Crop Health Management Research Articles

Enhancing Rice Crop Health through Computational Intelligence-Based Disease Detection

Rice is one of the most important staple crops worldwide, and rice plant diseases are a significant threat to global food security. Early detection and accurate classification of these diseases are crucial for effective disease management and prevention of crop losses. In this paper, we propose a novel computational intelligence-based technique for rice disease detection and classification. Our proposed method is composed of a residual network-based feature extractor followed by a Light Gradient Boosting Machine (LGBM) classifier. We use a publicly available rice leaf dataset to evaluate the performance of our proposed method. The results demonstrate that our proposed method achieves high accuracy, sensitivity, and specificity in identifying diseased rice plants, outperforming existing state-of-the-art methods. We also compare our proposed method against other methods using different performance metrics, showing its superior performance. The proposed method provides a promising approach to enhance rice crop health management and can be adapted and customized for other crops and agricultural settings. The proposed computational intelligence-based technique for rice disease detection and classification has significant implications for improving crop productivity and ensuring food security.

Influence of integrated nutrient management on the physiology of Chinnar ‘a local land race’ of rice (Oryza sativa L.)

Rice is the staple cereal crop of India as well as Tamil Nadu. Consumers tend to prefer local cultivars and landraces of rice which have region-specific importance. Chinnar is such a local landrace of rice that is preferred by the consumers of Madurai because of its nutritional quality. Like other cultivars, Chinnar responds to added nutrients for enhancement of growth. Integrated nutrient management (INM) is one of the suitable options in crop and soil health management where inorganic and organic sources of nutrients are used. This study was carried out during 2022 at Agricultural College and Research Institute, Madurai to evaluate the influence of INM on the growth of the Chinnar landrace of rice. Recommended dose of fertilizer (RDF) with respect to inorganic nutrient sources viz., fertilizers (Urea, SSP and MOP) @ 50% and the remaining with organic manures through poultry manure, goat manure, farm yard manure, enriched farm yard manure and vermicompost were applied as per nitrogen equivalent basis. Results revealed higher leaf area index, chlorophyll content of Chinnar at various stages of growth phase. Thus, from the study, it is concluded that INM can be preferred to obtain higher growth attributes of Chinnar rice in Madurai conditions of Tamil Nadu.

Agrochemical Contamination and Ageing Effects on Greenhouse Plastic Film for Recycling

Plastic films used for crop protection have reached notable consumption all over Europe, as well as in the rest of the world. This phenomenon however poses a serious environmental problem connected with the impact on the sustainability of agricultural production and relevant plastic footprint. Mechanical recycling of agricultural plastics is a common technique, but limited by many factors, as the loss of mechanical properties of plastic film. This phenomenon, due to its ageing after being exposed to natural weather conditions, plays a crucial role, especially when aggravated by contamination with agrochemicals ordinarily used for crop health and pest management. This article reports the result of some laboratory tests on agricultural plastic film, artificially aged for different periods and contaminated with two different agrochemicals (anti-aphid or fungicide). These results show that the impact of agrochemicals on plastic film is considerable, since it worsens the ageing process of the plastic film, conducting a more rapid reduction in its mechanical properties—mostly, a faster reduction in the elongation at break below 50% of the corresponding value of the virgin plastic film. This phenomenon, other than reducing the working life of the plastic film, gives it a low potential for being transformed into a closed-loop recycled material when entering the recycling stage. The increase in the value of the detected Carbonyl Index (CI) confirms the tendency of the material to degrade rapidly when in contact with agrochemicals, hence indicating that it may be impossible to mechanically recycle it.

