State-of-the-art computer vision techniques for automated sugarcane lodging classification

Modi, Rajesh U.; Chandel, Abhilash K.; Chandel, Narendra Singh; Dubey, Kumkum; Subeesh, A.; Singh, Akhilesh Kumar; Jat, Dilip; Mrunalini, Kancheti

doi:10.1016/j.fcr.2022.108797

Cited by 11 publications

(9 citation statements)

References 35 publications

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“…In terms of digitalization in sugarcane production and automatic intervention, several studies have been conducted (Figure 5), mostly related to sensing and predictive approaches, for example, biomass [67][68][69], gaps [1,3,53,[70][71][72][73][74], lodging identification and classification [75], yield estimation [76][77][78][79][80][81][82], nitrogen application [69,83], sugarcane disease detection [84,85], weed control [52,86], improved cropland use [87], harvesting planning [88,89], and prediction of seed replenishment positions [90]. [68], improved cropland use [87], image classifier [52], gaps [73], lodging identification [75] a yield estimation [79].…”

Section: Digital Solutions In Sugarcane Mechanizationmentioning

confidence: 99%

“…[79] applied machine learning alg rithms within a dataset composed of spectral bands and vegetation indices from Sentin 2 satellite images. They found that applying a non-linear model, random forest regressi [68], improved cropland use [87], image classifier [52], gaps [73], lodging identification [75] and yield estimation [79].…”

Section: Digital Solutions In Sugarcane Mechanizationmentioning

confidence: 99%

“…Example of digital solutions in sugarcane crop production. Biomass and nitrogen estimation[68], improved cropland use[87], image classifier[52], gaps[73], lodging identification[75] and yield estimation[79].…”

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confidence: 99%

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Challenges of Digital Solutions in Sugarcane Crop Production: A Review

Molin,

Wei,

da Silva

2024

AgriEngineering

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Over the years, agricultural management practices are being improved as they integrate Information and Communication Technologies (ICT) and Precision Agriculture tools. Regarding sugarcane crop production, this integration aims to reduce production cost, enhance input applications, and allow communication among different hardware and datasets, improving system sustainability. Sugarcane mechanization has some particularities that mandate the development of custom solutions based on digital tools, which are being applied globally in different crops. Digital mechanization can be conceived as the application of digital tools on mechanical operation. This review paper addresses different digital solutions that have contributed towards the mechanization of sugarcane crop production. The process of digitalization and transformation in agriculture and its related operations to sugarcane are presented, highlighting important ICT applications such as real-time mechanical operations monitoring and integration among operations, demonstrating their contributions and limitations regarding management efficiency. In addition, this article presents the major challenges to overcome and possible guidance on research to address these issues, i.e., poor communication technologies available, need for more focus on field and crop data, and lack of data interoperability among mechanized systems.

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Section: Digital Solutions In Sugarcane Mechanizationmentioning

confidence: 99%

Section: Digital Solutions In Sugarcane Mechanizationmentioning

confidence: 99%

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Challenges of Digital Solutions in Sugarcane Crop Production: A Review

Molin,

Wei,

da Silva

2024

AgriEngineering

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“…Cultivation of high value crops requires skilled and healthy labor for timely completion of multiple operations like bed preparation, planting, weeding, spraying, trellising, and harvesting (Modi et al, 2023; Zhang et al, 2020). Chemical spraying for controlling insects, pests, and weeds are repetitive operation carried out inside the polyhouse that requires expensive use of manual labor and bears high input energy.…”

Section: Introductionmentioning

confidence: 99%

Development of an automated mobile robotic sprayer to prevent workers' exposure of agro‐chemicals inside polyhouse

