Currently, estimations of strawberry productivity are conducted manually, which is a laborious and subjective process. The use of more efficient and precise estimation methods would result in better crop management. The objective of this study was to assess the performance of two regression algorithms-Linear Regression and Support Vector Machine—in estimating the average weight and number of fruits and the number of leaves on strawberry plants, using multispectral images obtained by a remotely piloted aircraft (RPA). The experiment, which was conducted in the experimental area of the Botany Laboratory at the Federal University of Uberlândia-Monte Carmelo Campus (Universidade Federal de Uberlândia, Campus Monte Carmelo), was carried out using a randomized block design with six treatments and four replications. The treatments comprised six commercial strawberry varieties: San Andreas, Albion, PR, Festival, Oso Grande, and Guarani. Images were acquired on a weekly basis and then preprocessed to extract radiometric values for each plant in the experimental area. These values were then used to train the production prediction algorithms. During the same period, data on the average fruit weight, number of fruits per plant, and number of leaves were collected. The total fruit weight in the field was 48.08 kg, while the linear regression (LR) and Support Vector Machine (SVM) estimates were 48.04 and 43.09 kg, respectively. The number of fruits obtained in the field was 4585, and the number estimated by LR and SVM algorithms was 4564 and 3863, respectively. The number of leaves obtained in the field was 10,366, and LR and SVM estimated 10,360 and 10,171, respectively. It was concluded that LR and SVM can estimate strawberry production and the number of fruits and leaves using multispectral unmanned aerial vehicle (UAV) images. The LR algorithm was the most efficient in estimating production, with 99.91% accuracy for average fruit weight, 99.55% for the number of fruits and 99.94% for the number of leaves. SVM exhibited 89.62% accuracy for average fruit weight, 84.26% for the number of fruits, and 98.12% for the number of leaves.

Technologies assisted by remote sensing have been developed to quantify the morphophysiological characteristics of plants in a timely and rapid manner. Thus, the purpose of the present study was to evaluate the behavior of strawberry cultivars in terms of production and their phenotypic characterization using multispectral imaging under the environmental conditions of the Brazilian Cerrado biome. The experiment was conducted in the field, at the Federal University of Uberlândia, Campus Monte Carmelo, Minas Gerais state, Brazil. A randomized block experimental design was used with six strawberry cultivars (San Andreas, Albion, PR, Festival, Oso Grande, and Guarani), with four replicates. The variables evaluated were number and mass of fruits per plant; total productivity SPAD index; number, length, and width of leaves, soluble solids content, titratable acidity, vitamin C, anthocyanins, fruit firmness, soil temperature under and above the mulch, leaf temperature, air temperature, and relative air humidity. Multispectral imaging was obtained with unmanned aerial vehicle with a Mapir Survey 3W camera attached to it. The normalized difference vegetation index was calculated, and Pearson’s correlation was performed between the agronomic variables and the spectral bands. The obtained results indicated that temperature and relative air humidity affect strawberry production. Estimation of agronomic variables using multispectral imaging at 78 days after planting is feasible and can help in the selection of new genetic materials.