Effectiveness of Social Media Based Agro Advisory Services in Andhra Pradesh– An Analysis

Extension organizations are using social media and innovative content-generating approaches to reach farmers and assist them with their information needs. But to what extent are farmers using the information received by them and converting the information into practical application is the question to be answered. Hence a study to fi nd out the eff ectiveness of the social media-based agro-advisory services was conducted in the IT revolutionized the state of Andhra Pradesh with a sample of 126 farmers from three districts through random sampling method. The eff ectiveness of social media was analyzed by developing a composite index with four sub-indicators having a Cronbach alpha of 0.8 which stated that the majority of the farmers 39.7 per cent had medium eff ectiveness of the messages they received. The study found out that most of the farmers receive information daily and the information needs are mostly around crop health management. The understandability of the information was good which helped in increased yields followed by the rate of adoption and increase in income of farmers. These results help to strategize the strengths of messages shared through social media and help as guidelines in new content development by extension organizations.

FARMER BUDDY SYSTEM USING MACHINE LEARNING

The major focus for soil management in agriculture for enhancing crop productivity is on the maintenance and improvement of dynamic soil parameters. The population stresses, terrestrial limitations and the decline of traditional soil management methods have directed to a deterioration in the fertility of the soil in developing countries like India. Crop health is a major element in the highly productive system of modern agriculture. A substantial increase in crop production can be attained by adopting the suitable crop health management strategy. The increased productivity could be achieved through effective soil resource management and corrective measures to apply micronutrients. Timely detection and controlling of problems connected with crop yield pointers enables the decision makers (agricultural experts) and farmers to decide on appropriate soil resource management and crop environment management. Keeping in account in this “Farmer Buddy System” a ML based website , the crop prediction and classification of soil problems are effective

ISOLATION, CHARACTERIZATION AND IDENTIFICATION OF MULTIFACETED HALOTOLERANT BACILLUS LICHENIFORMIS AND BACILLUS WUDALIANCHIENSIS FROM RHIZOSPHERIC SOILS OF BANGALORE

Plant growth promotion through versatile microorganisms inhabiting the rhizospheric region of the soil has emerged as a sustainable approach in improving the crop productivity and crop health management. The present study reports the isolation and characterization of halotolerant as well as drought tolerant isolates that possess exceptional plant growth promotional attributes. Out of 32 isolates obtained, 13 isolates exhibited excellent plant growth promotional attributes such as phosphate solubilization, phytohormone production, siderophore production etc. Among 13 isolates tested for stress tolerance studies, two isolates Mulberry -1 and Ragi-7 were able to tolerate both drought and saline stresses and were further identified as Bacillus licheniformis and Bacillus wudalianchiensis based on biochemical and molecular techniques. Antibiosis activity of both the isolates tested against two important plant pathogens Fusarium oxysporum and Phomopsis vexans was found to be positive. Bacillus licheniformis and Bacillus wudalianchiensis were further tested for their efficacy to solubilize five different inorganic phosphate sources. The amount of phosphate liberated was found to be highest (3562 mgml-1 and 1894 mgml-1) with diammonium phosphate followed by single super phosphate and tricalcium phosphate with respect to Bacillus licheniformis and Bacillus wudalianchiensis. Zea mays L. seeds treated with the culture filtrate of both the isolates were cultivated on Murashige-Skoog medium under In-vitro conditions. Bacillus wudalianchiensis was found to be better compared to Bacillus licheniformis with respect to plant growth promotional aspects as they were able to increase the root and shoot length of Zea mays L. under In-vitro conditions. Application of the multifaceted capabilities of unexplored halotolerant strain of Bacillus wudalianchiensis could open up a new avenue in plant biotechnology.

Septoria Nodorum Blotch of Wheat: Disease Management and Resistance Breeding in the Face of Shifting Disease Dynamics and a Changing Environment.

