Open source disease analysis system of cactus by artificial intelligence and image processing

Abstract

There is a growing interest in cactus cultivation because of numerous cacti uses from houseplants to food and medicinal applications. Various diseases impact the growth of cacti. To develop an automated model for the analysis of cactus disease and to be able to quickly treat and prevent damage to the cactus. The Faster R-CNN and YOLO algorithm technique were used to analyze cactus diseases automatically distributed into six groups: 1) anthracnose, 2) canker, 3) lack of care, 4) aphid, 5) rusts and 6) normal group. Based on the experimental results the YOLOv5 algorithm was found to be more effective at detecting and identifying cactus disease than the Faster R-CNN algorithm. Data training and testing with YOLOv5S model resulted in a precision of 89.7% and an accuracy (recall) of 98.5%, which is effective enough for further use in a number of applications in cactus cultivation. Overall the YOLOv5 algorithm had a test time per image of only 26 milliseconds. Therefore, the YOLOv5 algorithm was found to suitable for mobile applications and this model could be further developed into a program for analyzing cactus disease.

Wild Animals Detection Based on YOLOv5

“…In this paper, based on the performance requirements for detection of wild animals, the YOLOv5 algorithm belongs to one-stage detection is selected to achieve the target detection of wild animals. After several generations of improvement, YOLO algorithm has introduced many mainstream ideas and methods, achieving a satisfactory balance between its running speed and accuracy [4,5], making it relatively easier to carry on different devices. Based on the model construction, we analyzed the animal detection accuracy of YOLOv5 in different field scenarios in detail to verify the feasibility of this method.…”