Using Transfer Learning for Nutrient Deficiency Prediction and Classification in Tomato Plant

Kusanur, Vrunda; Chakravarthi, Veena S.

doi:10.14569/ijacsa.2021.0121087

Cited by 9 publications

(5 citation statements)

References 13 publications

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“…Overall, Inception-V3 consistently delivered superior performance across all datasets. This can be attributed to its deeper architecture, which enable more robust feature extraction capabilities, as observed in previous studies comparing CNN architectures [8,16].…”

Section: Discussionmentioning

confidence: 56%

“…However, the lack of such nutrients harms the plant, leading to yield loss for farmers [7]. According to [8], the conventional methods that rely on visual inspection by agriculture specialists for plant nutrient deficiency identification are time-consuming and labour-intensive. The agriculture industry has increasingly adopted machine learning (ML) and Artificial Intelligence (AI) to enhance the efficiency and accuracy of plant health diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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Analysing the Performance and Interpretability of CNN-Based Architectures for Plant Nutrient Deficiency Identification

Mkhatshwa,

Kavu,

Daramola

2024

Computation

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Early detection of plant nutrient deficiency is crucial for agricultural productivity. This study investigated the performance and interpretability of Convolutional Neural Networks (CNNs) for this task. Using the rice and banana datasets, we compared three CNN architectures (CNN, VGG-16, Inception-V3). Inception-V3 achieved the highest accuracy (93% for rice and banana), but simpler models such as VGG-16 might be easier to understand. To address this trade-off, we employed Explainable AI (XAI) techniques (SHAP and Grad-CAM) to gain insights into model decision-making. This study emphasises the importance of both accuracy and interpretability in agricultural AI and demonstrates the value of XAI for building trust in these models.

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Section: Discussionmentioning

confidence: 56%

Section: Introductionmentioning

confidence: 99%

Analysing the Performance and Interpretability of CNN-Based Architectures for Plant Nutrient Deficiency Identification

Mkhatshwa,

Kavu,

Daramola

2024

Computation

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“…In this method, the main idea is the transfer of knowledge, called transfer learning [ 28 ]. DenseNet [ 14 , 17 , 29 ], AlexNet [ 14 ], VGG [ 14 , 17 , 19 ], ResNet [ 14 , 19 , 24 ], Inception-ResNet [ 15 , 17 , 30 ], Inception [ 19 , 30 ], EfficientNet [ 29 , 30 ] and MobileNet [ 29 , 30 ] are some of the learning transfer models that are commonly used in the literature and have been found by researchers to be successful in experiments related to plant health. Many different parameters, such as the depth and width of the models, the convolution layers used, the connections between the layers, etc., differentiate the architectures of the models and affect the performance of the models in factors such as success and training time.…”

Section: Methodsmentioning

confidence: 99%

“…Researchers that used pre-trained neural networks, i.e., models called Transfer Learning (DenseNet, NasNet, InceptionResnet, VGG, and GoogleNet), were able to make predictions that demonstrated an accuracy above 86%. Kusanur [ 19 ] detected magnesium and calcium deficiency in tomato plants, while Rahadiyan [ 20 ] detected potassium, calcium, magnesium and sulfur deficiency in chili pepper. Due to the variety and lack of data in the studies, processes such as resizing, shearing, rotation, scaling, mirroring and cropping in images were observed to improve the results, while processes such as changing the color scale or adding noise reduced the accuracy [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Exploiting Pre-Trained Convolutional Neural Networks for the Detection of Nutrient Deficiencies in Hydroponic Basil

Gül

Bora

2023

Sensors

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Due to the integration of artificial intelligence with sensors and devices utilized by Internet of Things technology, the interest in automation systems has increased. One of the common features of both agriculture and artificial intelligence is recommendation systems that increase yield by identifying nutrient deficiencies in plants, consuming resources correctly, reducing damage to the environment and preventing economic losses. The biggest shortcomings in these studies are the scarcity of data and the lack of diversity. This experiment aimed to identify nutrient deficiencies in basil plants cultivated in a hydroponic system. Basil plants were grown by applying a complete nutrient solution as control and non-added nitrogen (N), phosphorous (P) and potassium (K). Then, photos were taken to determine N, P and K deficiencies in basil and control plants. After a new dataset was created for the basil plant, pretrained convolutional neural network (CNN) models were used for the classification problem. DenseNet201, ResNet101V2, MobileNet and VGG16 pretrained models were used to classify N, P and K deficiencies; then, accuracy values were examined. Additionally, heat maps of images that were obtained using the Grad-CAM were analyzed in the study. The highest accuracy was achieved with the VGG16 model, and it was observed in the heat map that VGG16 focuses on the symptoms.

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“…CNN models were also frequently used in applications for nutrient detection. This was demonstrated in soybean leaf defoliation in [ 70 ], nutrient concentration in [ 72 ], nutrient deficiencies in [ 75 ], net photosynthesis modeling in [ 71 ] and calcium and magnesium deficiencies in [ 73 ]. As shown in [ 74 ], the cadmium concentration of lettuce leaves was estimated using a different DL model called DBN that was optimized using particle swarm optimization.…”

Section: Deep Learning In Ceamentioning

confidence: 99%

Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects

Ojo

Zahid

2022

Sensors

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Controlled environment agriculture (CEA) is an unconventional production system that is resource efficient, uses less space, and produces higher yields. Deep learning (DL) has recently been introduced in CEA for different applications including crop monitoring, detecting biotic and abiotic stresses, irrigation, microclimate prediction, energy efficient controls, and crop growth prediction. However, no review study assess DL’s state of the art to solve diverse problems in CEA. To fill this gap, we systematically reviewed DL methods applied to CEA. The review framework was established by following a series of inclusion and exclusion criteria. After extensive screening, we reviewed a total of 72 studies to extract the useful information. The key contributions of this article are the following: an overview of DL applications in different CEA facilities, including greenhouse, plant factory, and vertical farm, is presented. We found that majority of the studies are focused on DL applications in greenhouses (82%), with the primary application as yield estimation (31%) and growth monitoring (21%). We also analyzed commonly used DL models, evaluation parameters, and optimizers in CEA production. From the analysis, we found that convolutional neural network (CNN) is the most widely used DL model (79%), Adaptive Moment Estimation (Adam) is the widely used optimizer (53%), and accuracy is the widely used evaluation parameter (21%). Interestingly, all studies focused on DL for the microclimate of CEA used RMSE as a model evaluation parameter. In the end, we also discussed the current challenges and future research directions in this domain.

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Using Transfer Learning for Nutrient Deficiency Prediction and Classification in Tomato Plant

Cited by 9 publications

References 13 publications

Analysing the Performance and Interpretability of CNN-Based Architectures for Plant Nutrient Deficiency Identification

Analysing the Performance and Interpretability of CNN-Based Architectures for Plant Nutrient Deficiency Identification

Exploiting Pre-Trained Convolutional Neural Networks for the Detection of Nutrient Deficiencies in Hydroponic Basil

Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects

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