Jat

Dubey

Potdar

et al. 2023

Journal of Field Robotics

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Automated spraying practices are inevitable for modern polyhouse management to attain a broader objective of minimizing human exposure to agrochemicals. In the present study, an automated mobile robotic sprayer (AMRS) was developed to combat the increased human intervention and safeguard agricultural workers from potential health hazards. The system mainly comprises embedded sensors (ultrasonic, proximity, XBee) and controllers (Arduino, PLC). The controller drives the system on a piping track between the rows as well as on the head space achieving end-to-end automation for spraying operations. The system performance was evaluated on the tomato crop with respect to the physiological traits, yield and economics. Additionally, the study leveraged response surface methodology to optimize forward speed, spray distance, and working pressure of AMRS on the responses, droplet density, coverage, volume mean diameter (VMD) and application rate. Optimization of forward speed (0.79 km/h), spray distance (250 mm) and working pressure (0.40 MPa) resulted in 90.7 droplets/cm 2 droplet density, 47.1% coverage, 170.2 μm VMD and 86.0 mL/m 2 of application rate. Ergonomic aspects of AMRS were assessed by the parameters, human exposure, discomfort and postural assessment with respect to knapsack sprayer. The working heart rate of 103 beats/ min, work pulse of 12 beats/min, oxygen consumption rate of 916 mL/min and energy expenditure rate of 18.7 kJ/min recorded during the ergonomic evaluation of the AMRS were 25%, 75%, 42%, and 41% lower compared to manual spraying, respectively. Moreover, three-six times higher work pulse, cardiac cost, body part discomfort score and overall discomfort rating were observed which indicated more drudgery involved in manual spraying. State-of-the-art system developed for polyhouses would minimize the drudgery and health hazards, significantly. The system's resilience and effectiveness pave the way for its wider deployment where the use of agrochemicals are prevalent, particularly in small-size polyhouses.

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“…Traditional methods for detecting wheat lodging are manual that rely on visual and subjective rating of the plants’ stature, which is highly laborious phenotyping and a time consuming activity ( Ali et al., 2023 ; Modi et al., 2023 ; Zaji et al., 2023 ). These methods are more prone to error depending on a number of factors, including the expertise of the person rating the lodging and, the time of rating after the lodging, to some extent, affected by extreme environmental conditions during the assessment, affecting the accuracy and reliability of the results ( Ali et al., 2019a ).…”

Section: Introductionmentioning

confidence: 99%

LodgeNet: an automated framework for precise detection and classification of wheat lodging severity levels in precision farming

Ali,

Mohammed,

Bais

et al. 2023

Front. Plant Sci.

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Wheat lodging is a serious problem affecting grain yield, plant health, and grain quality. Addressing the lodging issue in wheat is a desirable task in breeding programs. Precise detection of lodging levels during wheat screening can aid in selecting lines with resistance to lodging. Traditional approaches to phenotype lodging rely on manual data collection from field plots, which are slow and laborious, and can introduce errors and bias. This paper presents a framework called ‘LodgeNet,’ that facilitates wheat lodging detection. Using Unmanned Aerial Vehicles (UAVs) and Deep Learning (DL), LodgeNet improves traditional methods of detecting lodging with more precision and efficiency. Using a dataset of 2000 multi-spectral images of wheat plots, we have developed a novel image registration technique that aligns the different bands of multi-spectral images. This approach allows the creation of comprehensive RGB images, enhancing the detection and classification of wheat lodging. We have employed advanced image enhancement techniques to improve image quality, highlighting the important features of wheat lodging detection. We combined three color enhancement transformations into two presets for image refinement. The first preset, ‘Haze & Gamma Adjustment,’ minimize atmospheric haze and adjusts the gamma, while the second, ‘Stretching Contrast Limits,’ extends the contrast of the RGB image by calculating and applying the upper and lower limits of each band. LodgeNet, which relies on the state-of-the-art YOLOv8 deep learning algorithm, could detect and classify wheat lodging severity levels ranging from no lodging (Class 1) to severe lodging (Class 9). The results show the mean Average Precision (mAP) of 0.952% @0.5 and 0.641% @0.50-0.95 in classifying wheat lodging severity levels. LodgeNet promises an efficient and automated high-throughput solution for real-time crop monitoring of wheat lodging severity levels in the field.

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State-of-the-art computer vision techniques for automated sugarcane lodging classification

Cited by 11 publications

References 35 publications

Challenges of Digital Solutions in Sugarcane Crop Production: A Review

Challenges of Digital Solutions in Sugarcane Crop Production: A Review

Development of an automated mobile robotic sprayer to prevent workers' exposure of agro‐chemicals inside polyhouse

LodgeNet: an automated framework for precise detection and classification of wheat lodging severity levels in precision farming

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