E-Agriculture Planning Tool for Supporting Smallholder Cocoa Intensification Using Remotely Sensed Data

Abstract

Remote sensing approaches are often used to monitor land cover change. However, the small physical size (about 1–2 hectare area) of smallholder orchards and the cultivation of cocoa (Theobroma cocoa L.) under shade trees make the use of many popular satellite sensors inefficient to distinguish cocoa orchards from forest areas. Nevertheless, high-resolution satellite imagery combined with novel signal extraction methods facilitates the differentiation of coconut palms (Cocos nucifera L.) from forests. Cocoa grows well under established coconut shade, and underplanting provides a viable opportunity to intensify production and meet demand and government targets. In this study, we combined grey-level co-occurrence matrix (GLCM) textural features and vegetation indices from Sentinel datasets to evaluate the sustainability of cocoa expansion given land suitability for agriculture and soil capability classes. Additionally, it sheds light on underexploited areas with agricultural potential. The mapping of areas where cocoa smallholder orchards already exist or can be grown involved three main components. Firstly, the use of the fine-resolution C-band synthetic aperture radar and multispectral instruments from Sentinel-1 and Sentinel-2 satellites, respectively. Secondly, the processing of imagery (Sentinel-1 and Sentinel-2) for feature extraction using 22 variables. Lastly, fitting a random forest (RF) model to detect and distinguish potential cocoa orchards from non-cocoa areas. The RF classification scheme differentiated cocoa (for consistency, the coconut–cocoa areas in this manuscript will be referred to as cocoa regions or orchards) and non-cocoa regions with 97 percent overall accuracy and over 90 percent producer’s and user’s accuracies for the cocoa regions when trained on a combination of spectral indices and GLCM textural feature sets. The top five variables that contributed the most to the model were the red band (B4), red edge curve index (RECI), blue band (B2), near-infrared (NIR) entropy, and enhanced vegetation index (EVI), indicating the importance of vegetation indices and entropy values. By comparing the classified map created in this study with the soil and land capability legacy information of Bougainville, we observed that potential cocoa regions are already rated as highly suitable. This implies that cocoa expansion has reached one of many intersecting limits, including land suitability, political, social, economic, educational, health, labour, and infrastructure. Understanding how these interactions limit cocoa productivity at present will inform further sustainable growth. The tool provides inexpensive and rapid monitoring of land use, suitable for a sustainable planning framework that supports responsible agricultural land use management. The study developed a heuristic tool for monitoring land cover changes for cocoa production, informing sustainable development that balances the needs and aspirations of the government and farming communities with the protection of the environment.

Keywords

• cocoa detection
• land capability
• uncertainty
• GLCM
• Google Earth Engine
• random forest