JUCS - Journal of Universal Computer Science 28(3): 311-328, doi: 10.3897/jucs.80733

Water stress classification using Convolutional Deep Neural Networks

Lerina Aversano^‡, Mario Luca Bernardi^‡, Marta Cimitile^§

‡ University of Sannio, Benevento, Italy§ Unitelma Sapienza, Rome, Italy

Corresponding author: Lerina Aversano ( aversano@unisannio.it )

Corresponding author: Mario Luca Bernardi ( bernardi@unisannio.it )

Corresponding author: Marta Cimitile ( marta.cimitile@unitelmasapienza.it )

This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY-ND 4.0). This license allows reusers to copy and distribute the material in any medium or format in unadapted form only, and only so long as attribution is given to the creator. The license allows for commercial use.

Citation: Aversano L, Bernardi ML, Cimitile M (2022) Water stress classification using Convolutional Deep Neural Networks. JUCS - Journal of Universal Computer Science 28(3): 311-328. https://doi.org/10.3897/jucs.80733

Abstract

In agriculture, given the global water scarcity, optimizing the irrigation system have become a key requisite of any semi-automatic irrigation scheduling system. Using efficient assessment methods for crop water stress allows reduced water consumption as well as improved quality and quantity of the production. The adoption of Neural Network can support the automatic in situ continuous monitoring and irrigation through the real-time classification of the plant water stress. This study proposes an end-to-end automatic irrigation system based on the adoption of Deep Neural Networks for the multinomial classification of tomato plants’ water stress based on thermal and optical aerial images. This paper proposes a novel approach that cover three important aspects: (i) joint usage of optical and thermal camera, captured by un-manned aerial vehicles (UAVs); (ii) strategies of image segmentation in both thermal imaging used to obtain images that can remove noise and parts not useful for classifying water stress; (iii) the adoption of deep pre-trained neural ensembles to perform effective classification of field water stress. Firstly, we used a multi-channel approach based on both thermal and optical images gathered by a drone to obtain a more robust and broad image extraction. Moreover, looking at the image processing, a segmentation and background removal step is performed to improve the image quality. Then, the proposed VGG-based architecture is designed as a combination of two different VGG instances (one for each channel). To validate the proposed approach a large real dataset is built. It is com- posed of 6000 images covering all the lifecycle of the tomato crops captured with a drone thermal and optical photocamera. Specifically, our approach, looking mainly at leafs and fruits status and patterns, is designed to be applied after the plants has been transplanted and have reached, at least, early growth stage (covering vegetative, flowering, friut-formation and mature fruiting stages).

Keywords

intelligent systems, deep learning, smart irrigation, precision agriculture, digital agriculture