# Algorithms for Plant Monitoring Applications: A Comprehensive Review
## 3 Literature Analysis
### Algorithms
#### 3.2.4 Deep Learning  
In this group, a relevant predominance of the CNN is observed; see Figure 12. One of the main methods in agronomical research is the application of computer vision technology to detect plant diseases. Although classic computer vision is still used [63], the convolutional operator in neural networks has become very popular, thanks to the ability of CNN in images processing. Occasionally, different models can be used in conjunction with CNN.  
As in classic machine learning, hyper-spectral images are also widely used in deep learning: the authors in [155; 156; 157; 158; 159; 160; 161] propose different architectures, optimization methods, and loss functions. Hyper-spectral features can be used for disease detection on wheat [162], but also for crop mapping, thanks to the application of modern Encoder-Decoder (ED) architectures [163]. Nevertheless, many papers show the application of CNNs to classic RGB pictures captured by UAVs or directly on the field, exploiting both predefined network architectures and novel ones. Applications include plant height identification [164], flowering [165], disease detection [166; 167; 168; 169; 170], and mapping [171]. Crops involved in disease detection are maize [172; 173], rice [174; 175; 176], wheat [177], tomato [178; 179; 180], beans [181], apple [182; 183], stone fruits [184; 185], grapes [186; 187], banana [188; 189], sugarcane [190], forest trees [191; 192], tropical trees [193; 194], and tea [195; 196]. [PERSON] et al. in [197] use Single Shot Detector (SSD) to detect tomato diseases while [PERSON] et al. in [198] exploit Siamese networks.  
An interesting characteristic of CNNs is its ability to automatically extract the most relevant features from an image. Nevertheless, in some cases, image preprocessing can be useful: [PERSON] et al. in [179] use the Asymptotic Non-Local Means algorithm for denoising the input samples.  
Aside from classification, CNNs are used in image segmentation: [PERSON] et al. in [199] evaluate different network architectures for both purposes. CNNs can also be used for other goals; [PERSON] et al. in [200] perform a simultaneous identification of tomato leaf diseases and fruit counting, while [PERSON] et al. in [201] count tobacco lesions in complex agricultural settings.  
[PERSON] et al. in [202] focus on possible issues that arise while applying incremental learning to identify new diseases from unseen data. In general, the importance of having a well-crafted dataset is discussed in [203]. Occasionally, researchers face the problem of unbalanced data, especially when building custom ones. In these situations, the training phase may suffer from poor generalization or unbalanced label relevance underestimation. [PERSON] et al. in [204] propose a comprehensive method to deal with this problem, by using Generative Adversarial Network (GAN) to increase the amount of unbalanced class samples. Indeed, this kind of neural network, trained on a target dataset, learns how to generate new data with the same statistical characteristics. In [205,206], GANs are used to expand a dataset of leaf images, avoiding the general struggle of dealing with unlabeled data. In general, dataset availability remains a common problem in machine learning and deep learning. Some studies exploit public ones like PlantVillage [207, 208, 209, 210], whereas others combine different datasets in order to achieve more generalization [112,211]. Platforms like Kaggle are also employed [212], while [PERSON] et al. [213] make use of datasets extracted from the literature. Indeed, many authors build custom datasets, making them available for future use [214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227].  
Figure 12: Histogram showing the recurrence of deep learning algorithms among those used in the reviewed studies.  
Multi-task deep learning is leveraged with multimodal data fusion for innovative applications like monitoring the growth of film-mulched winter wheat [228].