The fungus Parastagonospora nodorum is a narrow host range necrotrophic fungal pathogen that causes Septoria nodorum blotch (SNB) of cereals, most notably wheat (Triticum aestivum). Although commonly observed on wheat seedlings, P. nodorum infection has the greatest effect on the adult crop. It results in leaf blotch, which limits photosynthesis and thus crop growth and yield. It can also affect the wheat ear, resulting in glume blotch, which directly affects grain quality. Reports of P. nodorum fungicide resistance, the increasing use of reduced tillage agronomic practices, and high evolutionary potential of the pathogen, combined with changes in climate and agricultural environments, mean that genetic resistance to SNB remains a high priority in many regions of wheat cultivation. In this review, we summarize current information on P. nodorum population structure and its implication for improved SNB management. We then review recent advances in the genetics of host resistance to P. nodorum and the necrotrophic effectors it secretes during infection, integrating the genomic positions of these genetic loci by using the recently released wheat reference genome assembly. Finally, we discuss the genetic and genomic tools now available for SNB resistance breeding and consider future opportunities and challenges in crop health management by using the wheat-P. nodorum interaction as a model.

Rendimiento, calidad y comportamiento poscosecha de frutos de aguacate ‘Hass’ de huertos con diferente fertilización

En frutales, la productividad difiere entre huertos debido a la condición particular del estatus nutrimental de los árboles, fertilidad del suelo, portainjerto, sanidad del cultivo y manejo agronómico. El objetivo de esta investigación fue comparar el rendimiento, calidad y comportamiento poscosecha de frutos de aguacate ‘Hass’ en dos huertos con diferente manejo de la fertilización. El primero se ubicó en Valle de Bravo y el segundo en Villa de Allende, Estado de México. La evaluación se realizó en 2017. En cada huerto se midió el rendimiento, calidad, comportamiento poscosecha y extracción de nutrimentos por los frutos. Además, se diagnosticó el estatus nutrimental del árbol y la fertilidad del suelo. Los frutos del huerto de Valle de Bravo superaron a los de Villa de Allende en rendimiento, peso, longitud, diámetro y concentración y densidad de aceite, sin embargo, presentaron menor firmeza y pérdida de peso. Los aguacates de Valle de Bravo tuvieron mayor concentración de K, S, Zn y B, los de Villa de Allende concentraron más Ca, Fe, Cu y Mn. En Valle de Bravo los árboles mostraron deficiencias de Ca, S, Cu, Mn, Zn y B, mientras que, en Villa de Allende, los elementos deficientes fueron K, S, Zn y B. En conclusión, el mayor rendimiento de fruto obtenido en el huerto de Valle de Bravo se relacionó con el mejor estatus nutrimental de los árboles y mayor fertilidad del suelo.

Application of artificial intelligence techniques in irrigation and crop health management for crop yield enhancement

Agriculture is the backbone of all developing countries. The livelihood of a huge majority of the Indian population is completely dependent on agriculture. However lack of basic requirement lead to poor development in this sector. The major factor required for the development of this sector is to improve water supply level. The lack of proper monsoon has resulted in poor agricultural yield. Based on the natural calamities we can predict the requirement of water for particular crop at different stages of growth and also enhance the yield. In this paper, we have chosen Thottiyam taluk as example located in the state of Tamilnadu, India. For that location we have collected the basic attributes required for good irrigation management for the months of December and January. Artificial Intelligence techniques are applied to predict the water level requirement and their performances can be compared.

Predicting soil electrical conductivity using multi-layer perceptron integrated with grey wolf optimizer

