Development and Validation of a Model Based on Vegetation Indices for the Prediction of Sugarcane Yield

Abstract

Currently, Brazil is the leading producer of sugarcane in the world, with self-sufficiency in the use of ethanol as a biofuel, as well as being one of the largest suppliers of sugar to the world. This study aimed to develop a predictive model for sugarcane production based on data extracted from aerial imagery obtained from drones or satellites, allowing the precise tracking of plant development in the field. A model based on a semiparametric approach associated with the inverse Gaussian distribution applied to vegetation indices (VIs), such as the Normalized Difference Vegetation Index (NDVI) and Visible Atmospherically Resistant Index (VARI), was developed with data from drone images obtained from two field experiments with randomized replications and four sugarcane varieties. These experiments were performed under conditions identical to those applied by sugarcane farmers. Further, the model validation was carried out by scaling up the analyses with data extracted from Sentinel-2 images of several commercial sugarcane fields. Very often, in countries such as Brazil, sugarcane crops occupy extensive areas. Consequently, the development of tools capable of being operated remotely automatically benefits the management of this crop in the field by avoiding laborious and time-consuming sampling and by promoting the reduction of operation costs. The results of the model application in both sources of data, i.e., data from field experiments as well as the data from commercial fields, showed a suitable level of overlap between the data of predicted yield using VIs generated from drone and satellite images with the data of verified yield obtained by measuring the production of experiments and commercial fields, indicating that the model is reliable for forecasting productivity months before the harvest time.

Challenges of Digital Solutions in Sugarcane Crop Production: A Review

“…Among these phases, the process of obtaining the billets is important because their quality, uniformity and distribution have a direct impact on yield [27]. [37], planting [38,39] and post-planting (adapted from [40,41]).…”

“…Despite this cost advantage, a restricted number of Brazilian growers use mechanical seeding since there is a high investment cost for the specific machinery required [45]. In fact, there is [37], planting [38,39] and post-planting (adapted from [40,41]).…”

“…Figure 4. General sugarcane crop production operations: pre-planting[37], planting[38,39] and post-planting (adapted from[40,41]). …”

Development and Performance Evaluation of Low-Cost 2WT-Operated Earthing-Up Machine for Sugarcane Cultivation in Bangladesh

“…Among the SAARC countries, India is the top sugarcane (70 t ha -1 ) pro lowed by Pakistan (60 t ha -1 ), Nepal (45 t ha -1 ), and Bangladesh (42 t ha -1 ) [2]. T yield is led by Peru with 112 t ha -1 and the worldwide average in 2016 was 70.6 2022-23, the sugarcane production in Brazil reached 598.3 million tons, whic higher than the previous year [4]. Brazil more than doubled its sugarcane pro recent decades to meet the demands of global bio-energy and climate change [5].…”

“…The highest yield is led by Peru with 112 t ha −1 and the worldwide average in 2016 was 70.6 t ha −1 [3]. In 2022-23, the sugarcane production in Brazil reached 598.3 million tons, which was 3.9% higher than the previous year [4]. Brazil more than doubled its sugarcane production in recent decades to meet the demands of global bio-energy and climate change mitigation [5].…”