Identification and Counting of Coffee Trees Based on Convolutional Neural Network Applied to RGB Images Obtained by RPA

Lucas Santos Santana (), Gabriel Araújo e Silva Ferraz (), Gabriel Henrique Ribeiro dos Santos, Nicole Lopes Bento and Rafael de Oliveira Faria
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Lucas Santos Santana: Department of Agricultural Engineering, Federal University of Lavras, Aquenta Sol, Lavras 37200-900, MG, Brazil
Gabriel Araújo e Silva Ferraz: Department of Agricultural Engineering, Federal University of Lavras, Aquenta Sol, Lavras 37200-900, MG, Brazil
Gabriel Henrique Ribeiro dos Santos: Department of Agricultural Engineering, Federal University of Lavras, Aquenta Sol, Lavras 37200-900, MG, Brazil
Nicole Lopes Bento: Department of Agricultural Engineering, Federal University of Lavras, Aquenta Sol, Lavras 37200-900, MG, Brazil
Rafael de Oliveira Faria: Department of Agricultural Engineering, Federal University of Lavras, Aquenta Sol, Lavras 37200-900, MG, Brazil

Sustainability, 2023, vol. 15, issue 1, 1-17

Abstract: Computer vision algorithms for counting plants are an indispensable alternative in managing coffee growing. This research aimed to develop an algorithm for automatic counting of coffee plants and to determine the best age to carry out monitoring of plants using remotely piloted aircraft (RPA) images. This algorithm was based on a convolutional neural network (CNN) system and Open Source Computer Vision Library (OpenCV). The analyses were carried out in coffee-growing areas at the development stages three, six, and twelve months after planting. After obtaining images, the dataset was organized and inserted into a You Only Look Once (YOLOv3) neural network. The training stage was undertaken using 7458 plants aged three, six, and twelve months, reaching stability in the iterations between 3000 and 4000 it. Plant detection within twelve months was not possible due to crown unification. A counting accuracy of 86.5% was achieved with plants at three months of development. The plants’ characteristics at this age may have influenced the reduction in accuracy, and the low uniformity of the canopy may have made it challenging for the neural network to define a pattern. In plantations with six months of development, 96.8% accuracy was obtained for counting plants automatically. This analysis enables the development of an algorithm for automated counting of coffee plants using RGB images obtained by remotely piloted aircraft and machine learning applications.

Keywords: remote sensing; deep learning; precision coffee-growing; digital agriculture; plant count (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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