In irrigation systems, salinity is a critical problem as it has undesirable impacts on crop health, agricultural throughput and farming management. Considering these, it is imperative to regularly monitor and develop measures to predict salinity of the soil to negate the salinization effects on agriculture. This paper constructs and evaluates the performance of the hybrid machine learning model of multilayer perceptron (MLP)-Grey Wolf Optimizer (MLP-GWO) for electrical conductivity (EC). MLP-GWO model is trained with soil sample data (i.e., parameters for organic matter, OM and soil constituents Ca+2, Mg+2, K+, Na+, Cl−, SO4−2, HCO3−) from Khuzestan province in Iran. Seven modelling scenarios representing different combinations of salinity parameters are investigated to establish a hybrid MLP-GWO model that aims to reduce the error rate of the resulting forecasts of EC. To ascertain conclusive results, the MLP-GWO model is cross-validated with its classical counterpart without the add-in (i.e., GWO) optimizer, and the model error metrics are evaluated by coefficients of determination (R2), root mean squared error (RMSE) and relative root mean square error (RRMSE) in independent test data. For all tested predictive models, the performance of the MLP-GWO hybrid model is superior to a classical model, evidenced by larger R2 (~0.552–0.711 relative to ~0.430–0.711) and a lower RMSE and RRSE (~1.293–3.537 vs. 1.616–4.421 and ~3.736–9.899 vs. 4.613–12.133). The proposed GWO as an optimizer leads to a plausible improvement in an MLP model due to the most optimal weights attained in the neuronal layer that facilitates a robust feature extraction process to predict EC. As conclusion, the obtained results proved the effectiveness of the hybrid MLP-GWI model for predicting soil properties, which has potential implications in precision agriculture where salinity needs to be modeled for crop management practices.

Evaluation of bioagents for their compatibility in the development of consortium for enhanced efficacy

The concept of development of microbial consortia for bio-control relies on the fact that bioagents under natural habitats live in communities with some benefits for plants. Application of bioagents in a consortium may improve efficacy, reliability and consistency of the bioagents even under diverse soil and environmental conditions. Diversity in biocontrol mechanisms offered by each bioagent in consortium may help in enhancing disease suppressiveness and may also strengthen the capabilities of the partners in an additive or synergistic manner. Till now no guideline has been published for the evaluation of bioagents to test their compatibility before developing bioagent consortium. In the present studies compatibility among biocontrol potential Trichoderma-Pseudomonas and Trichoderma-Trichoderma isolates was studied by dual culture, mixed formulations and using cell free cultures. In dual culture all the combinations (14 no.) were found compatible with each other as no isolate inhibited the growth of one-another i.e. absence of  inhibition zone. All the mixed formulations of potential Trichoderma-Pseudomonas isolates (8 no.) were found compatible with each other as they were growing simultaneously on PDA without antagonizing the growth of other or formation of inhibition zone in their combinations. The cell free cultures of each Trichoderma and Pseudomonas isolates tested with each other using Food Poison Technique showed synergistic effects on their fresh mycelial weight among some combinations while majority showed no significant differences with their checks. Further all the combinations (14 no.) were tested for their effects on seed germination and vigour index of chickpea in glasshouse. All the combinations showed significantly better seed germination while some combinations viz. Th14+Psf173, TCMS36+Psf173,Th17+Th19,Th17+Psf2,Th17+TCMS36 and Th14+Psf2 showed better plant vigour index (43.5 to 44.9% ) as compared to their checks (28.8 to 41.5%).These guidelines could be used before developing bioagent consortium and evaluation in field for crop health management.

Unraveling the Complexity of Coffee Leaf Rust Behavior and Development in Different Coffea arabica Agroecosystems.

Crop health management systems can be designed according to practices that help to reduce crop losses by restricting pathogen development and promoting host plant growth. A good understanding of pathogen and host dynamics, which are interdependent, is therefore needed. In this article, we used a holistic approach to explain the behavior of coffee leaf rust (CLR), a major coffee disease. We monitored coffee plant and CLR dynamics simultaneously in plots under different disease management and agroforestry systems. Diseased leaves were also collected to characterize inoculum stock and rust life stages (latent rust area, area with uredospores, necrosis due to rust) through picture analysis. We used structural equation modeling to obtain an overview of CLR pathosystem functioning on a plant scale. This overview integrates processes such as disease dilution by host leaf renewal, direct and indirect effects of fruit load on CLR development, antagonistic effects of shading depending on rust life stages, the tonic effect of copper-based fungicides on leaf retention, and effects on rust life stages depending on fungicide types. From our results, we also deduced that the inoculum stock could be calculated in unsprayed plots from the rust area with uredospores, with uredospores at 58 × 103 cm-2, on average.

The global burden of pathogens and pests on major food crops.

