Crop Stress Detection and Classification Using Hyperspectral Remote Sensing

Agricultural production has observed many changes in technology over the last 20 years. Producers are able to utilize technologies such as site-specific applicators and remotely sensed data to assist with decision making for best management practices which can improve crop production and provide protection to the environment. It is known that plant stress can interfere with photosynthetic reactions within the plant and/or the physical structure of the plant. Common types of stress associated with agricultural crops include herbicide induced stress, nutrient stress, and drought stress from lack of water. Herbicide induced crop stress is not a new problem. However, with increased acreage being planting in varieties/hybrids that contain herbicide resistant traits, herbicide injury to non-target crops will continue to be problematic for producers. With rapid adoption of herbicide-tolerant cropping systems, it is likely that herbicide induced stress will continue to be a major concern. To date, commercially available herbicide-tolerant varieties/hybrids contain traits which allow herbicides like glyphosate and glufosinate-ammonium to be applied as a broadcast application during the growing season. Both glyphosate and glufosinate-ammonium are broad spectrum herbicides which have activity on a large number of plant species, including major crops like non-transgenic soybean, corn, and cotton. Therefore, it is possible for crop stress from herbicide applications to occur in neighboring fields that contain susceptible crop varieties/hybrids. Nutrient and moisture stress as well as stress caused by herbicide applications can interact to influence yields in agricultural fields. If remotely sensed data can be used to accurately identify specific levels of crop stress, it is possible that producers can use this information to better assist them in crop management to maximize yields and protect their investments. This research was conducted to evaluate classification of specific crop stresses utilizing hyperspectral remote sensing

Crop Stress Detection and Classification Using Hyperspectral Remote Sensing

Agricultural production has observed many changes in technology over the last 20 years. Producers are able to utilize technologies such as site-specific applicators and remotely sensed data to assist with decision making for best management practices which can improve crop production and provide protection to the environment. It is known that plant stress can interfere with photosynthetic reactions within the plant and/or the physical structure of the plant. Common types of stress associated with agricultural crops include herbicide induced stress, nutrient stress, and drought stress from lack of water. Herbicide induced crop stress is not a new problem. However, with increased acreage being planting in varieties/hybrids that contain herbicide resistant traits, herbicide injury to non-target crops will continue to be problematic for producers. With rapid adoption of herbicide-tolerant cropping systems, it is likely that herbicide induced stress will continue to be a major concern. To date, commercially available herbicide-tolerant varieties/hybrids contain traits which allow herbicides like glyphosate and glufosinate-ammonium to be applied as a broadcast application during the growing season. Both glyphosate and glufosinate-ammonium are broad spectrum herbicides which have activity on a large number of plant species, including major crops like non-transgenic soybean, corn, and cotton. Therefore, it is possible for crop stress from herbicide applications to occur in neighboring fields that contain susceptible crop varieties/hybrids. Nutrient and moisture stress as well as stress caused by herbicide applications can interact to influence yields in agricultural fields. If remotely sensed data can be used to accurately identify specific levels of crop stress, it is possible that producers can use this information to better assist them in crop management to maximize yields and protect their investments. This research was conducted to evaluate classification of specific crop stresses utilizing hyperspectral remote sensing

Evaluating Soybean Cultivars for Low- and High-Temperature Tolerance During the Seedling Growth Stage

Soybean (Glycine max L.) seedlings may be exposed to low or high temperatures under early or conventional soybean production systems practiced in the US Midsouth. However, a wide range of soybean cultivars commonly grown in the region may inherit diverse tolerance to degrees of temperatures. Therefore, a study was conducted in a controlled-environment facility to quantify 64 soybean cultivars from Maturity Group III to V, to low (LT; 20/12 °C), optimum (OT; 30/22 °C), and high (HT; 40/32 °C) temperature treatments during the seedling growth stage. Several shoot, root, and physiological parameters were assessed at 20 days after sowing. The study found a significant decline in the measured root, shoot, and physiological parameters at both low and high temperatures, except for root average diameter (RAD) and lateral root numbers under LT effects. Under HT, shoot growth was significantly increased, however, root growth showed a significant reduction. Maturity group (MG) III had significantly lower values for the measured root, shoot, and physiological traits across temperature treatments when compared with MG IV and V. Cultivar variability existed and reflected considerably through positive or negative responses in growth to LT and HT. Cumulative stress response indices and principal component analysis were used to identify cultivar-specific tolerance to temperatures. Based on the analysis, cultivars CZ 5225 LL and GS47R216 were identified as most sensitive and tolerant to LT, while, cultivars 45A-46 and 5115LL identified as most tolerant and sensitive to HT, respectively. The information on cultivar-specific tolerance to low or high temperatures obtained in this study would help in cultivar selection to minimize stand loss in present production areas

Effects of Purple Seed Stain on Seed Quality and Composition in Soybean

Purple seed stain disease, caused by (Cercospora kukuchii), is a major concern in soybean (Glycine max (L.)) in Mississippi, USA, due to its effects on seed quality, reducing soybean seed grade and potential market price at elevators. Therefore, investigating the effects of purple seed stain (PSS) on seed quality (germination and vigor) and seed composition (nutrition) is critical. The objective of this study was to investigate the effects of PSS on seed harvest index, seed germination, seed vigor, and seed composition components (protein, oil, fatty acids, and sugars). A field experiment was initiated in 2019 in Stoneville, MS, at the Delta Research and Extension Center (DREC) on a Commerce silt loam soil (fine-silty, mixed, superactive, nonacid, thermic Fluventic Epiaquepts). Soybean variety Credenz 4748 LL was used. The results showed that infected (symptomatic) seed had a 5.5% greater Seed Index (based on 100 seed weight) when compared to non-infected (non-symptomatic, as control) seed. Non-infected seed had greater percent germination and seedling vigor when compared to infected seed. Germination was 30.9% greater and vigor was 58.3% greater in non-infected seed. Also, the results showed that infected seed with PSS had higher protein content and some amino acids. No changes in total oil and fatty acids. Sucrose and stachyose were lower in infected seed than in non-infected seed. The research showed that PSS impacted seed health and seed quality (germination and vigor) and seed composition (protein, sugars, and some amino acids). Purple stained seed should be avoided when planting and should be managed properly as low germination is a potential risk. Planting population should be adjusted accordingly due to lack of germination and vigor if PSS is present. This research help growers for purple seed management, and scientists to further understand the potential negative impact on seed quality and nutrition. Further research is needed before conclusive recommendations are made