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Sensors 2017, 17(5), 1095; doi:10.3390/s17051095

A Case Study of Improving Yield Prediction and Sulfur Deficiency Detection Using Optical Sensors and Relationship of Historical Potato Yield with Weather Data in Maine

1
Department of Cooperative Extension, University of Maine, 57 Houlton Rd, Presque Isle, ME 04769, USA
2
Maine Potato Board, 744 Main Street, Suite 1, Presque Isle, ME 04769, USA
3
Department of Ecosystem Science and Management, 248 Ag Sciences and Industrial Building, Pennsylvania State University, University Park, PA 16802, USA
*
Authors to whom correspondence should be addressed.
Academic Editor: Simon X. Yang
Received: 6 April 2017 / Revised: 2 May 2017 / Accepted: 8 May 2017 / Published: 11 May 2017
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Abstract

In Maine, potato yield is consistent, 38 t·ha−1, for last 10 years except 2016 (44 t·ha−1) which confirms that increasing the yield and quality of potatoes with current fertilization practices is difficult; hence, new or improvised agronomic methods are needed to meet with producers and industry requirements. Normalized difference vegetative index (NDVI) sensors have shown promise in regulating N as an in season application; however, using late N may stretch out the maturation stage. The purpose of the research was to test Trimble GreenSeeker® (TGS) and Holland Scientific Crop Circle™ ACS-430 (HCCACS-430) wavebands to predict potato yield, before the second hilling (6–8 leaf stage). Ammonium sulfate, S containing N fertilizer, is not advised to be applied on acidic soils but accounts for 60–70% fertilizer in Maine’s acidic soils; therefore, sensors are used on sulfur deficient site to produce sensor-bound S application guidelines before recommending non-S-bearing N sources. Two study sites investigated for this research include an S deficient site and a regular spot with two kinds of soils. Six N treatments, with both calcium ammonium nitrate and ammonium nitrate, under a randomized complete block design with four replications, were applied at planting. NDVI readings from both sensors were obtained at V8 leaf stages (8 leaf per plant) before the second hilling. Both sensors predict N and S deficiencies with a strong interaction with an average coefficient of correlation (r2) ~45. However, HCCACS-430 was observed to be more virtuous than TGS. The correlation between NDVI (from both sensors) and the potato yield improved using proprietor-proxy leaf area index (PPLAI) from HCCACS-430, e.g., r2 value of TGS at Easton site improve from 48 to 60. Weather data affected marketable potato yield (MPY) significantly from south to north in Maine, especially precipitation variations that could be employed in the N recommendations at planting and in season application. This case study addresses a substantial need to revise potato N recommendations at planting and develop possible in season N recommendation using ground based active optical (GBAO) sensors. View Full-Text
Keywords: NDVI; sensor; nitrogen; sulfur; leaf area index and weather NDVI; sensor; nitrogen; sulfur; leaf area index and weather
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MDPI and ACS Style

Sharma, L.K.; Bali, S.K.; Dwyer, J.D.; Plant, A.B.; Bhowmik, A. A Case Study of Improving Yield Prediction and Sulfur Deficiency Detection Using Optical Sensors and Relationship of Historical Potato Yield with Weather Data in Maine. Sensors 2017, 17, 1095.

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