Abstract
Areca yellow leaf disease is a major attacker of the planting and production of arecanut. The continuous expansion of arecanut (Areca catechu L.) planting areas in Hainan has placed a great need to strengthen the monitoring of this disease. At present, there is little research on the monitoring of areca yellow leaf disease. PlanetScope imagery can achieve daily global coverage at a high spatial resolution (3 m) and is thus suitable for the high-precision monitoring of plant pest and disease. In this paper, PlanetScope images were employed to extract spectral features commonly used in disease, pest and vegetation growth monitoring for primary models. In this paper, 13 spectral features commonly used in vegetation growth and pest monitoring were selected to form the initial feature space, followed by the implementation of the Correlation Analysis (CA) and independent t-testing to optimize the feature space. Then, the Random Forest (RF), Backward Propagation Neural Network (BPNN) and AdaBoost algorithms based on feature space optimization to construct double-classification (healthy, diseased) monitoring models for the areca yellow leaf disease. The results indicated that the green, blue and red bands, and plant senescence reflectance index (PSRI) and enhanced vegetation index (EVI) exhibited highly significant differences and strong correlations with healthy and diseased samples. The RF model exhibits the highest overall recognition accuracy for areca yellow leaf disease (88.24%), 2.95% and 20.59% higher than the BPNN and AdaBoost models, respectively. The commission and omission errors were lowest with the RF model for both healthy and diseased samples. This model also exhibited the highest Kappa coefficient at 0.765. Our results exhibit the feasible application of PlanetScope imagery for the regional large-scale monitoring of areca yellow leaf disease, with the RF method identified as the most suitable for this task. Our study provides a reference for the monitoring, a rapid assessment of the area affected and the management planning of the disease in the agricultural and forestry industries.
Highlights
Arecanut is a major economic crop in the tropical and subtropical regions of China
Despite the great progress made by the aforementioned research, the studies generally focus on field crops such as wheat, while the remote sensing monitoring of areca yellow leaf disease is limited in some regions with fragmented plots and cloudy and rainy weather
Compared with field crops such as wheat, the remote sensing monitoring of areca yellow leaf disease is limited in some regions with fragmented plots and cloudy and rainy weather
Summary
Arecanut is a major economic crop in the tropical and subtropical regions of China. The continuous and rapid development of the arecanut cultivation industry has increased its cultivation area, enhancing the problems associated with arecanut disease hazards [1]. There is no direct and effective agent for the eradication of areca yellow leaf disease, and it should be cut down in time to prevent the spread once it is found. This disease is the principle limiting factor of arecanut production and cultivation [3]. Current conventional monitoring, forecasting, prevention and control approaches of areca yellow leaf disease is relatively traditional. Such approaches generally focus on point-measurements, while large-scale monitoring and reporting methods are lacking [4]. Traditional manual survey methods are time-consuming, labor-intensive, inaccurate, and can cause varying degrees of damage to crops
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