# Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0
## 3 Artificial Intelligence and RS  
In this new era of computing, the pace of technological advancement is reaching peaks as it never has before. Artificial intelligence (AI) has vast potential in the agricultural field, although it has not been fully explored in this sphere and is still in its infancy. One of the subsets of AI is machine learning, which is used for operating definite tasks using a computer system [43] and is generally divided into supervised and unsupervised learning techniques. Similarly, neural networks, another basic pillar of AI, provide the basis for deep learning algorithms by recognizing patterns [44]. Although deep neural network (DNN) became famous in 2016, due to engineering complexities they are not very popular today. Instead, convolutional neural networks (CNNs) and recurrent neural network (RNN) architectures are becoming more popular and their application can be found in object detection, classification, segmentation, etc. [28]. It has been previously reported that machine and deep learning techniques are playing pivotal roles in the following three main domains of RS [10]:  
* Classification problems [45; 46];
* Model emulators development [47; 48];
* Complex empirical relationships [49; 50]. Nonetheless, there is plenty of room to make the best use of these techniques that has not previously been undertaken. For example, [PERSON] et al. [18] reported that despite the potential to account for contextual information in space and time based on historical series, machine learning has been mainly exploited to estimate variables at the pixel and instantaneous level. Likewise, it is also important to consider the limitations of artificial intelligence tools applied to agricultural RS through the construction of training databases. The machine learning algorithms, describing the relation of causality between inputs and outputs, often cannot solidify the full complexity of a diverse relationship due to oversimplifications in reality [18; 40]. Such limitations need to be resolved. The 5 th industrial revolution is expected to address the challenges faced in RS to enhance the proficiency and productivity. One of the most important areas in agricultural RS is plant stress detection. In this regard, image classification is the foremost area of deep learning application and gives a diagnostic decision (healthy or diseased) based on the input (one or two related images). For example, A CNN was used to detect and classify individual lesions and spots on a plant leaf, allowing to identify multiple diseases on the same leaf [51]. Researchers reported an increased accuracy of 12% compared to previously reported methods, which was over 75% overall. Similar applications to stress (biotic and abiotic) classification using AI principles have been reported using deep learning principles in apple, cassava, wheat, maize, tomato, soybean, grapevine, coffee, and rice [51; 52; 53; 54; 55; 56; 57]. In the same way, various studies on segmentation and object detection, subsets of deep learning, include studies on cucumber diseases [68], fruit crop diseases [69], rice leaf diseases [70], cucumber leaf disease [71], tomato diseases and pests [57], grapevine esca disease [61], maize disease [72], strawberry disease [73], and sweet pepper disease [74]. Likewise, AI methods have been implied in diverse agricultural contexts, including high-throughput phenotyping [5], yield prediction [75], fruit detection [76], weed detection [77], and biomass studies [78]. Crops undergo a continuous change in their physiological characteristics over their lifecycles, and these physiological changes are greatly affected by stress (biotic or abiotic). AI is paving the way to make detection and identification processes of plant diseases faster,among other RS applications, but it is only based on obtained images, which hinders earlier detection of disease. Similarly, most of the commercial hyperspectral imaging sensors used in RS platforms can only work optimally under laboratory conditions.