Crop pathogens and pests reduce the yield and quality of agricultural production. They cause substantial economic losses and reduce food security at household, national and global levels. Quantitative, standardized information on crop losses is difficult to compile and compare across crops, agroecosystems and regions. Here, we report on an expert-based assessment of crop health, and provide numerical estimates of yield losses on an individual pathogen and pest basis for five major crops globally and in food security hotspots. Our results document losses associated with 137 pathogens and pests associated with wheat, rice, maize, potato and soybean worldwide. Our yield loss (range) estimates at a global level and per hotspot for wheat (21.5% (10.1-28.1%)), rice (30.0% (24.6-40.9%)), maize (22.5% (19.5-41.1%)), potato (17.2% (8.1-21.0%)) and soybean (21.4% (11.0-32.4%)) suggest that the highest losses are associated with food-deficit regions with fast-growing populations, and frequently with emerging or re-emerging pests and diseases. Our assessment highlights differences in impacts among crop pathogens and pests and among food security hotspots. This analysis contributes critical information to prioritize crop health management to improve the sustainability of agroecosystems in delivering services to societies.

Multiple-Disease System in Coffee: From Crop Loss Assessment to Sustainable Management.

Assessment of crop loss due to multiple diseases and pests (D&P) is a necessary step in designing sustainable crop management systems. Understanding the drivers of D&P development and yield loss helps identify leverage points for crop health management. Crop loss assessment is also necessary for the quantification of D&P regulation service to identify promising systems where ecosystem service provision is optimized. In perennial crops, assessment of crop losses due to D&P is difficult, as injuries can affect yield over years. In coffee, one of the first perennials in which crop loss trials were implemented, crop losses concurrent with injuries were found to be approximately 50% lower than lagged losses that originated following the death of productive branches due to D&P. Crop losses can be assessed by field trials and surveys, where yield reduction factors such as the number of productive branches that have died are quantified, and by modeling, where damage mechanisms for each injury are considered over several years.

Non-tuned data intelligent model for soil temperature estimation: A new approach

In knowledge-based decision-support systems, soil temperature (ST) estimation can be considered as the core modeling task used for investigating the dynamics of solar energy exchange between the land surface and the sub-surface soil layers. In addition, the impacts of meteorological processes on energy uptake into the underlying soils and the design of more resilient and sustainable agricultural systems for improved crop health and resource management. In this paper, monthly ST estimation at various soil depths (i.e., 5, 50 and 100 cm) is performed by applying data-intelligent machine learning models: extreme learning machine (ELM), artificial neural network (ANN) and M5 Model Tree (M5 Tree). The predictive models are trained using meteorological information from two stations (i.e., Mersin and Adana, Turkey). The models are constructed using monthly input variables, including air temperature (T-air), windspeed (W), relative humidity (RH), solar radiation (SR), while the objective variable is the soil temperature measurement at 5, 50 and 100 cm depths for the period 1986–2010. Multi-objective performance criteria are applied to diagnose the predictive accuracy of the data-intelligent models, entailing the correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), Willmott's Index (WI), Nash Sutcliffe's coefficient (Nash) and the Legates & McCabe's Index (LMI). Based on the tested dataset, the ELM model is yielded the most accurate performance for Mersin station compared to the lower performance of ANN and M5 Tree models. For this case, the most accurate performance is attained for the ST estimation at a depth of 50 cm, with the highest value of R = 0.992, WI = 0.999, Nash = 0.981, LMI = 0.879 and the lowest value of the relative RMSE and MAE values (i.e., 4.7 and 4.0%, respectively), attained using T-air, W, RH and periodicity as the predictor variables. The performance results for the Adana station behave differently, where ELM is seen to exceed the ANN and M5 Tree models for ST estimation at the depths of 5 and 100 cm. On the other hand, the ANN model performs marginally better than the ELM and the M5 Tree model at a depth of 50 cm with a different set of input combinations. The assessment of the estimation skills reveals the efficacy of the ELM over the counterpart benchmark models. In accordance with the present results, it is concluded that ELM model can be applied as an ideal decision-support tool for soil temperature estimation at multiple depths, whilst ensuring that an appropriate combination of meteorological inputs is applied to yield an optimal model.

