Drone RGB Images as a Reliable Information Source to Determine Legumes Establishment Success

Lorena Parra,Pedro V Mauri,Jaime Lloret,Jose F Marin,Salima Yousfi,David Mostaza-Colado

doi:10.3390/drones5030079

Abstract

The use of drones in agriculture is becoming a valuable tool for crop monitoring. There are some critical moments for crop success; the establishment is one of those. In this paper, we present an initial approximation of a methodology that uses RGB images gathered from drones to evaluate the establishment success in legumes based on matrixes operations. Our aim is to provide a method that can be implemented in low-cost nodes with relatively low computational capacity. An index (B1/B2) is used for estimating the percentage of green biomass to evaluate the establishment success. In the study, we include three zones with different establishment success (high, regular, and low) and two species (chickpea and lentils). We evaluate data usability after applying aggregation techniques, which reduces the picture’s size to improve long-term storage. We test cell sizes from 1 to 10 pixels. This technique is tested with images gathered in production fields with intercropping at 4, 8, and 12 m relative height to find the optimal aggregation for each flying height. Our results indicate that images captured at 4 m with a cell size of 5, at 8 m with a cell size of 3, and 12 m without aggregation can be used to determine the establishment success. Comparing the storage requirements, the combination that minimises the data size while maintaining its usability is the image at 8 m with a cell size of 3. Finally, we show the use of generated information with an artificial neural network to classify the data. The dataset was split into a training dataset and a verification dataset. The classification of the verification dataset offered 83% of the cases as well classified. The proposed tool can be used in the future to compare the establishment success of different legume varieties or species.

Highlights

Nowadays, agriculture is suffering under the extreme pressure to increase its productivity to feed the population while reducing its environmental impacts
Remote sensing based on Unmanned Aerial Vehicles (UAVs) is one of the most used ones to determine the crop status, and its implementation will increase in the decades [2]
We have considered as false positives the pixels with value = 0 composed mainly by pixels of soil or other types of surface that are not green vegetation

Summary

Introduction

Agriculture is suffering under the extreme pressure to increase its productivity to feed the population while reducing its environmental impacts. The predictions indicate an intense population increment [1]. This increase jeopardises food security in many regions, pushing the farmers to maximise productivity. Two options are arising: agroecological practices and monitoring technologies as part of precision agriculture. Both aim to manage the inputs better and to reduce impact while preserving productivity. The use of sensing technologies in agriculture has become a useful tool for monitoring crops. Remote sensing based on Unmanned Aerial Vehicles (UAVs) is one of the most used ones to determine the crop status, and its implementation will increase in the decades [2]. Non-professional UAVs present lower-cost, which facilitates their use by farmers

Objectives

Results

Discussion

Conclusion