection system for plant images. Methods Built upon the two-stage framework of object detection neural networks such as Mask R-CNN, the proposed network involves three types of extensions, including the addition of additional level of feature pyramids to improve the exploration and proposal of candidate regions, the aggregation of feature maps from all levels of feature pyramids per candidate region to fully exploit the information from feature pyramids, and the introduction of a squeeze-and-excitation block to the construction of feature pyramids and the aggregated feature maps to improve the representation of feature maps. Results The proposed network was evaluated using 74 images of infected apple fruits. In 3-fold cross-validation, the proposed network achieved averaged precision (AP) of 72.26, AP at 0.5 threshold of 88.51 and AP at 0.75 threshold of 82.30. In the comparative experiments, the proposed network outperformed the other competing networks. The utility of the three extensions was also demonstrated in comparison to Mask R-CNN. Conclusions The experimental results suggest that the proposed network could identify and localize the symptom of the disease with high accuracy, leading to an early diagnosis and treatment of the disease, and thus holding the potential for improving crop yield and quality.

The Need for Machines for the Nondestructive Quality Assessment of Potatoes with the Use of Artificial Intelligence Methods and Imaging Techniques

“…Neural networks have been used for many years in agricultural and food industries, for example, for classifying the quality of beans [ 74 ], citrus fruits [ 75 ], apples [ 76 ], plums, tomatoes [ 77 ], coffee beans [ 74 , 78 ], and strawberries [ 79 ], or detecting plant diseases on plantations [ 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 ] due to the ease of solving troublesome, difficult, or multidimensional linear and nonlinear problems, which, using traditional software techniques, require the use of complicated algorithms and, thus, a lot of time and dedication.…”