Machine Learning in the Classification of Soybean Genotypes for Primary Macronutrients’ Content Using UAV–Multispectral Sensor

Abstract

Using spectral data to quantify nitrogen (N), phosphorus (P), and potassium (K) contents in soybean plants can help breeding programs develop fertilizer-efficient genotypes. Employing machine learning (ML) techniques to classify these genotypes according to their nutritional content makes the analyses performed in the programs even faster and more reliable. Thus, the objective of this study was to find the best ML algorithm(s) and input configurations in the classification of soybean genotypes for higher N, P, and K leaf contents. A total of 103 F2 soybean populations were evaluated in a randomized block design with two repetitions. At 60 days after emergence (DAE), spectral images were collected using a Sensefly eBee RTK fixed-wing remotely piloted aircraft (RPA) with autonomous take-off, flight plan, and landing control. The eBee was equipped with the Parrot Sequoia multispectral sensor. Reflectance values were obtained in the following spectral bands (SBs): red (660 nm), green (550 nm), NIR (735 nm), and red-edge (790 nm), which were used to calculate the vegetation index (VIs): normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), green normalized difference vegetation index (GNDVI), soil-adjusted vegetation index (SAVI), modified soil-adjusted vegetation index (MSAVI), modified chlorophyll absorption in reflectance index (MCARI), enhanced vegetation index (EVI), and simplified canopy chlorophyll content index (SCCCI). At the same time of the flight, leaves were collected in each experimental unit to obtain the leaf contents of N, P, and K. The data were submitted to a Pearson correlation analysis. Subsequently, a principal component analysis was performed together with the k-means algorithm to define two clusters: one whose genotypes have high leaf contents and another whose genotypes have low leaf contents. Boxplots were generated for each cluster according to the content of each nutrient within the groups formed, seeking to identify which set of genotypes has higher nutrient contents. Afterward, the data were submitted to machine learning analysis using the following algorithms: decision tree algorithms J48 and REPTree, random forest (RF), artificial neural network (ANN), support vector machine (SVM), and logistic regression (LR, used as control). The clusters were used as output variables of the classification models used. The spectral data were used as input variables for the models, and three different configurations were tested: using SB only, using VIs only, and using SBs+VIs. The J48 and SVM algorithms had the best performance in classifying soybean genotypes. The best input configuration for the algorithms was using the spectral bands as input.

Hyperspectral Response of the Soybean Crop as a Function of Target Spot (Corynespora cassiicola) Using Machine Learning to Classify Severity Levels

“…The authors of [47] found high accuracy values in the classification of diseased and healthy leaves by using the SVM algorithm, similarly to [44], who used LR to find a disease detection model. The SVM has been proven to be an effective algorithm in several classification tasks, such as classifying soybean genotypes regarding the primary macronutrient contents [48] and classifying soybean genotypes according to their content of industrial grain parameters [10]. In both studies, using the raw information provided by the sensor guaranteed better results for the algorithm.…”

Classification of Soybean Genotypes as to Calcium, Magnesium, and Sulfur Content Using Machine Learning Models and UAV–Multispectral Sensor

“…The formed clusters were used as output variables, while the spectral data were used as input variables for the following classification models in the machine learning analyses: Multilayer Perceptron Artificial Neural Network (ANN, [27]), REPTree Decision Tree Algorithm (DT, [28]), J48 Decision Tree Algorithm (J48, [29]), Logistic Regression (LR, [30]), random forest (RF, [31]), and Support Vector Machine (SVM, [32]). The algorithms were chosen according to those most recently used in the literature [16,33,34]. The inputs tested in the datasets were spectral bands (SBs), vegetation indices (VIs), and the combination of both VIs+SBs.…”

“…In recent literature, there are works that use ML techniques together with multispectral data for various activities linked to phenotyping, such as [14] using leaf reflectance to classify soybean genotypes in terms of industrial characters, reaching levels of correct classification close to 0.9 [15]. Due to these applications, the use of such technologies can be promising in classifying soybean genotypes efficient in nutrient absorption; Santana et al [16] managed to carry out such selection in soybeans for primary macronutrients, achieving greater precision with algorithms such as SVM and J48.…”

A New Approach to Identifying Sorghum Hybrids Using UAV Imagery Using Multispectral Signature and Machine Learning

“…A random cross-validation sampling strategy with k-fold = 10 and 10 repetitions (total of 100 runs) was applied. This strategy was used to evaluate the performance of the six supervised machine learning models, as already reported in other studies [27][28][29]. All model parameters were set according to the default setting of the Weka 3.8.5 software.…”