Evaporation Forecasting through Interpretable Data Analysis Techniques

Abstract

Climate change is increasing temperatures and causing periods of water scarcity in arid and semi-arid climates. The agricultural sector is one of the most affected by these changes, having to optimise scarce water resources. An important phenomenon within the water cycle is the evaporation from water reservoirs, which implies a considerable amount of water lost during warmer periods of the year. Indeed, evaporation rate forecasting can help farmers grow crops more sustainably by managing water resources more efficiently in the context of precision agriculture. In this work, we expose an interpretable machine learning approach, based on a multivariate decision tree, to forecast the evaporation rate on a daily basis using data from an Internet of Things (IoT) infrastructure, which is deployed on a real irrigated plot located in Murcia (southeastern Spain). The climate data collected feed the models that provide a forecast of evaporation and a summary of the parameters involved in this process. Finally, the results of the interpretable presented model are validated with the best literature models for evaporation rate prediction, i.e., Artificial Neural Networks, obtaining results very similar to those obtained for them, reaching up to 0.85R2 and 0.6MAE. Therefore, in this work, a double objective is faced: to maintain the performance obtained by the models most frequently used in the problem while maintaining the interpretability of the knowledge captured in it, which allows better understanding the problem and carrying out appropriate actions.

Study of applications of Internet of Things and Machine Learning for Smart Agriculture

“…Different sensor nodes, network layer protocols, cloud services and ML algorithms developed for smart agriculture applications viz. irrigation monitoring ( [1], [26], [27], [34], [43], [44]), production process management ( [28], [29], [41]), plant growth and disease monitoring ( [30], [31], [32], [38], [39], [42]) and precision agriculture ( [33], [34], [40]) are considered in Table III.…”

Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

“…However, due to the black box nature of most AI models, users cannot understand the connections between features. This is crucial when the system is designed to simulate physical farming events with socioeconomic effects like evaporation [242]. Therefore, many researchers are working on the implementation potentials of XAI applied in smart farming cyber security.…”

“…Nidhi et al [242] presented an IoT and XAI-based framework to detect plant diseases such as rust and blast in pearl millet. Parametric data from the pearl millet farmland at ICAR, Mysore, India was utilized to train the proposed Custom-Net Deep Learning Models, reaching a classification accuracy of 98.78% which is similar to state-of-the-art models including Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19 and superior to them in terms of reducing the training time by 86.67%.…”

Explainable Artificial Intelligence and Interpretable Machine Learning for Agricultural Data Analysis

“…In the agricultural domain, several previous studies have started applying the techniques since 2020 (Fig. 1): Crop yield estimate (Sihi et al, 2022;Wolanin et al, 2020); crop type and trait classification using satellite (Newman and Furbank, 2021;Orynbaikyzy et al, 2020); soil texture classification (Zhou et al, 2022); leaf disease classification (Wei et al, 2022); water flux and quality assessment (Garrido et al, 2022;Zhang et al, 2022); IoT based smart agriculture system (Sabrina et al, 2022); biomethane production (De Clercq et al, 2020); agricultural land identification (Viana et al, 2021). However, these studies use only a few particular methods.…”