Title : Early assessment of nutrient deficiency in fruit crops using Unpiloted Aerial System (UAS) imagery
Abstract:
Nutrient deficiency in fruit crops such as apples, peaches, blueberries and grapes significantly impact yield and quality, needing timely and accurate detection methods. Typically, nutrient management in fruits is done through visual inspection of plants, plant tissue sampling and soil analysis which are time consuming, and not always accurate. Detection of crop stress is one of the major applications of remote sensing in agriculture. The capability of remote sensing using unpiloted aerial system (UAS) based multispectral imagery provides high-resolution images of crops, which enables us to identify and address the issue of crop stress caused by nutrient deficiencies. Several studies have highlighted the effectiveness of these techniques. Drone technologies provide farmers with accurate, cost-effective, and timely tools to manage crops and resources effectively. Identifying nutrient levels in crops before any visual deficiency symptoms appear helps to introduce corrective actions in a timely manner. We conducted a study in fruits- apples, peaches, grapes and blueberries across six different orchards in Connecticut, to demonstrate if the multispectral sensor attached with UAS could early detect nutrient deficiencies. Drone Images were collected in orchards. The collected images were processed to identify spectral responses induced by nutrient deficiency in fruit crops. Tissue analysis was done to build models that correlate drone data with laboratory tissue results. Our result suggests that there is a strong correlation between specific spectral indices and nutrient levels in the crops. Vegetative indices based on red and near infrared regions, such as NDRE and NDVI could detect nutrient deficiencies in fruit crops. Those findings show deficiencies of calcium, zinc, phosphorous and manganese in apples, potassium deficiency in blueberries, calcium deficiency in peaches and calcium, magnesium and Manganese deficiencies in Grapes. Thus, the study underscores effectiveness of UAS-based imagery in early assessing nutrient deficiencies in fruit crops.
Key words: Unpiloted Aerial Systems, Vegetative indices, Fruits, Remote Sensing.