Novel Trichoderma strains isolated from tree barks as potential biocontrol agents and biofertilizers for direct seeded rice

This study is the first time report of utilization of Trichoderma spp. isolated from different tree bark from Odisha state of India for rice crop health management and higher productivity. Six isolates of Trichoderma spp. were identified based on the morphological characteristics and species determination was performed by molecular assays. One of the isolated strains determined as Trichoderma erinaceum outperformed others. Trichoderma erinaceum controlled three soil borne plant pathogens i.e. Rhizoctonia solani, Sclerotium rolfsii and Sclerotium oryzae effectively under controlled condition and R. solani and Helminthosporium oryzae under filed condition. Seed treatments with the formulated isolates improved the germination rate of rice and enhanced vigour. These parameters along with higher chlorophyll content could be related to higher yield observed in two rice varieties; Karuna and Sahabhagidhan. Among the six isolates tested, Trichoderma erinaceum treatment recorded highest yield. Significantly higher expression of some stress related enzymes was observed in Trichoderma treated plants which helped in better crop growth both under biotic and abiotic stresses. These isolates helped both the varieties to accumulate more nutrients. This study proves that Trichoderma erinaceum obtained from tree bark may be incorporated in integrated rice crop management both as biocontrol agent and biofertilizer.

Reverse modelling to estimate yield losses caused by crop diseases

Yield loss analysis is critical to inform tactical and strategic decisions in crop health management, and requires quantification of three elements: the levels of injury caused by disease or pest, the actual (injured) yield, and the attainable (uninjured) yield. Reverse modelling allows reconstruction of an object or a process from limited information combined with a mathematical model. This approach is applied to estimate yield losses caused by diseases in winter wheat using a process‐based simulation model (WHEATPEST), in combination with field data generated by a network of experiments across France, where multiple disease injuries and actual yields, but not attainable yields, were measured. The analysis covers 70 [year × region × variety × crop management] combinations encompassing five years (2004–2008), four French regions, two winter wheat varieties (one high‐yielding and one hardy variety), and two levels of crop management corresponding to two levels of chemical intensification. The analysis involved three main successive modelling steps where actual yield, attainable yield and yield losses associated with individual diseases were simulated. Overall, simulated yield losses to combined diseases ranged from 0 to 4.2 t ha−1, and averaged 0.80 t ha−1. Septoria tritici blotch caused the highest mean yield loss of 0.66 t ha−1. The results highlight the contribution of varietal improvement to agricultural sustainability and performances. Reverse modelling can be applied to other crops and diseases or pests, in order to estimate individual and combined disease yield losses.

Interaction of Engineered Nanoparticles with the Agri-environment.

Nanoparticles with their unique surface properties can modulate the physiological, biochemical, and physicochemical pathways, such as photosynthesis, respiration, nitrogen metabolism, and solute transport. In this context, researchers have developed a wide range of engineered nanomaterials (ENMs) for the improvement of growth and productivity by modulating the metabolic pathways in plants. This class of tailor-made materials can potentially lead to the development of a new group of agrochemical nanofertilizers. However, there are reports that engineered nanomaterials could impart phytotoxicity to edible and medicinal plants. On the contrary, there is a series of ENMs that might be detrimental when applied directly and/or indirectly to the plants. These particles can sometimes readily aggregate and dissolute in the immediate vicinity; the free ions released from the nanomatrix can cause serious tissue injury and membrane dysfunction to the plant cell through oxidative stress. On that note, thorough studies on uptake, translocation, internalization, and nutritional quality assessment must be carried out to understand ENM-plant interactions. This review critically discusses the possible beneficial or adverse aftereffect of nanofertilizers in the immediate environment to interrelate the impacts of ENMs on the crop health and food security management.

