On Using Deep Artificial Intelligence to Automatically Detect Apple Diseases from Leaf Images

Abstract

Plant diseases, if misidentified or ignored, can drastically reduce production levels and harvest quality. Technology in the form of artificial intelligence applications has the potential to facilitate and improve the disease identification process, which in turn will empower prompt control. More specifically, the work in this paper addressed the identification of three common apple leaf diseases—rust, scab, and black rot. Twelve deep transfer learning artificial intelligence models were customized, trained, and tested with the goal of categorizing leaf images into one of the aforementioned three diseases or a healthy state. A dataset of 3171 leaf images (621 black rot, 275 rust, 630 scab, and 1645 healthy) was used. Extensive performance evaluation revealed the excellent ability of the transfer learning models to achieve high values (i.e., >99%) for F1 score, precision, recall, specificity, and accuracy. Hence, it is possible to design smartphone applications that enable farmers with poor knowledge or limited access to professional care to easily identify suspected infected plants.

Agroclimatic and Phytosanitary Events and Emerging Technologies for Their Identification in Avocado Crops: A Systematic Literature Review

“…Advances in the adaptation of ML and DL model-based techniques [89], along with the development of IoT prototypes for crop data capture [21], are the two fronts that should continue to be studied to obtain improvements in avocado production. Among the applications of emerging technologies in agricultural applications, there is a high use of models based on CNN classifiers for the detection of diseases in the leaves of trees, with research working on the preparation of these models through transfer learning to detect diseases in products such as corn, apples, and tomatoes, with accuracy measurements between 98.6% and 99.4% obtained in the testing of classifiers [90][91][92]. These advances are relevant for the use of emerging technologies in the identification of APEs, and in the specific case of avocado crops, the use of data from the crop and the use of pretrained models with related data should be prioritized since there is a bias in the application of transfer learning to pretrained models for other agricultural products.…”