Symptoms of nutrient deficiencies in rice plants often appear on the leaves. The leaf color and shape, therefore, can be used to diagnose nutrient deficiencies in rice. Image classification is an efficient and fast approach for this diagnosis task. Deep convolutional neural networks (DCNNs) have been proven to be effective in image classification, but their use to identify nutrient deficiencies in rice has received little attention. In the present study, we explore the accuracy of different DCNNs for diagnosis of nutrient deficiencies in rice. A total of 1818 photographs of plant leaves were obtained via hydroponic experiments to cover full nutrition and 10 classes of nutrient deficiencies. The photographs were divided into training, validation, and test sets in a 3 : 1 : 1 ratio. Fine-tuning was performed to evaluate four state-of-the-art DCNNs: Inception-v3, ResNet with 50 layers, NasNet-Large, and DenseNet with 121 layers. All the DCNNs obtained validation and test accuracies of over 90%, with DenseNet121 performing best (validation accuracy = 98.62 ± 0.57%; test accuracy = 97.44 ± 0.57%). The performance of the DCNNs was validated by comparison to color feature with support vector machine and histogram of oriented gradient with support vector machine. This study demonstrates that DCNNs provide an effective approach to diagnose nutrient deficiencies in rice.

Wheat is the third most important cereal crop in Nepal after rice and maize both in area and production, but its productivity of 2.3 tonne ha^-1 is very less compared to other developed countries (6 tonne ha^-1 for Switzerland and China) in the world. The main cause of low wheat yield in Nepal is the improper and inadequate use of fertilizer devoid of site specific nutrient management practices. Therefore, a farmers' field experiment was conducted during November 2015 to April 2016 to rectify the best fertilizer management options at two sites of Damak and Gauradaha in Jhapa district in eastern-Terai of Nepal using Nutrient Expert®-Wheat model. The research was accomplished in Randomized Complete Block Design with 2 treatments and 20 replications, considering farmers' field as replication. Two treatments included in the experimentation were NE® (Nutrient Expert Recommendation) and FFP (Farmer's Fertilizer Practices). The statistical result revealed the highly significant difference in terms of number of effective tiller m^-2, plant height, filled grain per spike, spike length, grain, straw and biological yields and harvest index. The highest yield (4.71 tonne ha^-1) was obtained from NE field than FFP (2.99 tonne ha^-1). On an average, NE based practices produced 58 % higher yield in comparison to FFP. NE based treatment produced significantly higher biomass yield, yield attributes and cost-benefit ratio than FFP treatments. Field experiment validation confirmed that the Nutrient Expert® Wheat model could be used as the most adoptable and practical precision decision support system tool to make a more authentic fertilizer recommendation in eastern-Terai of Nepal.

Drought stress is seen as the major abiotic stress in the modern day agriculture and hinders crop germination and seedling establishment and maize suffers the problem more as a summer season crop. Priming is a physiological method to overcome such deleterious effect of water stress with the main aim of increasing the germination of seed. A lab experiment was therefore performed with maize seed priming using Mannitol @ 0%, 2%, 4%, 6% and 8% (w/v) concentrations subjected to germination under induced drought of 0 Mpa, 0.15 Mpa, 0.5 MPa, 1.0 MPa and 1.7 MPa using NaCl. The experiment was laid in completely randomized design (CRD) with three replications. Priming with mannitol reduced the Mean Germination Time (MGT); the best result obtained in seeds primed with 2% mannitol. However, the final germination count, Relative Water Content (RWC) and root and shoot length remained unaltered. Germination activities reduced with increasing moisture stress. The study indicated that priming with mannitol could improve the speed of germination in maize seeds.

Germination is a portentous yield determining factor that is a challenge in the low water potential environment due to disrupted imbibition. Hormonal seed priming can markedly increase the germination on maize even in such a stressful environment. Therefore, research was aimed to analyze the application of gibberellic acid (GA3) priming to maize seeds to minimize the deleterious effects of reduced water potential. GA3 priming was done at 0, 50, 100, 150, and 200 ppm for 12 hours and subjected to drought levels of 0, 0.15, 0.50, 1.05, and 1.75 MPa by using NaCl solution. Different germination parameters, i.e., germination percentage, mean germination time, germination index, relative water content, seedling vigor index, root length, and shoot length, were determined on the final day of the experiment. The results showed that all germination parameters were adversely influenced by low water potential. Every level of GA3 priming has hastened all parameters. GA3 priming at 100 ppm decreased the mean germination time by 35 hours under 1.75 MPa compared to non-primed seeds. GA3 priming increased the shoot length of maize seedlings with a shoot length of 2.9 cm in non-primed seedlings compared to 6.4 cm in 200 ppm GA3 priming under 1.05 MPa. GA3 priming is the best method for the early establishment of maize seedlings in low water potential conditions. As a result, it may be utilized as a low-cost and straightforward approach for establishing maize crops under saline and drought conditions.

High nighttime temperature (HNT) due to asymmetric diurnal warming threatens wheat productivity. This study evaluated the effect of HNT on wheat phenology, physiology, and yield through field and controlled environment experiments in Central Queensland, Australia. Two wheat genotypes, Faraday and AVT#6, were assessed under three sowing dates—1 May (Early), 15 June (Mid), and 1 August (Late)—within the recommended sowing window for the region. In a parallel growth chamber study, the plants were exposed to two nighttime temperature regimes, of 15 °C (normal) and 20 °C (high), with consistent daytime conditions from booting to maturity. Late sowing resulted in shortened vegetative growth and grain filling periods and increased exposure to HNT during the reproductive phase. This resulted in elevated floret sterility, lower grain weight, and up to 40% yield loss. AVT#6 exhibited greater sensitivity to HNT despite maturing earlier. Leaf gas exchange analysis revealed increased nighttime respiration (Rn) and reduced assimilation (A), resulting in higher Rn/A ratio for late-sown crops. The results from controlled environment chambers resembled trends of the field experiment, producing lower grain yield and biomass under HNT. Cumulative nighttime hours above 20 °C correlated more strongly with yield losses than daytime heat. These findings highlight the need for HNT-tolerant genotypes and optimized sowing schedules under future climate scenarios.