Study published in the international journal “Remote sensing” on the use of satellite imaging, relating leaf production data to silkworm rearing results.
“Remote Sensing” is an international peer-reviewed, open access journal about the science and application of remote sensing technology, and is published semimonthly online by MDPI.
Special Issue
This article belongs to the Special Issue “Remote Sensing for Crop Stress Monitoring and Yield Prediction”.
Understanding and predicting crop stress and yield with changing climate is critical for designing effective adaptation and mitigation strategies. Recently, numerous studies have been conducted for multi-scale crop stress monitoring and climate impact assessment, using various data sources and novel algorithms. This Special Issue is designed to synthesize recent advances in utilizing remote sensing for cropland and irrigation mapping, crop growth assessment, crop water and heat stress monitoring, as well as yield prediction. Studies combining remote sensing and process-based/statistical models for better yield prediction under extreme weather (e.g., droughts, floods, heatwaves, heavy winds) and quantifying the associated uncertainties through inter-method and inter-model comparisons are especially welcomed.
Remote Sensing Imaging as a Tool to Support Mulberry Cultivation for Silk Production
ABSTRACT
In recent decades there has been an increasing use of remotely sensed data for precision agricultural purposes. Sericulture, the activity of rearing silkworm (Bombyx mori L.) larvae to produce silk in the form of cocoons, is an agricultural practice that has rarely used remote sensing techniques but that could benefit from them. The aim of this work was to investigate the possibility of using satellite imaging in order to monitor leaf harvesting in mulberry (Morus alba L.) plants cultivated for feeding silkworms; additionally, quantitative parameters on silk cocoon production were related to the analyses on vegetation indices.
Adopting PlanetScope satellite images, four M. alba fields were monitored from the beginning of the silkworm rearing season until its end in 2020 and 2021. The results of our work showed that a decrease in the multispectral vegetation indices in the mulberry plots due to leaf harvesting was correlated with the different parameters of silk cocoons spun by silkworm larvae; in particular, a decrease in the Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) had high correlations with quantitative silk cocoon production parameters (R2 values up to 0.56, p < 0.05). These results led us to the conclusion that precision agriculture can improve sericultural practice, offering interesting solutions for estimating the quantity of produced silk cocoons through the remote analysis of mulberry fields.
FUNDING
This research was funded by Veneto Region, Measure 16.1-2 Programme of Rural Development for the Veneto Region, 2014-2020-DGR 2175 del 23 December 2016, grant number:55-04/12/2017 SERINNOVATION.
Authors
Domenico Giora (1), Alberto Assirelli (2), Silvia Cappellozza (3), Luigi Sartori (1), Alessio Saviane (3), Francesco Martinello (1), José A. Martínez-Casasnovas (4,5).
(1) Department of Land, Environment, Agriculture and Forestry (TeSAF) – University of Padua, Agripolis – Italy
(2) Council for Agricultural Research and Economics, Research Centre for Engineering and Agro-Food Processing (CREA-IT) – Monterotondo, Rome – Italy
(3) Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment (CREA-AA) – Sericulture Laboratory of Padua – Italy
(4) Research Group in AgroICT and Precision Agriculture (GRAP), Agrotecnio CENTRA-Center, University of Lleida – Spain
(5) Department of Environment and Soil Science, University of Lleida – Spain