Copula-statistical precipitation forecasting model in Australia’s agro-ecological zones

Vine copulas are employed to explore the influence of multi-synoptic-scale climate drivers – El Niño Southern Oscillation (ENSO) and Inter-decadal Pacific Oscillation (IPO) Tripole Index (TPI) – on spring precipitation forecasting at Agro-ecological Zones (AEZs) of the Australia’s wheat belt. To forecast spring precipitation, significant seasonal lagged correlation of ENSO and TPI with precipitation anomalies in AEZs using data from Australian Water Availability Project (1900–2013) was established. Most of the AEZs exhibit statistically significant dependence of precipitation and climate indices, except for the western AEZs. Bivariate and trivariate copula models were applied to capture single (ENSO) and dual predictor (ENSO & TPI) influence, respectively, on seasonal forecasting. To perform a comprehensive evaluation of the developed copula-statistical models, a total of ten one- and two-parameter bivariate copulas ranging from elliptical to Archimedean families were examined. Stronger upper tail dependence is visible in the bivariate model, suggesting that the influence of ENSO on precipitation forecasting during a La Niña event is more evident than during an El Niño event. In general, while the inclusion of TPI as a synoptic-scale driver into the models leads to a notable reduction in the mean simulated precipitation, it depicts a general improvement in the median values. The forecasting results showed that the trivariate forecasting model can yield a better accuracy than the bivariate model for the east and southeast AEZs. The trivariate forecasting model was found to improve the forecasting during the La Niña and negative TPI. This study ascertains the success of copula-statistical models for investigating the joint behaviour of seasonal precipitation modelled with multiple climate indices. The forecasting information and respective models have significant implications for water resources and crop health management including better ways to adapt and implement viable agricultural solutions in the face of climatic challenges in major agricultural hubs, such as Australia’s wheat belt.

Effect of observation scale on remote sensing based estimates of evapotranspiration in a semi-arid row cropped orchard environment

Understanding in detail the spatial distribution of evapotranspiration (ET) in row cropped fruit production areas with diverse water requirements is vital for monitoring water use and efficient irrigation scheduling. Spatially distributed ET for these environments can be estimated using remote sensing (RS). However, the computation of RS based ET under such conditions is complicated because of the complex parameterizations that are required to derive ET for the mixed pixels comprising of bare soil and well-watered plants typical of row cropped areas. Also, the parameterization of these processes is not scale invariant, owing to change in the percentage of vegetation cover in the mixed pixels across remote sensing observation scales. In this study, our main objectives were (1) to isolate and evaluate the effect of varying spatial scales (comparable to canopy sizes and larger) of the remote sensing data on ET estimates; and (2) provide an operational method for estimating remote sensing based ET for row cropped conditions. ET was computed using an empirical technique (S-SEBI: Simplified-Surface Energy Balance Index Algorithm) for almond and pistachio orchards from remote sensing imagery collected at a scale comparable to the canopy sizes of the trees (5.8 and 7.2 m) and a scale that was much larger than the canopy size (120 m) using the MASTER and Landsat sensors, respectively. In order to account for the effect of mixed pixels, a Normalized Difference Vegetation Index based correction factor was applied to the derived ET values and the results averaged for different fields were validated with Penman–Monteith based ET estimates. It was found that the corrected mean ET estimates at 120 m were in agreement with the Penman–Monteith based ET estimates (RMSEaverage = 0.12 mm/h), whereas they were underestimated at the finer resolutions. Our results indicated that a remote sensing pixel resolution comparable to the row spacing and smaller and comparable to the canopy size overestimated the land surface temperature and consequently, underestimated ET when using operational models that do not account for vegetation and soil temperature separately. The results of the application of the NDVI correction factor indicates that good spatial estimates of crop ET can be made for crops growing in orchards using simple ET models that require minimal data and freely available Landsat imagery. These findings are very encouraging for the regular monitoring of crop health and effective management of irrigation water in highly water stressed agricultural environments.