# Systematic Mapping Study on Remote Sensing in Agriculture
Classification techniques are the other major category, which have been used for weed detection [61; 62], identification and quantification of the leaf area [63], disease detection [26; 64], and identification of rapeseed [65]. Some of the most common classification techniques are listed as follows. * Artificial Neural Networks (ANN). ANN models have shown great potential in various RS applications in PA. For example, [PERSON] et al. [66] used an ANN to compute surface soil moisture. [PERSON] et al. [67] developed an ANN system to predict vine water status. In [68], the authors used ANNs to separate maize plants from weeds. * Random Forest algorithm (RF). It is an ensemble classification model that consists of a set of randomized decision trees. It has been used in [35] to estimate biomass and the amount of nitrogen in plants. In [36], RFs are applied to estimate the content of nitrogen in the leaf of the plants. * Support vector machines (SVM), naive Bayes classifier, and k-mean clustering. These methods have also been applied in different areas of agricultural machine learning systems. [PERSON] et al. [69] proposed a SVM classifier to detect diseases in pomegranate leaves at an early stage. [PERSON] et al. [70] presented a SVM-based technique for detecting diseases in tomato leaves. k-Nearest neighbors algorithm (kNN) was used in [71] to classify large agricultural land cover types. A system to discriminate weeds from crops using naive Bayesian classifiers is presented in [72]. Moreover, in [73], [PERSON] et al. proposed a naive Bayes classifier to detect gourd leaf diseases using color and texture features. * Deep Learning (DL). The use of DL in agriculture is a recent and promising alternative to traditional methods [74; 75]. It has been used in several applications in the domain of PA. For example, a fully convolutional neural network for mapping weed is used in [76]. [PERSON] et al. [77] used a CNN model for the classification of crops using multitemporal optical and SAR data. [PERSON] et al. [78] addressed the problem of segmenting mixed crops applying CNN methods. [PERSON] et al. [20] proposed a deep learning-based CNN algorithm to classify weeds from grass and broadleaf. Moreover, [PERSON] et al. [79] dealt with the crop mapping problem using a multi-level DL network. This paper describes a systematic mapping study in the area of remote sensing in agriculture. Many recent and interesting review papers can be found in the literature regarding RSA research [43; 45; 74; 80; 81; 82; 83]. However, the present paper is the first to adopt the form of a systematic mapping. These studies are characterized by following a formalized methodology, whose objective is to find the current trends in techniques, problems, applications, publication channels, etc., obtaining recommendations for researchers and practitioners in this area. The rest of the paper is organized as follows. In Section 2, the steps of the methodology used are explained. Then, Section 3 presents the quantitative results of the study. The main findings, suggestions, and limitations are discussed in Section 4. Finally, the conclusions and future perspectives are drawn in Section 5.