Identification of Plant Diseases in Jordan Using Convolutional Neural Networks.

In the realm of global food security, plants serve as the primary source of sustenance. However, plant diseases pose a significant threat to this security. The process for diagnosing these diseases forms the bedrock of disease control efforts. The precision and expediency of these diagnoses wield substantial influence over disease management and the consequent reduction of economic losses. This research endeavors to diagnose the prevalent crops in Jordan, as identified by the Jordanian Department of Statistics for the year 2019. These crops encompass four key agricultural varieties: cucumbers, tomatoes, lettuce, and cabbage. To facilitate this, a novel dataset known as "Jordan22" was meticulously curated. Jordan22 was compiled by collecting images of diseased and healthy plants captured on Jordanian farms. These images underwent meticulous classification by a panel of three agricultural specialists well-versed in plant disease identification and prevention. The Jordan22 dataset comprises a substantial size, amounting to 3210 images. The results yielded by the CNN were remarkable, with a test accuracy rate reaching an impressive 0.9712. Optimal performance was observed when images were resized to 256 × 256 dimensions, and max pooling was used instead of average pooling. Furthermore, the initial convolutional layer was set at a size of 32, with subsequent convolutional layers standardized at 128 in size. In conclusion, this research represents a pivotal step towards enhancing plant disease diagnosis and, by extension, global food security. Through the creation of the Jordan22 dataset and the meticulous training of a CNN model, we have achieved substantial accuracy in disease detection, paving the way for more effective disease management strategies in agriculture. [ABSTRACT FROM AUTHOR]