Deep learning (DL) and computer vision applications in precision agriculture have great potential to identify and classify plant and vegetation species. This study presents the applicability of DL modeling with computer vision techniques to analyze the nutrient levels of hydroponically grown four lettuce cultivars (Lactuca sativa L.), namely Black Seed, Flandria, Rex, and Tacitus. Four different nutrient concentrations (0, 50, 200, 300 ppm nitrogen solutions) were prepared and utilized to grow these lettuce cultivars in the greenhouse. RGB images of lettuce leaves were captured. The results showed that the developed DL’s visual geometry group 16 (VGG16) and VGG19 architectures identified the nutrient levels of lettuces with 87.5 to 100% accuracy for four lettuce cultivars, respectively. Convolution neural network models were also implemented to identify the nutrient levels of the studied lettuces for comparison purposes. The developed modeling techniques can be applied not only to collect real-time nutrient data from other lettuce type cultivars grown in greenhouses but also in fields. Moreover, these modeling approaches can be applied for remote sensing purposes to various lettuce crops. To the best knowledge of the authors, this is a novel study applying the DL technique to determine the nutrient concentrations in lettuce cultivars.

Sustainability is a key factor in the development of new materials for plant pots, given the significant environmental impact of traditional plastic-based pots. Researchers have paid attention to developing biodegradable and sustainable alternatives to petroleum-based pots. In this study, two novel bioplastic formulations are developed, which incorporated soy-based by-product fractions to produce plant pots with self-fertilizing capability while also being cost-competitive. A 3D-printing process, fused filament fabrication, is used to produce plant containers from the filaments of soy-based new materials. Further, a small-scale greenhouse experiment is conducted to compare the performance of the soy-based 3D-printed bioplastic pots with pure polylactic acid (PLA) 3D-printed pots and traditional plastic pots, by growing a fruit-bearing plant (tomato) and a flowering plant (zinnia). Plant growth properties and root circling are analyzed, and the results show that the soy-based pots performed comparably to traditional plastic pots, especially in dry conditions, and also reduced root circling. While a more in-depth analysis is necessary, these initial findings suggest that using soy-based fractions and 3D-printing technology could provide a sustainable approach to developing plant pots, which could reduce the environmental impact of plastic-based containers and improve plant health.

Lettuce is an important vegetable in the human diet and is commonly consumed for salad. It is a source of vitamin A, which plays a vital role in human health. Improvements in lettuce production will be needed to ensure a stable and economically available supply in the future. The influence of nitrogen (N), phosphorus (P), and potassium (K) compounds on the growth dynamics of four hydroponically grown lettuce (Lactuca sativa L.) cultivars (Black Seeded Simpson, Parris Island, Rex RZ, and Tacitus) in tubs and in a nutrient film technique (NFT) system were studied. Hyperspectral images (HSI) were captured at plant harvest. Models developed from the HSI data were used to estimate nutrient levels of leaf tissues by employing principal component analysis (PCA), partial least squares regression (PLSR), multivariate regression, and variable importance projection (VIP) methods. The optimal wavebands were found in six regions, including 390.57–438.02, 497–550, 551–600, 681.34–774, 802–821, and 822–838 nm for tub-grown lettuces and four regions, namely 390.57–438.02, 497–550, 551–600, and 681.34–774 nm for NFT-system-grown lettuces. These fitted models’ levels showed high accuracy $(R^2=0.85-0.99)$ in estimating the growth dynamics of the studied lettuce cultivars in terms of nutrient content. HSI data of the lettuce leaves and applied N solutions demonstrated a direct positive correlation with an accuracy of 0.82–0.99 for blue and green regions in 400–575 nm wavebands. The results proved that, in most of the tested multivariate regression models, HSI data of freshly cut leaves correlated well with laboratory-measured data.

In North Dakota, agriculture contributes a large sector of the state’s economy, but vegetable production is limited due to the state’s climate condition. Inadequate soil moisture and low soil temperature are the two major factors prohibiting quality produce and high-yield vegetable production. In this study, a soil-water potential, sensor-based drip irrigation system was developed, designed, and installed to evaluate its application on tomato and watermelon productions in a two-year field experiment in 2019 and 2020. The experimental treatments were drip irrigation and no irrigation under three mulches: black plastic, clear plastic, and landscape fabric mulches. Irrigation was scheduled at 8:00 am for watermelon and 9:00 a.m. for tomato, with the ability for each irrigation event to be bypassed based on the soil moisture conditions. Due to rainfall differences in the two years, irrigation was barely needed in 2019, but in 2020, drip irrigation was applied frequently. On average, for the two-years’ field experiment, the highest yield for tomatoes was obtained from drip irrigation under black plastic drip irrigation treatment with 40.24 Mg ha⁻¹ in 2020, whereas the highest yield for watermelon was from drip irrigation under clear plastic mulch with 165.55 Mg ha⁻¹ in 2020. The effect of mulch, irrigation, and combined practices were analyzed based on the average fruit weight and diameter, electrical conductivity (EC), pH, and sugar content of the samples. The results showed that for watermelon, the average weight and diameter were significantly heavier and higher with irrigation treatments, but the EC and the pH values were significantly higher with mulch treatments. For tomatoes, the average weight, diameter, pH, and sugar content were all significantly higher with mulch treatment, but the EC was higher with irrigation treatment.