Using image augmentation techniques and convolutional neural networks to identify insect infestations on tomatoes

Pest insects are a danger to both regional and global food security. In Jordan, the most productive crop is tomato. Jordan's agriculture output is threatened by insect infestations. The study intends to use a deep learning model called convolutional neural networks on a dataset that includes eight categories of insect pest images. A dataset was used and a group of images from reliable sources were added to it. The image collection was analyzed, and an image augmentation technique was used to increase the number of images, which reached 5894 after image augmentation. The data was split among 80 % training and 20 % validation. Convolutional Neural Networks trained on the data achieved 90 % training accuracy, 85 % testing accuracy, and 87 % validation accuracy. A high-accuracy deep learning model was developed that may be utilized on mobile applications to detect pests that affected crops to assist farmers. The original database used was small in size. When tested on deep learning and machine learning systems, the accuracy was very low, reaching 50-60 % without image augmentation, despite image enhancement techniques.